analytical techniques for market research

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A Beginner’s Guide to Market Research Analysis

• Bhumika Dutta
• Sep 07, 2021

Introduction

With the massive amount of data that is being produced every day, it is very essential to analyze the data and include intensive research before extracting any relevant information from them. 

If someone is planning to start a business, they need to do proper market research to understand their consumer requirements, before diving deep into the industry market. 

This article is a beginner’s guide to market research analysis where we cover the following topics:

What is Market Research?

Types of Market Research

Market Research analysis techniques: Statistical analysis and data analysis

Market Research analysis tools

Let us learn more about market research.

What is Market Research ?

Market Research is a process of data analysis that allows the evaluation of data regarding any new product and its viability in the market through direct customer research. This approach enables organizations or enterprises to identify their target market, gather and document feedback given by the potential customers, and make educated decisions.

Market Research can be performed by conducting surveys, interacting with samples (a group of people), and other similar methods by the company itself or by any outsourcing agency specializing in the feat. It is done to understand patterns and how the targeted audience will react to the product or service offered by the company. 

The main objectives of market research, according to Question pro , are:

Market Research helps in the administrative development of any company through several ways like planning and organization. To satisfy all the needs of any market, human and material resource control is also required, which is done by market research.

The social aspects and requirements of the customer are also taken care of by Market research. It informs the business owners about the targeted customers and the specifications that will reach out to a wider variety of customers.

Every new and old company wants to reach out to a stage of economic stability as it determines the degree of success or failure of any business to some extent. 

Market Research is very important for any business as it provides important customer insights that can be used to improve the quality of the product or service or change the marketing strategy according to newly devised marketing plans, ultimately making the business more customer-centric. 

Businesses can anticipate their production and sales by knowing their consumers' demands. The market research also aids in choosing the best inventory goods to keep on hand. Thus, with market research and well-planned strategies, the company can stay ahead of its customers.

There are different types of Market research that are performed while marketing analysis. It depends upon the organization on what works best for them, but here are a few market research types and their methods that are used to draw meaningful conclusions:

Primary Market Research:

Primary market research is an amalgamation of both qualitative and quantitative research, where the consumers of any business are contacted directly, either by the company itself or through any other third party. The data collected can either be statistical data (quantitative) or generic information (qualitative). 

There are generally two types of information collected during such analysis: exploratory and specific. 

Exploratory research is a type of open-ended research in which an issue is investigated by asking open-ended questions in a thorough interview format, generally with a small group of people known as a sample. 

Specific research, on the other hand, is more focused and utilized to tackle problems found by exploratory research.

The methods used in qualitative market research are:

Focus Groups:

It is a commonly used research method in which a small group of people responds to online surveys. This method does not require any personal interaction with the consumers and is used to collect important information.

Personal interview:

Personal interviews consist of unstructured, open-ended questions asked by the researcher to the respondents personally, in the form of an interview. This approach is largely reliant on the interviewer's competence and expertise in asking questions that elicit replies.

Ethnographic research:

This sort of detailed research is undertaken in the respondents' native environment. This approach necessitates the interviewer adapting to the respondents' native surroundings, which might be a metropolis or a distant village.

(Suggested reading: Top 10 B2C Marketing strategies )

Secondary Market Research:

Secondary research makes use of material that has been organized by an outside source such as government agencies, media, chambers of business, and so on. This knowledge is widely disseminated through newspapers, journals, books, corporate websites, free government and non-government organizations, and so on.

These are the methods of secondary market research:

Public Sources:  Information can be collected from public sources like libraries or pre-recorded documents.

Commercial Sources:  Commercial sources like newspapers, magazines, journals, media, etc. are great sources of information.

Educational institutions:  Most colleges and educational institutions are a valuable source of knowledge since they conduct more research than any other corporate sector.

Techniques of Market Research Analysis:

In this article, we are going to discuss the two types of analysis techniques that are used for Market research analysis: Statistical Analysis techniques and Data Analysis techniques .

Statistical analysis:

Statistics is a very important subfield of mathematics that gives numerical values in any analysis. The following statistical analysis techniques are used for market research analysis:

Regression Analysis:

Regression analysis is a statistical technique that is used to find the relationship between two or more variables. There are both dependent and independent variables in this analysis, and any change in the dependent variable depends on the independent variables. 

There are two types of regression techniques: Linear regression and Multiple regression . Regression analysis results in a ‘regression curve’ and analysts can study the degree of the curve to find relevant information.

Analysis of Variance Test:

It is also known as the ANOVA test and is used along with regression analysis to understand the effect of the independent variables over dependent variables. It may compare many groups at the same time to check whether there is a link between them.

Conjoint Analysis:

Conjoint analysis is a technique where market researchers encourage individuals to make trade-offs when making decisions, much like they do in real life, and then analyse the outcomes to determine the most popular option. They conduct surveys and use conjoint analysis softwares to process the survey data to figure out the importance of every option that drove the customers.

The T-Test:

The T-test helps the researchers to compare two datasets on whether they have different mean values and determines if the difference between them has some meaning to it or are purely coincidental. 

(Related blog: T-test vs Z-test )

Crosstab Analysis:

Crosstab analysis is a form of quantitative analysis that carries market research in order to analyse different and mutually exclusive data. 

Data Analysis:

The following are the data analysis techniques for market research analysis:

Descriptive data analysis:

Market researchers analyse historical data sets using descriptive data analysis techniques, basically arranging raw information into groupings so that any current patterns may be easily recognised. 

Some of the descriptive data analysis techniques are simple arithmetic tabulations like number of sales, website visits, etc. and data aggregation and mining systems as well.

(Must read: Multivariate data analysis )

Diagnostic data analysis:

Diagnostic analytics use increasingly complex algorithms to uncover the causes of results. Researchers may frequently identify correlation and/or causal connections by comparing and contrasting data sets. 

Diagnostic data analysis approaches include conjoint and regression analyses , as well as probability measurements.

Predictive data analysis:

Businesses employ predictive data analysis techniques that rely on descriptive and diagnostic numbers to foresee probable outcomes. Market researchers use sophisticated statistical models based on current data to assist them forecast the likelihood of certain future occurrences occurring. 

Analysts must thus be trained in sophisticated machine learning programming and understand how to apply algorithms to existing data sets in ways that allow for the depiction of relationships between numerous variables. 

Regression models, such as linear regression and discrete choice analysis, are two of the most common methods through which academics may assist businesses in optimising their future business operations.

Prescriptive data analysis:

Prescriptive data analysis techniques are thought to be the most sophisticated of all data analysis approaches. Companies utilise them to assist them decide the measures they need to take to prevent future problems or capitalise on future possibilities. 

Prescriptive data analysis techniques, similar to predictive analytics in that they seek to forecast the likelihood of certain events occurring, take the insight a step further, attempting to predict possible outcomes based on specific courses of action, as well as possible actions to take based on one or more targeted outcomes.

Tools of Market Research Analysis:

There are certain tools that are required for market research analysis.We have listed out a few of them:

Google Keywords tool: It shows the behavior of customers when online searching for similar products or services.

Questback: Questback is a premium service that connects a company with its target market. It may conduct extensive research on your behalf and offer useful input in a timely and effective manner.

Klout, Kred and Peerindex: To search for key influencers in a targeted market, tools like klout, kred and peerindex can be used.

KeySurvey: KeySurvey allows the creation of online questionnaires for surveys. It however does not source participants.

Google Analytics: Google analytics is used to provide feedback on customer behavior on the website. It can show which goods are popular but few people buy (or vice versa), and it can show which social media platforms the consumers are using, among other things.

FreeLunch: FreeLunch delivers global data across a variety of demographics and sectors, but it is particularly useful for firms targeting the United States, giving anything from thorough demographic breakdowns to insights into larger spending trends.

Market research is very essential for any starting or growing company. It allows the owners to have a clear vision of their market position and the activities they can perform to make the business better. 

In this article we have learned in detail about what market research is and the types of market research. We also discuss statistical analysis in market research and data analysis. We list out a few popular market research analytics tools as well.

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Brandwatch Consumer Research

Formerly the Falcon suite

Formerly Paladin

Published October 17 th 2023

10 Essential Methods for Effective Consumer and Market Research

When it comes to understanding the world around you, market research is an essential step.

We live in a world that’s overflowing with information. Sifting through all the noise to extract the most relevant insights on a certain market or audience can be tough.

That’s where market research comes in – it’s a way for brands and researchers to collect information from target markets and audiences.

Once reliant on traditional methods like focus groups or surveys, market research is now at a crossroads. Newer tools for extracting insights, like social listening tools, have joined the array of market research techniques available.

Here, we break down what market research is and the different methods you can choose from to make the most of it.

What is market research, and why is it critical for you as a marketer?

Market research involves collecting and analyzing data about a specific industry, market, or audience to inform strategic decision-making. It offers marketers valuable insights into the industry, market trends, consumer preferences, competition, and opportunities, enabling businesses to refine their strategies effectively.

By conducting market research, organizations can identify unmet needs, assess product demands, enhance value propositions, and create marketing campaigns that resonate with their target audience. 

This practice serves as a compass, guiding businesses in making data-driven decisions for successful product launches, improved customer relationships, and a stronger positioning in the business landscape. 

For marketers and insights professionals, market research is an indispensable tool. It helps them make smarter decisions and achieve growth and success in the market.

These 10 market research methods form the backbone of effective market research strategies. 

Continue reading or jump directly to each method by tapping the link below.

• Focus groups
• Consumer research with social media listening
• Experiments and field trials
• Observation
• Competitive analysis
• Public domain data
• Buy research
• Analyze sales data

Use of primary vs secondary market research

Market research can be split into two distinct sections: primary and secondary. These are the two main types of market research.

They can also be known as field and desk, respectively (although this terminology feels out of date, as plenty of primary research can be carried out from your desk).

Primary (field) research

Primary market research is research you carry out yourself. Examples of primary market research methods include running your own focus groups or conducting surveys. These are some of the key methods of consumer research. The ‘field’ part refers to going out into the field to get data.

Secondary (desk) research

Secondary market research is research carried out by other people that you want to use. Examples of secondary market research methods include studies carried out by researchers or financial data released by companies.

10 effective methods to do market research

The methods in this list cover both areas. Which ones you want to use will depend on your goals. Have a browse through and see what fits.

1. Focus groups

It’s a simple concept but one that can be hard to put into practice.

You bring together a group of individuals into a room, record their discussions, and ask them questions about various topics you are researching. For some, it’ll be new product ideas. For others, it might be views on a political candidate.

From these discussions, the organizer will try to pull out some insights or use them to judge the wider society’s view on something. The participants will generally be chosen based on certain criteria, such as demographics, interests, or occupations.

A focus group’s strength is in the natural conversation and discussion that can take place between participants (if they’re done right).

Compared to a questionnaire or survey with a rigid set of questions, a focus group can go off on tangents the organizer could not have predicted (and therefore not planned questions for). This can be good in that unexpected topics can arise; or bad if the aims of the research are to answer a very particular set of questions.

The nature of the discussion is important to recognize as a potential factor that skews the resulting data. Focus groups can encourage participants to talk about things they might not have otherwise, and others might impact the group. This can also affect unstructured one-on-one interviews.

In survey research, survey questions are given to respondents (in person, over the phone, by email, or via an online form). Questions can be close-ended or open-ended. As far as close-ended questions go, there are many different types:

• Dichotomous (two choices, such as ‘yes’ or ‘no’)
• Multiple choice
• Rating scale
• Likert scale (common version is five options between ‘strongly agree’ and ‘strongly disagree’)
• Matrix (options presented on a grid)
• Demographic (asking for information such as gender, age, or occupation)

Surveys are massively versatile because of the range of question formats. Knowing how to mix and match them to get what you need takes consideration and thought. Different questions need the right setup.

It’s also about how you ask. Good questions lead to good analysis. Writing clear, concise questions that abstain from vague expressions and don’t lead respondents down a certain path can help your results reflect the true colors of respondents.

There are a ton of different ways to conduct surveys as well, from creating your own from scratch or using tools that do lots of the heavy lifting for you.

3. Consumer research with social media listening

Social media has reached a point where it is seamlessly integrated into our lives. And because it is a digital extension of ourselves, people freely express their opinions, thoughts, and hot takes on social media.

Because people share so much content on social media and the sharing is so instant, social media is a treasure trove for market research. There is plenty of data to monitor , tap into, and dissect.

By using a social listening tool, like Consumer Research , researchers can identify topics of interest and then analyze relevant social posts. For example, they can track brand mentions and what consumers are saying about the products owned by that brand. These are real-world consumer research examples.

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Social media listening democratizes insights, and is especially useful for market research because of the vast amount of unfiltered information available. Because it’s unprompted, you can be fairly sure that what’s shared is an accurate account of what the person really cares about and thinks (as opposed to them being given a subject to dwell on in the presence of a researcher).

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Your complete social listening guide.

Learn how to get started with social listening

4. Interviews

In interviews, the interviewer speaks directly with the respondent. This type of market research method is more personal, allowing for communication and clarification, making it good for open-ended questions. Furthermore, interviews enable the interviewer to go beyond surface-level responses and investigate deeper.

However, the drawback is that interviews can be time-intensive and costly. Those who opt for this method will need to figure out how to allocate their resources effectively. You also need to be careful with leading or poor questions that lead to useless results. Here’s a good introduction to leading questions .

5. Experiments and field trials

Field experiments are conducted in the participants’ environment. They rely on the independent variable and the dependent variable – the researcher controls the independent variable in order to test its impact on the dependent variable. The key here is to establish whether there’s causality.

For example, take Hofling’s experiment that tested obedience, conducted in a hospital setting. The point was to test if nurses followed authority figures (doctors) and if the authority figures’ rules violated standards (The dependent variable being the nurses, the independent variable being a fake doctor calling up and ordering the nurses to administer treatment.)

According to Simply Psychology , there are key strengths and limitations to this method.

The assessment reads:

• Strength: Behavior in a field experiment is more likely to reflect real life because of its natural setting, i.e., higher ecological validity than a lab experiment.
• Strength: There is less likelihood of demand characteristics affecting the results, as participants may not know they are being studied. This occurs when the study is covert.
• Limitation: There is less control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.

There are also massive ethical implications for these kinds of experiments and experiments in general (especially if people are unaware of their involvement). Don’t take this lightly, and be sure to read up on all the guidelines that apply to the region where you’re based.

6. Observation

Observational market research is a qualitative research method where the researcher observes their subjects in a natural or controlled environment. This method is much like being a fly on the wall, but the fly takes notes and analyzes them later. In observational market research, subjects are likely to behave naturally, which reveals their true selves. 

They are not under much pressure. However, if they’re aware of the observation, they can act differently.

This type of research applies well to retail, where the researcher can observe shoppers’ behavior by day of the week, by season, when discounts are offered, and more. However, observational research can be time-consuming, and researchers have no control over the environments they research.

7. Competitive analysis

Competitive analysis is a highly strategic and specific form of market research in which the researchers analyze their company’s competitors. It is critical to see how your brand stacks up to rivals. 

Competitive analysis starts by defining the product, service, brand, and market segment. There are different topics to compare your firm with your competitors. It could be from a marketing perspective: content produced, SEO structure, PR coverage, and social media presence and engagement. It can also be from a product perspective: types of offerings, pricing structure. SWOT analysis is key in assessing strengths, weaknesses, opportunities, and threats.

We’ve written a whole blog post on this tactic, which you can read here .

8. Public domain data

The internet is a wondrous place. Public data exists for those strapped for resources or simply seeking to support their research with more data.  With more and more data produced every year, the question about access and curation becomes increasingly prominent – that’s why researchers and librarians are keen on open data.

Plenty of different types of open data are useful for market research: government databases, polling data, “fact tanks” like Pew Research Center, and more. 

Furthermore, APIs grant developers programmatic access to applications. A lot of this data is free, which is a real bonus.

9. Buy research

Money can’t buy everything, but it can buy research. Subscriptions exist for those who want to buy relevant industry and research reports. Sites like Euromonitor, Statista, Mintel, and BCC Research host a litany of reports for purchase, oftentimes with the option of a single-user license or a subscription.

This can be a massive time saver, and you’ll have a better idea of what you’re getting from the very beginning. You’ll also get all your data in a format that makes sense, saving you effort in cleaning and organizing.

10. Analyze sales data

Sales data is like a puzzle piece that can help reveal the full picture of market research insights. Essentially, it indicates the results. Paired with other market research data, sales data helps researchers better understand actions and consequences. Understanding your customers, their buying habits, and how they change over time is important.

This research will be limited to customers, and it’s important to keep that in mind. Nevertheless, the value of this data should not be underestimated. If you’re not already tracking customer data, there’s no time like the present.

Choosing the right market research method for your strategy

Not all methods will be right for your situation or your business. Once you’ve looked through the list and seen some that take your fancy, spend more time researching each option.You’ll want to consider what you want to achieve, what data you’ll need, the pros and cons of each method, the costs of conducting the research, and the cost of analyzing the results.

Get it right, and it’ll be worth all the effort.

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How To Do Market Research: Definition, Types, Methods

Jan 2, 2024

11 min. read

Market research isn’t just collecting data. It’s a strategic tool that allows businesses to gain a competitive advantage while making the best use of their resources. Research reveals valuable insights into your target audience about their preferences, buying habits, and emerging demands — all of which help you unlock new opportunities to grow your business.

When done correctly, market research can minimize risks and losses, spur growth, and position you as a leader in your industry. 

Let’s explore the basic building blocks of market research and how to collect and use data to move your company forward:

Table of Contents

What Is Market Research?

Why is market research important, market analysis example, 5 types of market research, what are common market research questions, what are the limitations of market research, how to do market research, improving your market research with radarly.

Market Research Definition: The process of gathering, analyzing, and interpreting information about a market or audience.

Market research studies consumer behavior to better understand how they perceive products or services. These insights help businesses identify ways to grow their current offering, create new products or services, and improve brand trust and brand recognition .

You might also hear market research referred to as market analysis or consumer research .

Traditionally, market research has taken the form of focus groups, surveys, interviews, and even competitor analysis . But with modern analytics and research tools, businesses can now capture deeper insights from a wider variety of sources, including social media, online reviews, and customer interactions. These extra layers of intel can help companies gain a more comprehensive understanding of their audience.

With consumer preferences and markets evolving at breakneck speeds, businesses need a way to stay in touch with what people need and want. That’s why the importance of market research cannot be overstated.

Market research offers a proactive way to identify these trends and make adjustments to product development, marketing strategies , and overall operations. This proactive approach can help businesses stay ahead of the curve and remain agile as markets shift.

Market research examples abound — given the number of ways companies can get inside the minds of their customers, simply skimming through your business’s social media comments can be a form of market research.

A restaurant chain might use market research methods to learn more about consumers’ evolving dining habits. These insights might be used to offer new menu items, re-examine their pricing strategies, or even open new locations in different markets, for example.

A consumer electronics company might use market research for similar purposes. For instance, market research may reveal how consumers are using their smart devices so they can develop innovative features.

Market research can be applied to a wide range of use cases, including:

• Testing new product ideas
• Improve existing products
• Entering new markets
• Right-sizing their physical footprints
• Improving brand image and awareness
• Gaining insights into competitors via competitive intelligence

Ultimately, companies can lean on market research techniques to stay ahead of trends and competitors while improving the lives of their customers.

Market research methods take different forms, and you don’t have to limit yourself to just one. Let’s review the most common market research techniques and the insights they deliver.

1. Interviews

3. Focus Groups

4. Observations

5. AI-Driven Market Research

One-on-one interviews are one of the most common market research techniques. Beyond asking direct questions, skilled interviewers can uncover deeper motivations and emotions that drive purchasing decisions. Researchers can elicit more detailed and nuanced responses they might not receive via other methods, such as self-guided surveys.

Interviews also create the opportunity to build rapport with customers and prospects. Establishing a connection with interviewees can encourage them to open up and share their candid thoughts, which can enrich your findings. Researchers also have the opportunity to ask clarifying questions and dig deeper based on individual responses.

Market research surveys provide an easy entry into the consumer psyche. They’re cost-effective to produce and allow researchers to reach lots of people in a short time. They’re also user-friendly for consumers, which allows companies to capture more responses from more people.

Big data and data analytics are making traditional surveys more valuable. Researchers can apply these tools to elicit a deeper understanding from responses and uncover hidden patterns and correlations within survey data that were previously undetectable.

The ways in which surveys are conducted are also changing. With the rise of social media and other online channels, brands and consumers alike have more ways to engage with each other, lending to a continuous approach to market research surveys.

3. Focus groups

Focus groups are “group interviews” designed to gain collective insights. This interactive setting allows participants to express their thoughts and feelings openly, giving researchers richer insights beyond yes-or-no responses.

One of the key benefits of using focus groups is the opportunity for participants to interact with one another. They spark discussions while sharing diverse viewpoints. These sessions can uncover underlying motivations and attitudes that may not be easily expressed through other research methods.

Observing your customers “in the wild” might feel informal, but it can be one of the most revealing market research techniques of all. That’s because you might not always know the right questions to ask. By simply observing, you can surface insights you might not have known to look for otherwise.

This method also delivers raw, authentic, unfiltered data. There’s no room for bias and no potential for participants to accidentally skew the data. Researchers can also pick up on non-verbal cues and gestures that other research methods may fail to capture.

5. AI-driven market research

One of the newer methods of market research is the use of AI-driven market research tools to collect and analyze insights on your behalf. AI customer intelligence tools and consumer insights software like Meltwater Radarly take an always-on approach by going wherever your audience is and continuously predicting behaviors based on current behaviors.

By leveraging advanced algorithms, machine learning, and big data analysis , AI enables companies to uncover deep-seated patterns and correlations within large datasets that would be near impossible for human researchers to identify. This not only leads to more accurate and reliable findings but also allows businesses to make informed decisions with greater confidence.

Tip: Learn how to use Meltwater as a research tool , how Meltwater uses AI , and learn more about consumer insights and about consumer insights in the fashion industry .

No matter the market research methods you use, market research’s effectiveness lies in the questions you ask. These questions should be designed to elicit honest responses that will help you reach your goals.

Examples of common market research questions include:

Demographic market research questions

• What is your age range?
• What is your occupation?
• What is your household income level?
• What is your educational background?
• What is your gender?

Product or service usage market research questions

• How long have you been using [product/service]?
• How frequently do you use [product/service]?
• What do you like most about [product/service]?
• Have you experienced any problems using [product/service]?
• How could we improve [product/service]?
• Why did you choose [product/service] over a competitor’s [product/service]?

Brand perception market research questions

• How familiar are you with our brand?
• What words do you associate with our brand?
• How do you feel about our brand?
• What makes you trust our brand?
• What sets our brand apart from competitors?
• What would make you recommend our brand to others?

Buying behavior market research questions

• What do you look for in a [product/service]?
• What features in a [product/service] are important to you?
• How much time do you need to choose a [product/service]?
• How do you discover new products like [product/service]?
• Do you prefer to purchase [product/service] online or in-store?
• How do you research [product/service] before making a purchase?
• How often do you buy [product/service]?
• How important is pricing when buying [product/service]?
• What would make you switch to another brand of [product/service]?

Customer satisfaction market research questions

• How happy have you been with [product/service]?
• What would make you more satisfied with [product/service]?
• How likely are you to continue using [product/service]?

Bonus Tip: Compiling these questions into a market research template can streamline your efforts.

Market research can offer powerful insights, but it also has some limitations. One key limitation is the potential for bias. Researchers may unconsciously skew results based on their own preconceptions or desires, which can make your findings inaccurate.

• Depending on your market research methods, your findings may be outdated by the time you sit down to analyze and act on them. Some methods struggle to account for rapidly changing consumer preferences and behaviors.
• There’s also the risk of self-reported data (common in online surveys). Consumers might not always accurately convey their true feelings or intentions. They might provide answers they think researchers are looking for or misunderstand the question altogether.
• There’s also the potential to miss emerging or untapped markets . Researchers are digging deeper into what (or who) they already know. This means you might be leaving out a key part of the story without realizing it.

Still, the benefits of market research cannot be understated, especially when you supplement traditional market research methods with modern tools and technology.

Let’s put it all together and explore how to do market research step-by-step to help you leverage all its benefits.

Step 1: Define your objectives

You’ll get more from your market research when you hone in on a specific goal : What do you want to know, and how will this knowledge help your business?

This step will also help you define your target audience. You’ll need to ask the right people the right questions to collect the information you want. Understand the characteristics of the audience and what gives them authority to answer your questions.

Step 2: Select your market research methods

Choose one or more of the market research methods (interviews, surveys, focus groups, observations, and/or AI-driven tools) to fuel your research strategy.

Certain methods might work better than others for specific goals . For example, if you want basic feedback from customers about a product, a simple survey might suffice. If you want to hone in on serious pain points to develop a new product, a focus group or interview might work best.

You can also source secondary research ( complementary research ) via secondary research companies , such as industry reports or analyses from large market research firms. These can help you gather preliminary information and inform your approach.

Step 3: Develop your research tools

Prior to working with participants, you’ll need to craft your survey or interview questions, interview guides, and other tools. These tools will help you capture the right information , weed out non-qualifying participants, and keep your information organized.

You should also have a system for recording responses to ensure data accuracy and privacy. Test your processes before speaking with participants so you can spot and fix inefficiencies or errors.

Step 4: Conduct the market research

With a system in place, you can start looking for candidates to contribute to your market research. This might include distributing surveys to current customers or recruiting participants who fit a specific profile, for example.

Set a time frame for conducting your research. You might collect responses over the course of a few days, weeks, or even months. If you’re using AI tools to gather data, choose a data range for your data to focus on the most relevant information.

Step 5: Analyze and apply your findings

Review your findings while looking for trends and patterns. AI tools can come in handy in this phase by analyzing large amounts of data on your behalf.

Compile your findings into an easy-to-read report and highlight key takeaways and next steps. Reports aren’t useful unless the reader can understand and act on them.

Tip: Learn more about trend forecasting , trend detection , and trendspotting .

Meltwater’s Radarly consumer intelligence suite helps you reap the benefits of market research on an ongoing basis. Using a combination of AI, data science, and market research expertise, Radarly scans multiple global data sources to learn what people are talking about, the actions they’re taking, and how they’re feeling about specific brands.

Our tools are created by market research experts and designed to help researchers uncover what they want to know (and what they don’t know they want to know). Get data-driven insights at scale with information that’s always relevant, always accurate, and always tailored to your organization’s needs.

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The 8 types of market research: definitions, uses and examples.

13 min read What are the different types of market research that can help you stay ahead of the curve with your marketing strategy? Understand how to use each type, and what the advantages and disadvantages are.

Market research (also called marketing research) is the action or activity of gathering information about market needs and preferences. This helps companies understand their target market — how the audience feels and behaves.

There are 8 types of market research, each with their own methods and tools:

• Primary research
• Secondary research
• Qualitative research
• Quantitative research
• Branding research
• Customer research
• Competitor research
• Product research

Let’s start our list by exploring primary and secondary research first.

Free eBook: How to rethink and reinvent market research

1. Primary research

Primary research is research that you collect yourself but going directly to the target market through a range of methods. Because it is data you create, you own the data set.

Two types of results — exploratory information (determines the nature of a problem that hasn’t yet been clearly defined) and conclusive information (carried out to solve a problem that exploratory research identified) — from participants are collected as raw data and then analyzed to gather insights from trends and comparisons.

This method is good for getting the views of a lot of people at one time, especially when time is short, but it comes with its own management issues. The interviewer must prepare a way to gather answers and record these, while engaging in conversation with many people.

Participants may be affected by the group setting, either from acquiescence bias (the desire to say yes to please the interviewer), dominance bias (stronger participants can alter the results from less dominant participants) or researcher bias (where the research leads or impacts the participant responses indirectly).

This provides a structured setting where the interviewer can listen to what’s being said and investigate further into an answer. The interviewer can also pick up on non-verbal cues from body language can help the interview understand where to deep-dive and broaden their understanding.

However, some of the same biases (acquiescence and researcher) still exit in this format. The method is time consuming to do the interviews and collect the data afterwards.

A survey is an excellent method for carrying out primary research as participants do need to be physically present with the interviewer to carry it out. The survey can be completed anywhere there is an internet connection, meaning there is flexibility for the participants to use different devices and for interviewers to contact participants in different geographical time-zones.Preparation is key, however, as the researchers must segment the market and create a list of participants to send the survey to. Hiring a panel or using existing marketing lists can help with this.

2. Secondary research

Secondary research is the use of data that has previously been collected, analysed and published (and therefore you do not own this data). An example of this for market research is:

Most information is freely available, so there are less costs associated with this kind of secondary research over primary research methods.

Secondary research can often be the preparation for primary research activities, providing a knowledge base. The information gathered may not provide the specific information to explain the results, which is where primary market research would be used to enhance understanding.

There is also a logistics planning need for a recording solution that can handle large datasets, since manual management of the volumes of information can be tricky.

Both primary and secondary research have its advantages and disadvantages, as we’ve seen, but they are best used when paired together. Combined, the data can give you the confidence to act knowing that any hypothesis you have is backed up.

Learn more about primary vs secondary research methods

The next market research types can be defined as qualitative and quantitative research types:

3. Qualitative research

Qualitative market research is the collection of primary or secondary data that is non-numerical in nature, and therefore hard to measure.

Researchers collect this market research type because it can add more depth to the data.

This kind of market research is used to summarise and infer, rather than pin-points an exact truth held by a target market. For example, qualitative market research can be done to find out a new target market’s reaction to a new product to translate the reaction into a clear explanation for the company.

4. Quantitative research

Quantitative research is the collection of primary or secondary data that is numerical in nature, and so can be collected more easily.

Researchers collect this market research type because it can provide historical benchmarking, based on facts and figures evidence.

There are a number of ways to collect this data — polls, surveys, desk research, web statistics, financial records — which can be exploratory in nature without a lot of depth at this stage.

Quantitative market research can create the foundation of knowledge needed by researchers to investigate hypotheses further through qualitative market research.

The next four variations of market research are specific to topics areas, that bring about specific information.:

5. Branding research

Branding market research assists a company to create, manage and maintain the company brand. This can relate to the tone, branding, images, values or identity of the company.

Research can be carried out through interviews, focus groups or surveys. For example, brand awareness surveys will ask your participants whether the brand is known to them and whether it is something they would be interested in buying.

Additional areas for brand research is also around brand loyalty, brand perception , brand positioning , brand value and brand identity .

The aim of research will be to understand how to know if:

• Your brand is performing in relation to other competitors
• There are areas to improve your brand activities
• There are positives to showcase to enhance your brand’s image

6. Customer research

Customer market research looks at the key influences on your target customers and how your company can make changes to encourage sales.

The aim of this research is to know your customer inside out, and continuously learn about how they interact with the company. Some themes covered by this include:

• Customer satisfaction – Exploring what keeps customers happy, as higher customer satisfaction is more likely to lead to increased customer retention.
• Customer loyalty – This looks at what experiences have happened to lead to greater customer loyalty across the customer lifecycle.
• Customer segmentation research – Discovering who the customers are, what their behaviour and preferences are and their shared characteristics.

Relevant desk research may look at historical purchase records, customer journey mapping , customer segmentation, demographics and persona templates.

Primary research, such as NPS and customer satisfaction surveys , or customer satisfaction interviews at the end of customer support calls, can also give more details.

7. Competitor research

Competitor market research is about knowing who your competition is and understanding their strengths and weaknesses, in comparison to your organization. It can also be about your competitive offering in the market, or how to approach a new market.

The aim of this research is to find ways to make your organization stand out and future planning through horizon scanning and listening to customer preferences.

For example, for competitive analysis, researchers would create a SWOT for your business and your competitors, to see how your business compares.

Primary research could interview customers about their buying preferences, while secondary sources would look at competitor’s market dominance, sales, structure and so on. With this thorough analysis, you can understand where you can change to be more competitive, and look for ideas that make you stand out.

8. Product research

Product market research is a key way to make sure your products and services are fit for launching in the market, and are performing as well as they can.

The aim of this research is to see how your product is perceived by customers, if they are providing value and working correctly. Ideas can also be formed about upgrades and future product development.

There are a number of avenues within product research:

• Product branding – Does the product brand and design attract customers in the intended way?
• Product feature testing – this can happen at various stages of development with target markets (in early development, between versions, before product launch, etc.) to check if there are positive reaction to new or improved features
• Product design thinking – what solutions would solve your customers’ current or future problems?
• Product marketing – Do the marketing messages help your product’s memorability and saleability, or can they be improved?

Primary research methods have a clear advantage in this kind of market research: Surveys can ask for rankings on the popularity or usefulness of features or conduct conjoint analysis, while in-person observation interviews (where the participant can handle a product) can be particularly useful in seeing what customers do with the product in real time.

How to use market research types in your company

In a good marketing strategy, it’s preferable to have a mixture of data across:

• Qualitative and quantitative research
• Primary and secondary research
• Your specific topic area or area of focus

With these three components, you can make sure your market strategy gives you a complete picture of your market’s operational data and experience data , — what your market does and why .

Economical experience data (O data)

This type of experience data is quantitative in nature (including operations, featuring sales data, finance data and HR data ). As it can be quantified into numerical values, it can be measured over and over, providing datasets.

There is the opportunity to use a data-driven approach to understanding the results and making predictions based on historical trends.

This sort of data can be measured more easily than emotions and feelings. But it can only tell you about past activities and what happened. It can’t tell you what will happen in the future and why things will happen — this is where X data comes in.

Emotional experience data (X data)

This type of experience data seeks to find reasons to explain emotional decisions and how brands ‘sit’ in people’s minds. In this way, this data is qualitative in nature.

Companies that have X data have a ‘mental advantage’ over other companies,  as they are able to understand the perceptions of the customer, their needs and values.

When you have tangible insights on the audience’s needs, you can then take steps to meet those needs and solve problems. This mitigates the risk of an experience gap – which is what your audience expects you deliver versus what you actually deliver.

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Market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, qualitative vs quantitative research 13 min read, qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, request demo.

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Market research examines consumer behavior and trends in the economy to help a business develop and fine-tune its business idea and strategy. It helps a business understand its target market by gathering and analyzing data.

Market research is the process of evaluating the viability of a new service or product through research conducted directly with potential customers. It allows a company to define its target market and get opinions and other feedback from consumers about their interest in a product or service.

Research may be conducted in-house or by a third party that specializes in market research. It can be done through surveys and focus groups, among other ways. Test subjects are usually compensated with product samples or a small stipend for their time.

Key Takeaways

• Companies conduct market research before introducing new products to determine their appeal to potential customers.
• Tools include focus groups, telephone interviews, and questionnaires.
• The results of market research inform the final design of the product and determine how it will be positioned in the marketplace.
• Market research usually combines primary information, gathered directly from consumers, and secondary information, which is data available from external sources.

Market Research

How market research works.

Market research is used to determine the viability of a new product or service. The results may be used to revise the product design and fine-tune the strategy for introducing it to the public. This can include information gathered for the purpose of determining market segmentation . It also informs product differentiation , which is used to tailor advertising.

A business engages in various tasks to complete the market research process. It gathers information based on the market sector being targeted by the product. This information is then analyzed and relevant data points are interpreted to draw conclusions about how the product may be optimally designed and marketed to the market segment for which it is intended.

It is a critical component in the research and development (R&D) phase of a new product or service introduction. Market research can be conducted in many different ways, including surveys, product testing, interviews, and focus groups.

Market research is a critical tool that companies use to understand what consumers want, develop products that those consumers will use, and maintain a competitive advantage over other companies in their industry.

Primary Market Research vs. Secondary Market Research

Market research usually consists of a combination of:

• Primary research, gathered by the company or by an outside company that it hires
• Secondary research, which draws on external sources of data

Primary Market Research

Primary research generally falls into two categories: exploratory and specific research.

• Exploratory research is less structured and functions via open-ended questions. The questions may be posed in a focus group setting, telephone interviews, or questionnaires. It results in questions or issues that the company needs to address about a product that it has under development.
• Specific research delves more deeply into the problems or issues identified in exploratory research.

Secondary Market Research

All market research is informed by the findings of other researchers about the needs and wants of consumers. Today, much of this research can be found online.

Secondary research can include population information from government census data , trade association research reports , polling results, and research from other businesses operating in the same market sector.

History of Market Research

Formal market research began in Germany during the 1920s. In the United States, it soon took off with the advent of the Golden Age of Radio.

Companies that created advertisements for this new entertainment medium began to look at the demographics of the audiences who listened to each of the radio plays, music programs, and comedy skits that were presented.

They had once tried to reach the widest possible audience by placing their messages on billboards or in the most popular magazines. With radio programming, they had the chance to target rural or urban consumers, teenagers or families, and judge the results by the sales numbers that followed.

Types of Market Research

Face-to-face interviews.

From their earliest days, market research companies would interview people on the street about the newspapers and magazines that they read regularly and ask whether they recalled any of the ads or brands that were published in them. Data collected from these interviews were compared to the circulation of the publication to determine the effectiveness of those ads.

Market research and surveys were adapted from these early techniques.

To get a strong understanding of your market, it’s essential to understand demand, market size, economic indicators, location, market saturation, and pricing.

Focus Groups

A focus group is a small number of representative consumers chosen to try a product or watch an advertisement.

Afterward, the group is asked for feedback on their perceptions of the product, the company’s brand, or competing products. The company then takes that information and makes decisions about what to do with the product or service, whether that's releasing it, making changes, or abandoning it altogether.

Phone Research

The man-on-the-street interview technique soon gave way to the telephone interview. A telephone interviewer could collect information in a more efficient and cost-effective fashion.

Telephone research was a preferred tactic of market researchers for many years. It has become much more difficult in recent years as landline phone service dwindles and is replaced by less accessible mobile phones.

Survey Research

As an alternative to focus groups, surveys represent a cost-effective way to determine consumer attitudes without having to interview anyone in person. Consumers are sent surveys in the mail, usually with a coupon or voucher to incentivize participation. These surveys help determine how consumers feel about the product, brand, and price point.

Online Market Research

With people spending more time online, market research activities have shifted online as well. Data collection still uses a survey-style form. But instead of companies actively seeking participants by finding them on the street or cold calling them on the phone, people can choose to sign up, take surveys, and offer opinions when they have time.

This makes the process far less intrusive and less rushed, since people can participate on their own time and of their own volition.

How to Conduct Market Research

The first step to effective market research is to determine the goals of the study. Each study should seek to answer a clear, well-defined problem. For example, a company might seek to identify consumer preferences, brand recognition, or the comparative effectiveness of different types of ad campaigns.

After that, the next step is to determine who will be included in the research. Market research is an expensive process, and a company cannot waste resources collecting unnecessary data. The firm should decide in advance which types of consumers will be included in the research, and how the data will be collected. They should also account for the probability of statistical errors or sampling bias .

The next step is to collect the data and analyze the results. If the two previous steps have been completed accurately, this should be straightforward. The researchers will collect the results of their study, keeping track of the ages, gender, and other relevant data of each respondent. This is then analyzed in a marketing report that explains the results of their research.

The last step is for company executives to use their market research to make business decisions. Depending on the results of their research, they may choose to target a different group of consumers, or they may change their price point or some product features.

The results of these changes may eventually be measured in further market research, and the process will begin all over again.

Benefits of Market Research

Market research is essential for developing brand loyalty and customer satisfaction. Since it is unlikely for a product to appeal equally to every consumer, a strong market research program can help identify the key demographics and market segments that are most likely to use a given product.

Market research is also important for developing a company’s advertising efforts. For example, if a company’s market research determines that its consumers are more likely to use Facebook than X (formerly Twitter), it can then target its advertisements to one platform instead of another. Or, if they determine that their target market is value-sensitive rather than price-sensitive, they can work on improving the product rather than reducing their prices.

Market research only works when subjects are honest and open to participating.

Example of Market Research

Many companies use market research to test new products or get information from consumers about what kinds of products or services they need and don’t currently have.

For example, a company that’s considering starting a business might conduct market research to test the viability of its product or service. If the market research confirms consumer interest, the business can proceed confidently with its business plan . If not, the company can use the results of the market research to make adjustments to the product to bring it in line with customer desires.

What Are the Main Types of Market Research?

The main types of market research are primary research and secondary research. Primary research includes focus groups, polls, and surveys. Secondary research includes academic articles, infographics, and white papers.

Qualitative research gives insights into how customers feel and think. Quantitative research uses data and statistics such as website views, social media engagement, and subscriber numbers.

What Is Online Market Research?

Online market research uses the same strategies and techniques as traditional primary and secondary market research, but it is conducted on the Internet. Potential customers may be asked to participate in a survey or give feedback on a product. The responses may help the researchers create a profile of the likely customer for a new product.

What Are Paid Market Research Surveys?

Paid market research involves rewarding individuals who agree to participate in a study. They may be offered a small payment for their time or a discount coupon in return for filling out a questionnaire or participating in a focus group.

What Is a Market Study?

A market study is an analysis of consumer demand for a product or service. It looks at all of the factors that influence demand for a product or service. These include the product’s price, location, competition, and substitutes as well as general economic factors that could influence the new product’s adoption, for better or worse.

Market research is a key component of a company’s research and development (R&D) stage. It helps companies understand in advance the viability of a new product that they have in development and to see how it might perform in the real world.

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How to do market research in 4 steps: a lean approach to marketing research

From pinpointing your target audience and assessing your competitive advantage, to ongoing product development and customer satisfaction efforts, market research is a practice your business can only benefit from.

Learn how to conduct quick and effective market research using a lean approach in this article full of strategies and practical examples. 

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A comprehensive (and successful) business strategy is not complete without some form of market research—you can’t make informed and profitable business decisions without truly understanding your customer base and the current market trends that drive your business.

In this article, you’ll learn how to conduct quick, effective market research  using an approach called 'lean market research'. It’s easier than you might think, and it can be done at any stage in a product’s lifecycle.

How to conduct lean market research in 4 steps

What is market research, why is market research so valuable, advantages of lean market research, 4 common market research methods, 5 common market research questions, market research faqs.

We’ll jump right into our 4-step approach to lean market research. To show you how it’s done in the real world, each step includes a practical example from Smallpdf , a Swiss company that used lean market research to reduce their tool’s error rate by 75% and boost their Net Promoter Score® (NPS) by 1%.

Research your market the lean way...

From on-page surveys to user interviews, Hotjar has the tools to help you scope out your market and get to know your customers—without breaking the bank.

The following four steps and practical examples will give you a solid market research plan for understanding who your users are and what they want from a company like yours.

1. Create simple user personas

A user persona is a semi-fictional character based on psychographic and demographic data from people who use websites and products similar to your own. Start by defining broad user categories, then elaborate on them later to further segment your customer base and determine your ideal customer profile .

How to get the data: use on-page or emailed surveys and interviews to understand your users and what drives them to your business.

How to do it right: whatever survey or interview questions you ask, they should answer the following questions about the customer:

Who are they?

What is their main goal?

What is their main barrier to achieving this goal?

Pitfalls to avoid:

Don’t ask too many questions! Keep it to five or less, otherwise you’ll inundate them and they’ll stop answering thoughtfully.

Don’t worry too much about typical demographic questions like age or background. Instead, focus on the role these people play (as it relates to your product) and their goals.

How Smallpdf did it: Smallpdf ran an on-page survey for a couple of weeks and received 1,000 replies. They learned that many of their users were administrative assistants, students, and teachers.

Next, they used the survey results to create simple user personas like this one for admins:

Who are they? Administrative Assistants.

What is their main goal? Creating Word documents from a scanned, hard-copy document or a PDF where the source file was lost.

What is their main barrier to achieving it? Converting a scanned PDF doc to a Word file.

💡Pro tip: Smallpdf used Hotjar Surveys to run their user persona survey. Our survey tool helped them avoid the pitfalls of guesswork and find out who their users really are, in their own words. 

You can design a survey and start running it in minutes with our easy-to-use drag and drop builder. Customize your survey to fit your needs, from a sleek one-question pop-up survey to a fully branded questionnaire sent via email. 

We've also created 40+ free survey templates that you can start collecting data with, including a user persona survey like the one Smallpdf used.

2. Conduct observational research

Observational research involves taking notes while watching someone use your product (or a similar product).

Overt vs. covert observation

Overt observation involves asking customers if they’ll let you watch them use your product. This method is often used for user testing and it provides a great opportunity for collecting live product or customer feedback .

Covert observation means studying users ‘in the wild’ without them knowing. This method works well if you sell a type of product that people use regularly, and it offers the purest observational data because people often behave differently when they know they’re being watched. 

Tips to do it right:

Record an entry in your field notes, along with a timestamp, each time an action or event occurs.

Make note of the users' workflow, capturing the ‘what,’ ‘why,’ and ‘for whom’ of each action.

Don’t record identifiable video or audio data without consent. If recording people using your product is helpful for achieving your research goal, make sure all participants are informed and agree to the terms.

Don’t forget to explain why you’d like to observe them (for overt observation). People are more likely to cooperate if you tell them you want to improve the product.

💡Pro tip: while conducting field research out in the wild can wield rewarding results, you can also conduct observational research remotely. Hotjar Recordings is a tool that lets you capture anonymized user sessions of real people interacting with your website. 

Observe how customers navigate your pages and products to gain an inside look into their user behavior . This method is great for conducting exploratory research with the purpose of identifying more specific issues to investigate further, like pain points along the customer journey and opportunities for optimizing conversion .

With Hotjar Recordings you can observe real people using your site without capturing their sensitive information

How Smallpdf did it: here’s how Smallpdf observed two different user personas both covertly and overtly.

Observing students (covert): Kristina Wagner, Principle Product Manager at Smallpdf, went to cafes and libraries at two local universities and waited until she saw students doing PDF-related activities. Then she watched and took notes from a distance. One thing that struck her was the difference between how students self-reported their activities vs. how they behaved (i.e, the self-reporting bias). Students, she found, spent hours talking, listening to music, or simply staring at a blank screen rather than working. When she did find students who were working, she recorded the task they were performing and the software they were using (if she recognized it).

Observing administrative assistants (overt): Kristina sent emails to admins explaining that she’d like to observe them at work, and she asked those who agreed to try to batch their PDF work for her observation day. While watching admins work, she learned that they frequently needed to scan documents into PDF-format and then convert those PDFs into Word docs. By observing the challenges admins faced, Smallpdf knew which products to target for improvement.

“Data is really good for discovery and validation, but there is a bit in the middle where you have to go and find the human.”

3. Conduct individual interviews

Interviews are one-on-one conversations with members of your target market. They allow you to dig deep and explore their concerns, which can lead to all sorts of revelations.

Listen more, talk less. Be curious.

Act like a journalist, not a salesperson. Rather than trying to talk your company up, ask people about their lives, their needs, their frustrations, and how a product like yours could help.

Ask "why?" so you can dig deeper. Get into the specifics and learn about their past behavior.

Record the conversation. Focus on the conversation and avoid relying solely on notes by recording the interview. There are plenty of services that will transcribe recorded conversations for a good price (including Hotjar!).

Avoid asking leading questions , which reveal bias on your part and pushes respondents to answer in a certain direction (e.g. “Have you taken advantage of the amazing new features we just released?).

Don't ask loaded questions , which sneak in an assumption which, if untrue, would make it impossible to answer honestly. For example, we can’t ask you, “What did you find most useful about this article?” without asking whether you found the article useful in the first place.

Be cautious when asking opinions about the future (or predictions of future behavior). Studies suggest that people aren’t very good at predicting their future behavior. This is due to several cognitive biases, from the misguided exceptionalism bias (we’re good at guessing what others will do, but we somehow think we’re different), to the optimism bias (which makes us see things with rose-colored glasses), to the ‘illusion of control’ (which makes us forget the role of randomness in future events).

How Smallpdf did it: Kristina explored her teacher user persona by speaking with university professors at a local graduate school. She learned that the school was mostly paperless and rarely used PDFs, so for the sake of time, she moved on to the admins.

A bit of a letdown? Sure. But this story highlights an important lesson: sometimes you follow a lead and come up short, so you have to make adjustments on the fly. Lean market research is about getting solid, actionable insights quickly so you can tweak things and see what works.

💡Pro tip: to save even more time, conduct remote interviews using an online user research service like Hotjar Engage , which automates the entire interview process, from recruitment and scheduling to hosting and recording.

You can interview your own customers or connect with people from our diverse pool of 200,000+ participants from 130+ countries and 25 industries. And no need to fret about taking meticulous notes—Engage will automatically transcribe the interview for you.

4. Analyze the data (without drowning in it)

The following techniques will help you wrap your head around the market data you collect without losing yourself in it. Remember, the point of lean market research is to find quick, actionable insights.

A flow model is a diagram that tracks the flow of information within a system. By creating a simple visual representation of how users interact with your product and each other, you can better assess their needs.

You’ll notice that admins are at the center of Smallpdf’s flow model, which represents the flow of PDF-related documents throughout a school. This flow model shows the challenges that admins face as they work to satisfy their own internal and external customers.

Affinity diagram

An affinity diagram is a way of sorting large amounts of data into groups to better understand the big picture. For example, if you ask your users about their profession, you’ll notice some general themes start to form, even though the individual responses differ. Depending on your needs, you could group them by profession, or more generally by industry.

We wrote a guide about how to analyze open-ended questions to help you sort through and categorize large volumes of response data. You can also do this by hand by clipping up survey responses or interview notes and grouping them (which is what Kristina does).

“For an interview, you will have somewhere between 30 and 60 notes, and those notes are usually direct phrases. And when you literally cut them up into separate pieces of paper and group them, they should make sense by themselves.”

Pro tip: if you’re conducting an online survey with Hotjar, keep your team in the loop by sharing survey responses automatically via our Slack and Microsoft Team integrations. Reading answers as they come in lets you digest the data in pieces and can help prepare you for identifying common themes when it comes time for analysis.

Hotjar lets you easily share survey responses with your team

Customer journey map

A customer journey map is a diagram that shows the way a typical prospect becomes a paying customer. It outlines their first interaction with your brand and every step in the sales cycle, from awareness to repurchase (and hopefully advocacy).

The above  customer journey map , created by our team at Hotjar, shows many ways a customer might engage with our tool. Your map will be based on your own data and business model.

📚 Read more: if you’re new to customer journey maps, we wrote this step-by-step guide to creating your first customer journey map in 2 and 1/2 days with free templates you can download and start using immediately.

Next steps: from research to results

So, how do you turn market research insights into tangible business results? Let’s look at the actions Smallpdf took after conducting their lean market research: first they implemented changes, then measured the impact.

Implement changes

Based on what Smallpdf learned about the challenges that one key user segment (admins) face when trying to convert PDFs into Word files, they improved their ‘PDF to Word’ conversion tool.

We won’t go into the details here because it involves a lot of technical jargon, but they made the entire process simpler and more straightforward for users. Plus, they made it so that their system recognized when you drop a PDF file into their ‘Word to PDF’ converter instead of the ‘PDF to Word’ converter, so users wouldn’t have to redo the task when they made that mistake. 

In other words: simple market segmentation for admins showed a business need that had to be accounted for, and customers are happier overall after Smallpdf implemented an informed change to their product.

Measure results

According to the Lean UX model, product and UX changes aren’t retained unless they achieve results.

Smallpdf’s changes produced:

A 75% reduction in error rate for the ‘PDF to Word’ converter

A 1% increase in NPS

Greater confidence in the team’s marketing efforts

"With all the changes said and done, we've cut our original error rate in four, which is huge. We increased our NPS by +1%, which isn't huge, but it means that of the users who received a file, they were still slightly happier than before, even if they didn't notice that anything special happened at all.”

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Market research (or marketing research) is any set of techniques used to gather information and better understand a company’s target market. This might include primary research on brand awareness and customer satisfaction or secondary market research on market size and competitive analysis. Businesses use this information to design better products, improve user experience, and craft a marketing strategy that attracts quality leads and improves conversion rates.

David Darmanin, one of Hotjar’s founders, launched two startups before Hotjar took off—but both companies crashed and burned. Each time, he and his team spent months trying to design an amazing new product and user experience, but they failed because they didn’t have a clear understanding of what the market demanded.

With Hotjar, they did things differently . Long story short, they conducted market research in the early stages to figure out what consumers really wanted, and the team made (and continues to make) constant improvements based on market and user research.

Without market research, it’s impossible to understand your users. Sure, you might have a general idea of who they are and what they need, but you have to dig deep if you want to win their loyalty.

Here’s why research matters:

Obsessing over your users is the only way to win. If you don’t care deeply about them, you’ll lose potential customers to someone who does.

Analytics gives you the ‘what’, while research gives you the ‘why’. Big data, user analytics , and dashboards can tell you what people do at scale, but only research can tell you what they’re thinking and why they do what they do. For example, analytics can tell you that customers leave when they reach your pricing page, but only research can explain why.

Research beats assumptions, trends, and so-called best practices. Have you ever watched your colleagues rally behind a terrible decision? Bad ideas are often the result of guesswork, emotional reasoning, death by best practices , and defaulting to the Highest Paid Person’s Opinion (HiPPO). By listening to your users and focusing on their customer experience , you’re less likely to get pulled in the wrong direction.

Research keeps you from planning in a vacuum. Your team might be amazing, but you and your colleagues simply can’t experience your product the way your customers do. Customers might use your product in a way that surprises you, and product features that seem obvious to you might confuse them. Over-planning and refusing to test your assumptions is a waste of time, money, and effort because you’ll likely need to make changes once your untested business plan gets put into practice.

Lean User Experience (UX) design is a model for continuous improvement that relies on quick, efficient research to understand customer needs and test new product features.

Lean market research can help you become more...

Efficient: it gets you closer to your customers, faster.

Cost-effective: no need to hire an expensive marketing firm to get things started.

Competitive: quick, powerful insights can place your products on the cutting edge.

As a small business or sole proprietor, conducting lean market research is an attractive option when investing in a full-blown research project might seem out of scope or budget.

There are lots of different ways you could conduct market research and collect customer data, but you don’t have to limit yourself to just one research method. Four common types of market research techniques include surveys, interviews, focus groups, and customer observation.

Which method you use may vary based on your business type: ecommerce business owners have different goals from SaaS businesses, so it’s typically prudent to mix and match these methods based on your particular goals and what you need to know.

1. Surveys: the most commonly used

Surveys are a form of qualitative research that ask respondents a short series of open- or closed-ended questions, which can be delivered as an on-screen questionnaire or via email. When we asked 2,000 Customer Experience (CX) professionals about their company’s approach to research , surveys proved to be the most commonly used market research technique.

What makes online surveys so popular?  

They’re easy and inexpensive to conduct, and you can do a lot of data collection quickly. Plus, the data is pretty straightforward to analyze, even when you have to analyze open-ended questions whose answers might initially appear difficult to categorize.

We've built a number of survey templates ready and waiting for you. Grab a template and share with your customers in just a few clicks.

💡 Pro tip: you can also get started with Hotjar AI for Surveys to create a survey in mere seconds . Just enter your market research goal and watch as the AI generates a survey and populates it with relevant questions. 

Once you’re ready for data analysis, the AI will prepare an automated research report that succinctly summarizes key findings, quotes, and suggested next steps.

An example research report generated by Hotjar AI for Surveys

2. Interviews: the most insightful

Interviews are one-on-one conversations with members of your target market. Nothing beats a face-to-face interview for diving deep (and reading non-verbal cues), but if an in-person meeting isn’t possible, video conferencing is a solid second choice.

Regardless of how you conduct it, any type of in-depth interview will produce big benefits in understanding your target customers.

What makes interviews so insightful?

By speaking directly with an ideal customer, you’ll gain greater empathy for their experience , and you can follow insightful threads that can produce plenty of 'Aha!' moments.

3. Focus groups: the most unreliable

Focus groups bring together a carefully selected group of people who fit a company’s target market. A trained moderator leads a conversation surrounding the product, user experience, or marketing message to gain deeper insights.

What makes focus groups so unreliable?

If you’re new to market research, we wouldn’t recommend starting with focus groups. Doing it right is expensive , and if you cut corners, your research could fall victim to all kinds of errors. Dominance bias (when a forceful participant influences the group) and moderator style bias (when different moderator personalities bring about different results in the same study) are two of the many ways your focus group data could get skewed.

4. Observation: the most powerful

During a customer observation session, someone from the company takes notes while they watch an ideal user engage with their product (or a similar product from a competitor).

What makes observation so clever and powerful?

‘Fly-on-the-wall’ observation is a great alternative to focus groups. It’s not only less expensive, but you’ll see people interact with your product in a natural setting without influencing each other. The only downside is that you can’t get inside their heads, so observation still isn't a recommended replacement for customer surveys and interviews.

The following questions will help you get to know your users on a deeper level when you interview them. They’re general questions, of course, so don’t be afraid to make them your own.

1. Who are you and what do you do?

How you ask this question, and what you want to know, will vary depending on your business model (e.g. business-to-business marketing is usually more focused on someone’s profession than business-to-consumer marketing).

It’s a great question to start with, and it’ll help you understand what’s relevant about your user demographics (age, race, gender, profession, education, etc.), but it’s not the be-all-end-all of market research. The more specific questions come later.

2. What does your day look like?

This question helps you understand your users’ day-to-day life and the challenges they face. It will help you gain empathy for them, and you may stumble across something relevant to their buying habits.

3. Do you ever purchase [product/service type]?

This is a ‘yes or no’ question. A ‘yes’ will lead you to the next question.

4. What problem were you trying to solve or what goal were you trying to achieve?

This question strikes to the core of what someone’s trying to accomplish and why they might be willing to pay for your solution.

5. Take me back to the day when you first decided you needed to solve this kind of problem or achieve this goal.

This is the golden question, and it comes from Adele Revella, Founder and CEO of Buyer Persona Institute . It helps you get in the heads of your users and figure out what they were thinking the day they decided to spend money to solve a problem.

If you take your time with this question, digging deeper where it makes sense, you should be able to answer all the relevant information you need to understand their perspective.

“The only scripted question I want you to ask them is this one: take me back to the day when you first decided that you needed to solve this kind of problem or achieve this kind of a goal. Not to buy my product, that’s not the day. We want to go back to the day that when you thought it was urgent and compelling to go spend money to solve a particular problem or achieve a goal. Just tell me what happened.”

— Adele Revella , Founder/CEO at Buyer Persona Institute

Bonus question: is there anything else you’d like to tell me?

This question isn’t just a nice way to wrap it up—it might just give participants the opportunity they need to tell you something you really need to know.

That’s why Sarah Doody, author of UX Notebook , adds it to the end of her written surveys.

“I always have a last question, which is just open-ended: “Is there anything else you would like to tell me?” And sometimes, that’s where you get four paragraphs of amazing content that you would never have gotten if it was just a Net Promoter Score [survey] or something like that.”

What is the difference between qualitative and quantitative research?

Qualitative research asks questions that can’t be reduced to a number, such as, “What is your job title?” or “What did you like most about your customer service experience?” 

Quantitative research asks questions that can be answered with a numeric value, such as, “What is your annual salary?” or “How was your customer service experience on a scale of 1-5?”

 → Read more about the differences between qualitative and quantitative user research .

How do I do my own market research?

You can do your own quick and effective market research by 

Surveying your customers

Building user personas

Studying your users through interviews and observation

Wrapping your head around your data with tools like flow models, affinity diagrams, and customer journey maps

What is the difference between market research and user research?

Market research takes a broad look at potential customers—what problems they’re trying to solve, their buying experience, and overall demand. User research, on the other hand, is more narrowly focused on the use (and usability ) of specific products.

What are the main criticisms of market research?

Many marketing professionals are critical of market research because it can be expensive and time-consuming. It’s often easier to convince your CEO or CMO to let you do lean market research rather than something more extensive because you can do it yourself. It also gives you quick answers so you can stay ahead of the competition.

Do I need a market research firm to get reliable data?

Absolutely not! In fact, we recommend that you start small and do it yourself in the beginning. By following a lean market research strategy, you can uncover some solid insights about your clients. Then you can make changes, test them out, and see whether the results are positive. This is an excellent strategy for making quick changes and remaining competitive.

Net Promoter, Net Promoter System, Net Promoter Score, NPS, and the NPS-related emoticons are registered trademarks of Bain & Company, Inc., Fred Reichheld, and Satmetrix Systems, Inc.

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How to Do Market Research: A Definitive Guide

Article Snapshot

Section 1: introduction to market research.

Before we dive into the intricacies of market research, let's first establish a solid understanding of what it entails. Market research is the systematic process of collecting, analyzing, and interpreting data about a target market or industry. It involves gathering information about potential customers, their needs and preferences, as well as assessing the overall market landscape and identifying opportunities for growth.

Market research plays a vital role in shaping business strategies and decision-making processes. It helps businesses identify market trends, evaluate product or service viability, understand customer behavior, and develop effective marketing campaigns. By leveraging market research, companies can minimize risks, optimize resources, and increase their chances of success.

Section 2: Preparing for Market Research

Before embarking on any market research endeavor, it is crucial to establish clear objectives and determine the appropriate research methodology. In this section, we will guide you through the essential steps of preparing for market research.

Defining Research Objectives

The first step in any market research project is to define clear research objectives. These objectives should align with your business goals and provide a framework for your research efforts. Whether you aim to understand customer satisfaction, evaluate market potential for a new product, or analyze competitor strategies, defining specific and measurable objectives is essential to ensure the research is focused and effective.

Choosing the Right Research Methodology

Once you have defined your research objectives, the next step is to select the most appropriate research methodology. There are various methodologies available, each with its strengths and limitations. Qualitative research methods, such as interviews and focus groups, allow for in-depth exploration of customer opinions and perceptions. On the other hand, quantitative research methods, like surveys and data analysis, provide statistical insights and numerical data.

Creating a Research Plan

To ensure the success of your market research endeavor, it is essential to develop a comprehensive research plan. A research plan outlines the steps, timeline, budget, and resources required for your market research project. By creating a well-structured plan, you can effectively manage your research activities, allocate resources efficiently, and stay on track to achieve your research objectives.

Section 3: Conducting Primary Market Research

Primary market research involves collecting firsthand data directly from your target audience. This section will explore various primary research methods and provide insights into how to conduct effective primary market research.

Survey Research

Surveys are a popular and effective method for gathering primary research data. They allow businesses to collect a large volume of data from a diverse audience. Designing effective survey questions, selecting appropriate survey administration methods, and maximizing response rates are crucial elements to consider when conducting survey research.

Interviews and Focus Groups

Interviews and focus groups offer a more in-depth understanding of customer opinions and behaviors. By engaging directly with participants, businesses can explore complex topics and gain valuable insights. This section will cover techniques for conducting successful interviews and focus groups, as well as analyzing and interpreting the qualitative data obtained.

Observational Research

Observational research involves observing and analyzing consumer behavior in real-life situations. This method provides rich insights into consumer interactions, preferences, and decision-making processes. We will discuss different types of observational research and address ethical considerations associated with this methodology.

Section 4: Gathering and Analyzing Secondary Market Research

Secondary market research involves gathering existing data and information from various sources. This section will explore reliable sources for secondary research data, data collection methods, and techniques for analyzing and interpreting secondary research findings.

Sources of Secondary Research Data

Identifying reputable sources for secondary market research data is crucial for obtaining accurate and reliable information. We will explore a wide range of sources, including market research firms, industry reports, government publications, and online databases.

Data Collection and Analysis

Once you have gathered the secondary research data, the next step is to organize and analyze it effectively. This section will provide insights into various data collection methods and techniques for analyzing and interpreting secondary research findings. We will also discuss the utilization of data visualization tools to present data in a visually appealing and informative manner.

Section 5: Utilizing Market Research Findings

Market research findings hold immense value only when they are effectively utilized to drive business growth. In this section, we will explore how to interpret and apply research findings, communicate results, and continually monitor and evaluate market research efforts.

Interpreting and Applying Research Findings

Interpreting research findings accurately is vital to extract actionable insights. We will discuss techniques and strategies for interpreting research findings and applying them to make informed business decisions. Real-world case studies will be presented to illustrate the practical application of market research findings.

Communicating Research Results

Effectively communicating research results is essential for ensuring that the insights gained are understood and utilized by key stakeholders. This section will provide tips for creating visually appealing and informative research reports and delivering impactful presentations to stakeholders and decision-makers.

Monitoring and Evaluating Market Research

Market research is an ongoing process, and continuous monitoring and evaluation are crucial to stay abreast of market trends and changes. We will explore strategies for tracking market dynamics, monitoring the effectiveness of research efforts, and adjusting research strategies based on feedback and evolving market conditions.

Understanding the Importance of Market Research

Market research is an indispensable component of any successful business strategy. It provides crucial insights into customer behavior, market trends, and competitor analysis, enabling businesses to make informed decisions and gain a competitive edge. In this section, we will explore the significance of market research and its role in driving business success.

The Value of Market Research

Market research serves as a guiding light for businesses, helping them navigate the complex landscape of consumer demands and market dynamics. By conducting thorough research, businesses can gain a deep understanding of their target audience, identify unmet needs, and develop products or services that truly resonate with their customers.

One of the primary benefits of market research is its ability to minimize risk. By gathering data and insights before launching a new product or expanding into a new market, businesses can assess market potential, evaluate customer preferences, and anticipate potential challenges. This proactive approach reduces the likelihood of costly mistakes and increases the chances of success.

Moreover, market research plays a vital role in identifying and capitalizing on market opportunities. By staying attuned to market trends, businesses can spot emerging consumer needs, industry shifts, and technological advancements. Armed with this knowledge, they can adapt their strategies, develop innovative solutions, and stay ahead of the competition.

Market research also provides a solid foundation for effective marketing campaigns. By understanding the target audience's preferences, motivations, and pain points, businesses can tailor their messaging, positioning, and communication channels to effectively reach and engage their customers. This targeted approach not only increases customer acquisition but also enhances customer loyalty and brand advocacy.

The Risks of Neglecting Market Research

Failing to conduct market research can have dire consequences for businesses. Without a deep understanding of their target audience, businesses risk developing products or services that do not meet customer needs or preferences. This can lead to low customer satisfaction, decreased sales, and ultimately, business failure.

Additionally, neglecting market research can result in missed opportunities. In a rapidly evolving marketplace, failing to track consumer trends, competitor strategies, and industry shifts can leave businesses lagging behind. By the time they realize the need for change, it may be too late to catch up, leading to lost market share and diminished competitiveness.

Furthermore, without market research, businesses may struggle to effectively allocate their resources. They may invest in marketing campaigns that do not resonate with their target audience or allocate resources to markets with limited potential. This misalignment of resources can drain finances and hinder overall business growth.

The Role of Market Research in Decision-Making

Market research serves as a compass for decision-making, guiding businesses in making strategic choices based on data-driven insights. Whether it is launching a new product, entering a new market, or adjusting pricing strategies, market research provides the necessary information to make informed decisions.

By conducting market research, businesses can gain a comprehensive understanding of their target audience's preferences, needs, and behaviors. This knowledge allows them to develop products or services that align with customer expectations, resulting in higher customer satisfaction and increased sales.

Market research also empowers businesses to assess the competitive landscape. By studying competitors' strengths, weaknesses, and market positioning, businesses can identify gaps and opportunities for differentiation. This knowledge enables them to develop unique value propositions and competitive strategies that set them apart from their rivals.

Additionally, market research helps businesses evaluate the effectiveness of their marketing efforts. By measuring key performance indicators (KPIs) and analyzing consumer responses, businesses can identify areas for improvement and refine their marketing strategies. This iterative approach ensures that marketing budgets are optimized and yields the highest return on investment (ROI).

In conclusion, market research is an invaluable tool for businesses aiming to thrive in a competitive marketplace. By understanding the importance of market research and leveraging its insights, businesses can make informed decisions, minimize risks, seize opportunities, and ultimately drive sustainable growth. Now that we have established the significance of market research, let's delve into the practical steps of preparing for and conducting market research.

Preparing for Market Research

Before diving into market research, it is crucial to lay a solid foundation by preparing for the research process. This section will explore the essential steps involved in preparing for market research, including defining research objectives, selecting the appropriate research methodology, and creating a comprehensive research plan.

Clearly defining research objectives is the cornerstone of any successful market research project. Research objectives serve as guiding principles that outline the specific goals and outcomes you hope to achieve through your research efforts. These objectives should be specific, measurable, achievable, relevant, and time-bound (SMART).

When defining your research objectives, consider what you aim to accomplish. Are you seeking to understand customer preferences for a new product? Do you want to assess market potential for a specific geographic region? Defining clear and focused research objectives will help you stay on track and ensure that your research efforts yield actionable insights.

Once you have defined your research objectives, the next step is to select the most appropriate research methodology. Different research methodologies offer unique advantages and are suited for different research objectives.

Qualitative research methods, such as interviews and focus groups, provide in-depth insights into customer opinions, attitudes, and perceptions. These methods allow for rich, nuanced data collection and are particularly useful for exploring complex topics or understanding the underlying motivations and emotions driving consumer behavior.

Quantitative research methods, on the other hand, involve the collection and analysis of numerical data. Surveys and questionnaires are common quantitative research tools that allow for large-scale data collection. These methods are useful for measuring customer satisfaction, analyzing customer preferences, and identifying statistical relationships between variables.

It's important to choose a research methodology that aligns with your research objectives, budget, and time constraints. Consider the advantages and limitations of each methodology and select the one that will provide the most relevant and accurate data for your specific research needs.

A well-structured research plan is essential for conducting market research efficiently and effectively. A research plan serves as a roadmap that outlines the steps, timeline, budget, and resources required for your research project.

By creating a comprehensive research plan, you can ensure that your market research efforts are well-organized, efficient, and yield valuable insights. The plan will also serve as a reference point to track progress and make adjustments as needed throughout the research process.

Now that you understand the importance of preparing for market research, we will delve into the practicalities of conducting primary market research in the next section.

Conducting Primary Market Research

Survey research is one of the most commonly used methods for collecting primary research data. Surveys allow businesses to gather a large volume of data from a diverse audience efficiently. They can be conducted through various channels, including online surveys, phone interviews, or in-person questionnaires.

When designing a survey, it is important to carefully craft the survey questions to ensure they are clear, unbiased, and relevant to the research objectives. Use a combination of open-ended and closed-ended questions to gather both qualitative and quantitative data. Open-ended questions provide respondents with the opportunity to express their opinions and provide detailed feedback, while closed-ended questions offer predefined response choices that can be easily analyzed.

To maximize response rates, it is essential to carefully consider the survey administration method. Online surveys are cost-effective and convenient, allowing respondents to complete the survey at their convenience. Phone interviews provide a personal touch and allow for follow-up questions, while in-person questionnaires enable businesses to interact directly with respondents. Choosing the appropriate survey administration method depends on factors such as target audience demographics, research objectives, and available resources.

Additionally, it is crucial to consider respondent fatigue and survey length. Long and tedious surveys can lead to decreased response rates and inaccuracies in responses. Keep the survey concise, focused, and engaging to ensure higher participation and reliable data.

Interviews and focus groups provide valuable qualitative insights into consumer opinions, preferences, and behaviors. These methods allow businesses to engage directly with participants and gain a deeper understanding of their thoughts and motivations.

Interviews can be conducted in-person, over the phone, or through video calls. They provide an opportunity to ask probing questions, delve into specific topics, and explore in-depth responses. The interviewer can adapt the questioning based on the participant's responses, allowing for a dynamic and personalized conversation.

Focus groups involve bringing together a small group of individuals to discuss a specific topic or product. This method allows participants to interact with one another, share their opinions, and generate insights through group discussions. Focus groups provide a unique perspective by capturing the collective thoughts and experiences of the participants.

To conduct successful interviews and focus groups, it is essential to carefully plan the session. Develop a discussion guide or interview script that includes a set of key questions or topics to cover. This will ensure consistency and enable comparability across interviews or focus groups. Actively listen to participants, encourage open and honest responses, and create a comfortable environment for sharing opinions.

Qualitative data obtained from interviews and focus groups require careful analysis. Use techniques such as thematic analysis or coding to identify recurring themes, patterns, and insights. These qualitative insights can provide valuable context and depth to complement quantitative data collected through surveys or other methods.

Observational research involves observing and analyzing consumer behavior in real-life settings. This method allows businesses to gain insights into consumer interactions, preferences, and decision-making processes. It can be particularly useful in retail environments, public spaces, or during product usage.

Participant observation involves immersing oneself in the context being studied and actively participating in the observed activities. This method allows researchers to gain firsthand experience and capture the nuances of behavior and interactions. Non-participant observation, on the other hand, involves observing from a distance without directly engaging with the participants. This method allows for more objective observations and avoids potential biases that may arise from researcher-participant interaction.

When conducting observational research, it is essential to consider ethical considerations and obtain necessary permissions, especially in public spaces or when observing sensitive behavior. Maintain confidentiality and anonymity of participants and ensure that the research does not infringe upon their privacy.

Observational research often involves recording observations through notes, photographs, or video recordings. These records serve as valuable data for analysis and interpretation. Analyze the collected data by identifying patterns, behaviors, and trends. Observational research findings can be used to supplement and validate other primary research methods, providing a comprehensive understanding of consumer behavior.

As we have explored the various primary research methods, it is important to note that choosing the appropriate method depends on the research objectives, target audience, available resources, and the depth of insights required. By carefully selecting and conducting primary market research methods, businesses can uncover valuable insights about their target audience, preferences, and behaviors.

Gathering and Analyzing Secondary Market Research

While primary market research provides valuable firsthand data, secondary market research involves gathering existing data and information from various sources. This section will explore the sources of secondary research data and provide insights into data collection methods and techniques for analyzing and interpreting secondary research findings.

Secondary market research relies on existing data and information that has been collected by others. There are various sources from which businesses can gather secondary research data, including:

When gathering secondary research data, it is crucial to consider the reliability and credibility of the sources. Ensure that the data comes from reputable sources and is up-to-date. Cross-referencing information from multiple sources can help validate the accuracy and consistency of the data.

Once you have gathered the relevant secondary research data, the next step is to organize and analyze it effectively. The process of data collection and analysis involves several key steps:

Secondary research findings should be interpreted and used in conjunction with primary research data to gain a comprehensive understanding of the market landscape. Combining primary and secondary research data allows for triangulation, validation, and a more holistic analysis of the research objectives.

By effectively gathering and analyzing secondary research data, businesses can gain valuable insights into market trends, consumer behavior, and industry dynamics. These insights serve as a foundation for informed decision-making, strategy formulation, and staying ahead of the competition.

Utilizing Market Research Findings

Interpreting and analyzing research findings is a critical step in extracting actionable insights that can drive business decisions. Here are some key considerations when interpreting and applying research findings:

Remember that market research is an iterative process, and new insights may emerge as you delve deeper into the data. Continuously revisit and refine your interpretation of the research findings to ensure that you capture the most accurate and valuable insights.

Effectively communicating research results is crucial to ensure that the insights gained are understood and utilized by key stakeholders. Here are some tips for communicating research results:

Market research is an ongoing process that requires continuous monitoring and evaluation. Here are some key aspects to consider when monitoring and evaluating market research efforts:

By monitoring and evaluating market research efforts, you can ensure that the insights gained are effectively utilized and that your research strategies remain aligned with the evolving market landscape.

In conclusion, effectively utilizing market research findings is essential for driving business growth and staying ahead of the competition. By interpreting and applying research findings, communicating results effectively, and continuously monitoring and evaluating research efforts, businesses can make informed decisions, improve customer experiences, and seize market opportunities.

Conclusion: The Power of Market Research

Market research is a powerful tool that empowers businesses to make informed decisions, understand their target audience, and gain a competitive edge. Throughout this comprehensive guide, we have explored the various aspects of market research, from understanding its importance to conducting primary and secondary research, and utilizing research findings effectively. Now, let's recap the key points and emphasize the power of market research in driving business success.

Market research serves as a compass for businesses, guiding them through the complex landscape of consumer demands, market trends, and competitor analysis. By conducting thorough research, businesses can gain valuable insights into their target audience, identify market opportunities, and mitigate risks. Market research enables businesses to make informed decisions, optimize resources, and drive sustainable growth.

One of the primary benefits of market research is its ability to provide a deep understanding of customer preferences and needs. By gaining insights into customer behavior, businesses can develop products and services that truly resonate with their target audience, leading to higher customer satisfaction and loyalty.

Market research also enables businesses to stay ahead of the competition. By monitoring market trends, tracking competitor activities, and assessing industry dynamics, businesses can identify emerging opportunities and adapt their strategies accordingly. This flexibility allows businesses to maintain a competitive edge and seize market opportunities before their competitors.

Furthermore, market research plays a vital role in effective marketing campaigns. By understanding consumer preferences, motivations, and pain points, businesses can tailor their messaging, positioning, and communication channels to reach and engage their target audience more effectively. This targeted approach increases customer acquisition, enhances brand perception, and drives business growth.

However, market research is not a one-time endeavor. It requires continuous monitoring and evaluation to stay attuned to evolving market trends, consumer preferences, and competitive dynamics. By monitoring key metrics, tracking market trends, and gathering ongoing customer feedback, businesses can refine their strategies, identify areas for improvement, and deliver exceptional customer experiences.

In conclusion, market research is an indispensable tool for businesses aiming to thrive in a competitive marketplace. By understanding the importance of market research, preparing thoroughly, conducting primary and secondary research effectively, interpreting and applying research findings, and continuously monitoring and evaluating research efforts, businesses can gain a deeper understanding of their target audience, make informed decisions, and drive business growth. Embrace the power of market research and unlock the endless opportunities it holds for your organization.

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Top Market Analysis Methods and Techniques

One of the most crucial challenges for any type of business is to collect an in-depth understanding of the market. This is where the different types of marketing analysis methods, techniques , and tools come to help.

We already explained what is market analysis  and what are its main characteristics. Here is a list of the key techniques and methods of market data analysis.

Factor Analysis

Factor analysis is a well-known statistical technique used to decline a large number of variables (which strongly correlate with each other) into a common group called a factor. The factor analysis is an exploratory analysis. It groups similar variables into dimensions.

Factor analysis has a very important role in marketing strategies and market research. Marketing factor analysis is changing one marketing variable/factor to see what will be the change to the other variable – the outcome.

For example:

Factor analysis requires when a company changes one marketing point, such as the price of a product, to see the changes of the sales of that product.

A crucial point here is to change only one variable at a time to be able to measure the relationship between the variables and the outcome.

Typically, companies test marketing variables with factor analysis using tools such as focus groups, surveys or other quantitative and qualitative research methods .

Factor analysis in marketing area is one of the key marketing analysis methods because it reflects the perception of the buyer of the product. You are able to find out what is important to your customers of the product.

Cluster Analysis

Cluster analysis is a powerful statistical tool used to classify different types of objects into groups (clusters). Businesses use this type of market analysis method to analyze data that has been categorized on similarities and differences.

As you might guess, cluster analysis is widely used for market segmentation.

It can be given a wide range of market segmentation examples made with the help of cluster analysis.

Used to quantify and characterize customer segments, cluster analysis enables you to target your customers according to their needs, beliefs, geographical location, behavioral and etc.

Why is cluster analysis a key marketing tool?

Because once a company finds out which type of consumer fits into a group, it can create successful marketing strategies related to the needs of its target segments.

This definitely helps you to discover new market opportunities.

Logistic Regression

Logistic Regression is an other statistical classification method widely used in market research. This is one of the popular marketing analysis methods also known as logit regression or logit modeling.

Logistic regression is very similar to the linear regression models , as it is used to get an understanding of the relationship between a dependent variable and one or multiple independent variables.

Good examples of logistic regression application in marketing could be to predict if it has a probability for a consumer to buy a product, given that their age is known.

Marketers use widely logistic analysis to assess the scope of customer acceptance and customers’ purchase intentions of a new product. This way, marketers can predict potential sales of that product.

Discriminant Analysis

Discriminant analysis is often used for creating Perceptual Mapping by marketers.

Other applications of discriminant analysis in marketing area are:

• Distinguish between heavy, medium, and light users
• Defining how market segments differ in media assimilation
• Defining the traits of consumers who will respond to direct marketing campaigns, etc.

Regression Analysis

Regression is a famous prediction technique that quantifying the relationship between dependent variable to one or more independent variables.

In marketing field, the regression analysis is widely used to predict how the relationship between two variables, (for example between advertising and sales), can develop in the time.

The main goal of regression analysis is to predict and control the relationship between at least two variables. This type of market analysis method is used for variations in market sales, share, and brand loyalty.

Business or marketing intelligence specialists can draw the regression line with data extracted from sales in the past periods.

Regression analysis can also be used in customer satisfaction research to answer questions such as: “Which product characteristics contribute most to someone’s overall satisfaction?”

Correlation Analysis

To put it in a simple way, correlation analysis is a technique used to identify how closely related two variables are to each other. It studies the degree of a relationship between two, numerically measured variables.

This type of analysis method is useful when you want to know if there are possible connections between variables.

Correlation analysis is used for answering questions such as: “Do longer blog posts get more shares on Facebook?”

According to B2B International , correlation analysis helps you to “find out what satisfies your customers and employees – and what keeps them loyal.”

What is the difference with regression analysis? Regression analysis emphasis on the prediction while correlation analysis focuses on the strength/the degree of a relationship between two variables.

In addition, they are a great basis for performing and developing business and marketing intelligence and for taking the right business decisions at the right time.

About The Author

Silvia Valcheva

Silvia Valcheva is a digital marketer with over a decade of experience creating content for the tech industry. She has a strong passion for writing about emerging software and technologies such as big data, AI (Artificial Intelligence), IoT (Internet of Things), process automation, etc.

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What are the Market Research Methods?

• August 15, 2023
• Topic: Brand Strategy , Corporate , Corporate Trends , Customer Experience , Market Analysis , Product Lifecycle
• Resource type: Insights Blog

So, you have a question, but you are unsure of how to get your answer. Maybe you are wondering who your target audience is or why you lost out on a deal to your competition. Maybe you are looking to expand into a new market and want to know more about the customers and competitors in the industry. While these examples are similar in the way they help you understand your business better, they all require different market research methodologies to arrive at the answer.  

What are Market Research Methodologies?  

Research methodologies are various ways to perform research to understand your problem. The correct type to employ depends on the answers you are seeking, the information you have, and the information you need to gather. There are many different methods, but most fall into four categories: data analytics, survey, qualitative, and secondary.

In this post, we will provide an overview of the four main research methodologies along with benefits and challenges of each.   

Custom or Syndicated Research  

In addition to the types of methodologies, there are two types of funded research: custom and syndicated.   

Custom research is funded by a single company and is focused on answering the key questions the business seeks to understand. Though more costly, the research design, implementation, and results are unique and targeted toward addressing the funding company’s needs.  

On the other hand, syndicated research is not curated or funded by a specific client; a market research company conducts it to offer data such as industry statistics, current best practices, or recent trends. Though not directly tied to a single company’s situation, businesses often buy syndicated research to gather perspective on their performance and identify areas where custom research can help provide more insight.   

The Four Types Of Market Research   

Data analytics  .

Data analytics research involves collecting and analyzing large sets of data to derive answers, uncover patterns, and predict future outcomes. This method helps you identify and understand things you are aware of but don’t yet understand.

Data can come from a variety of sources including CRM data, historical transactional data, survey data, a third-party publisher, and more to build a holistic map of the situation, identify gaps and discern trends. Data analytics is the most common research method with almost 70% of companies using it in at least one market research project in the past year.  

For example, you might have large sets of historical data and know there is a data-backed answer for how to segment your customers, but you have yet to compile all your information together to identify the answer.  

Benefits and Challenges

Benefits: Analyzing historical data provides a holistic view of a situation by combining different sets of data and modeling potential scenarios and outcomes. You can confirm hypotheses, break biases, and help build cases internally.

Challenges: This method requires a lot of data, and some of that data may be hard to access, hard to generate, or not easily analyzed. This method also requires a lot of time, money, and resources to acquire and parse the correct data.   

Survey research involves gathering opinions, preferences, and experiences by asking a set of questions to a targeted group of people. The focus of survey research is to test theories, assumptions, and hypotheses. The answers are collected from a representative sample of a targeted audience, allowing the researchers to quantify data and generalize the results to the wider population with a reasonable margin of error and strong confidence level.

Survey data can be collected from consumers, other business decision-makers, or your customer lists. Surveys are a very popular market research methodology with over 60% of companies performing at least one survey in the last year.  

For example, you may be wondering how satisfied your customers are, what factors drive satisfaction, and how you compare to key competitors in the market. By surveying your customers and those of key competitors you can understand the drivers of satisfaction and your relative strengths and opportunity areas in the market.  

Benefits and Challenges 

Benefits : Surveys provide an aggregate but statistically significant picture that companies can leverage to make decisions that align with their audiences’ preferences. Surveys also offer the ability to segment answers based on segments of the audience to analyze how different groups respond to the same questions.   

Challenges: Surveys are a fixed set of questions and cannot be adjusted once the survey has been deployed. Responses are limited to the questions posed by the researcher and don’t allow for open-ended qualitative responses. Surveys require many respondents, and depending on the target audience, it can be challenging to find a large enough sample size to provide statistically significant results. Lastly, surveys need to incentivize respondents, which could lead to a high price tag.  

Qualitative Methodologies   

Qualitative research focuses on targeted insights around concepts, opinions, and preferences. Unlike quantitative methods, these market research methodologies leverage a smaller set of data and respondents but allow for more in-depth answers. It also allows for companies to gather follow-up data that delves deeper into the reasoning behind responses  

This method is exploratory in nature to help you formulate hypothesis and establish directional themes or trends. Qualitative research also helps you understand the underlying motivations, attitudes, and perceptions of respondents.  

 The two most common qualitative research methodologies are in-depth interviews and focus groups.  

In-depth interviews  

This market research methodology involves one-on-one conversations between interviewers and those from the target audience. The interview follows a pre-determined set of questions to reveal sentiment, decision-making processes, and unmet needs. With only 40% of companies conducting them, interviews are the least used methodology, likely a result of the challenges mentioned below.  

Benefits : Interviews provide the ability to gather more in-depth answers on customer preferences by allowing researchers to ask follow-up questions to probe deeper and further clarify responses. It also allows respondents to answer in their own words rather than be bound by the available responses offered by a survey. 

Challenges: Interviews are responses from a small group of people and the results cannot be generalized to a wider audience. They are also very challenging to implement. Often, it is a struggle to identify and incentivize enough participants, and the price per respondent can be costly depending on their rarity and level of expertise. It is also critical to enlist an experienced interviewer to ensure that both the initial and follow-up questions are tailored to gather accurate information that fully addresses your target questions.   

  Focus groups   

These facilitator-led group discussions reveal perceptions of or reception to a concept or idea. While the facilitator guides the meeting, the direction of the conversation is determined by the participants creating organic responses that stem from participant perception. Just over half of companies have conducted a focus group in the last year.   

Benefits: F ocus groups allow for exploration of concepts and physical products beyond set responses like those available in through a survey. The social aspect of the focus group can also gather multiple points of view on a topic in one setting. This can add additional insight for both participants in their ongoing feedback and facilitators for their final analysis.

Challenges: Focus groups are kept small to gather meaningful insights from a group of people, something that would be difficult if the group was too large. As such, the sample size is very small, and the responses can‘t be extrapolated to a larger audience.  It is also challenging to find a group of qualified participants that are all available at the same time.

Traditionally, focus groups were conducted in person and there was a higher cost to host the group live. Now depending on the product or concept being reviewed, focus groups can be conducted over video calls, lessening the burden of cost and logistics, however the cost to incentivize members to take part remains. Similar to interviews, you will need to enlist an experienced moderator that can facilitate the conversation and help direct it as needed to ensure the target questions are addressed  

Secondary Research Methodologies     

Secondary research, a lso known as desk research, is leveraging data that already exists to answer your questions. This market research method is helpful for answering questions or deepening your understanding of things you are not directly familiar with but understand. It can be used to understand what others in your market are doing, identify potential markets for growth or expansion, or allow you to compare your organization to others on key performance indicators.  

For example, you might understand that customer preferences have affected your market, but you don’t know the exact changes. However, others have already done related research that can provide context or direct answers to your question. Secondary research is a very popular method with over half ( 55% ) of companies conducting secondary research to get insights they need for their strategies.   

Benefits: Secondary research is one of the quicker methodologies as it leverages existing data. The bulk of the time is spent identifying the problem, accessing existing data, and consolidating it for analysis and insights.   

Challenges: Some of the data you need might require payment, which would increase the cost of the overall project. There is also the risk that a data point needed for your analysis does not exist, requiring you to either speak to an expert or conduct your own research to fill in the gap.  

Picking the Right Research Methodology  

Though there are many options to choose from, the correct market research methodology to implement will be guided by the information you already have and the questions you are trying to answer.

Before you start your research, begin by listing what you know and what you are looking to learn. Some choices are very clear cut. For example, are you looking to learn more about your company’s operations in the hopes of identifying a better strategy? Since you have access to your own data and are looking to learn more, data analytics would be your best path forward.  

Sometimes choosing the right market research methodology might require more thought. For example, are you looking to launch a new product and want to learn more about customer preferences? You could interview customers or launch a focus group, but do you know what questions to ask? And as the sample pool is so small, the results from qualitative methods should not be used to make assumptions about a larger customer base.

The best place to start would be to conduct a survey to the target audience to get a basic understanding of the market and potential customer preferences. If it is a well-known customer base, you may be able to through secondary research by leveraging existing data to analyze the market.  

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Statistical Analysis Methods for Market Research

Statistical analysis will take market research to the next level, in this article…, introduction.

• What is statistical analysis?
• Statistical analysis methods
• Benefits of Using Statistical Analysis

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Primary market research allows organizations to collect information from target markets by employing traditional quantitative and qualitative techniques.

However, acquiring data is not the only factor for conducting effective market research as data is more widely available thanks to the power of technology and the internet and online panels .

Modern advancements make it easier for businesses across all industries to monitor customer and market sentiments while collecting large quantities of data.

The power of data is not inherent as a data set is only as good as the analysis and insights. The right analysis methods can assist your business in extracting key information and trends from a pool of random data points that analysts can use to set short and long-term growth strategies.

What is Statistical Analysis?

Statistical analysis is a quantitative data analysis method that uses numbers to assign a measurability factor that is easy to compare and interpret. Under statistical analysis, the raw data is collected and analyzed to identify any patterns and trends which can be used for informed decision making.

The process of using statistics for market research involves:

• Defining the type of data to be extracted from the target population
• Exploring the relationship of the data with the population set
• Developing a model that summarizes insights and defines any visible links between the data set and the population
• Testing the model to establish the validity of the model
• Incorporating the results into your business strategy by anticipating future trends

Statistical Analysis Methods

There are two main statistical analysis methods commonly used for market research purposes: descriptive and inferential statistics. Both methods have different goals and applications, making them suitable for evaluating different data sets.

Descriptive Statistics

Descriptive statistics provide insight into the data collected, but they do not draw any conclusions about the larger population the data sample is extracted from. This method essentially describes a sample by summarizing and graphing data.

Conducting market research with descriptive statistics can help organizations understand the basic features of any set of quantifiable data by grouping data and identifying any patterns or trends.

This method is relatively simple as it involves basic mathematic calculations and data aggregation to yield important figures to evaluate historical business practices and their effectiveness. Some common descriptive statistical analysis methods include:

This involves mathematical functions, including counting, percentage calculation, and frequency occurrences.

Measures of frequency are used to primarily count the number of times a specific variable, event, or number appears in a data set.

It is used to establish how often a response occurs in the sample.

This describes the central positions of a distribution for a given data set.

It is used to display the average responses by analyzing the frequency of the sample data points and expressing it using the mean, median, and mode.

The central tendency measure identifies the most common trends or shared characteristics in the sample data.

Inferential Statistics

Inferential statistics use insights and measurements derived from the sample set and extrapolates the results to a larger set.

This method is primarily used to draw conclusions from an experiment sample and generalize the points to a relevant population.

An underlying assumption of this method is that the sample size is an accurate representation of the population which requires us to identify the population, include relevant sampling techniques to extract the sample set, and have some built-in safeguards to account for sampling errors.

While this method is more complicated than descriptive statistics, it provides richer numerical data for future business strategies. Some common inference statistical analysis methods include:

This is used to establish the underlying structure of a larger set of correlated variables.

The purpose is to condense information contained in multiple original valuables into a smaller set with composite dimensions while ensuring there is minimum loss of information.

Simply stated we are reducing data by making one variable, which is easy to manage by representing a set of observed variables (typically semantic differential scales ) in terms of common factors which can explain correlation that can be applied to a larger population.

This is used to distinguish how market research respondents make complicated purchasing decisions that include perceiving and evaluating different variables related to a product or service.

Conjoint analysis requires respondents to evaluate the tradeoffs involved with different factors such as prices, branding, etc., and identify their bearing on purchase consideration to evaluate the decision-making criteria for customers.

This technique is used to evaluate patterns, trends, relationships, and probabilities by grouping variables to understand correlations between different variables involved in the sample data.

This method identifies relationships that might not be readily apparent by placing the variables next to each other in a two-dimensional table which provides a unique perspective and outlook beneficial for gauging insights.

The Totally Unduplicated Reach and Frequency Analysis is used to rank and optimize product combinations and while fine-tuning our communication strategies by analyzing the reach of communication sources and frequency.

TURF Analysis allows you to evaluate estimates of media and market potential in devising optimal communication and placement strategies.

It identifies the number of users reached by each communication method and how often they are reached so you can have a stronger grip on market sentiments.

This method provides an in-depth study into the relationship between two or more variables from a data set and their application to the overall population pool.

This can help businesses make predictions about future behavior by establishing a causal or dependency relationship that can be positive or negative.

The intensity is measured by a higher numeric value on a scale ranging from -1 to +1.

This is a commonly used method for predicting the strength of a relationship between two or more variables.

To run a regression analysis, you need to have a dependent variable whose variation is dependent on another variable and independent variables which are controlled by the experimenter, and its variation is not dependent on any other variable.

In the analysis, the impact of independent variables on the dependent variable is evaluated to understand which variables have a greater impact.

Speaking hypothesis testing is another way to derive conclusions about a population by testing representative sample sets against experimenter-defined expectations or hypotheses.

The hypothesis can establish relationships between variables or provide insights about population properties such as mean and variation through T-Test, Chi-Square, and ANOVA tests.

This method makes it easy to draw suitable conclusions when it is impossible to test the entire population.

However, this method requires sophisticated sampling techniques to ensure the sample is representative of the population.

Benefits of Using Statistical Analysis in Market Research

Statistical analysis methods can provide worthwhile benefits to facilitate market research processes by:

• Producing theories backed by numerical evidence . The quantitative nature of statistical analysis provides a solid numeric framework to provide objective support for relationships between variables and hypotheses. These statistics make it easier for organizations to make well thought out decisions to serve customers better and provide relevant products and services to align with long-term goals and a positive impact on productivity.
• Yielding data that is easily calculated and analyzed . The precision offered by numbers and percentages can provide answers that can be analyzed by performing arithmetic functions. They do not need to be coded, unlike qualitative data, to improve understandability. This cuts down on data processing time while producing relevant results.
• Yielding a larger respondent pool . Before implementing any statistical analysis techniques, you need to have a data set. It is important to note that the data set for quantitative research is typically larger than that of qualitative research because it consists of close-ended questions, which are less time consuming, thereby encouraging more people to complete the survey or questionnaire. The larger data set allows you to have a more accurate sample representing the population which provides greater credibility to any results derived from the analysis

Jim Whaley  is a business leader, market research expert, and writer. He posts frequently on  The Standard Ovation  and other industry blogs.

OvationMR is a global provider of first-party data  for those seeking solutions that require information for informed business decisions.

OvationMR is a leader in delivering insights  and reliable results across a variety of industry sectors around the globe consistently for market research professionals and management consultants.

Visit: https://www.ovationmr.com .

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The 7 Most Useful Data Analysis Methods and Techniques

Data analytics is the process of analyzing raw data to draw out meaningful insights. These insights are then used to determine the best course of action.

When is the best time to roll out that marketing campaign? Is the current team structure as effective as it could be? Which customer segments are most likely to purchase your new product?

Ultimately, data analytics is a crucial driver of any successful business strategy. But how do data analysts actually turn raw data into something useful? There are a range of methods and techniques that data analysts use depending on the type of data in question and the kinds of insights they want to uncover.

You can get a hands-on introduction to data analytics in this free short course .

In this post, we’ll explore some of the most useful data analysis techniques. By the end, you’ll have a much clearer idea of how you can transform meaningless data into business intelligence. We’ll cover:

• What is data analysis and why is it important?
• What is the difference between qualitative and quantitative data?
• Regression analysis
• Monte Carlo simulation
• Factor analysis
• Cohort analysis
• Cluster analysis
• Time series analysis
• Sentiment analysis
• The data analysis process
• The best tools for data analysis
•  Key takeaways

The first six methods listed are used for quantitative data , while the last technique applies to qualitative data. We briefly explain the difference between quantitative and qualitative data in section two, but if you want to skip straight to a particular analysis technique, just use the clickable menu.

1. What is data analysis and why is it important?

Data analysis is, put simply, the process of discovering useful information by evaluating data. This is done through a process of inspecting, cleaning, transforming, and modeling data using analytical and statistical tools, which we will explore in detail further along in this article.

Why is data analysis important? Analyzing data effectively helps organizations make business decisions. Nowadays, data is collected by businesses constantly: through surveys, online tracking, online marketing analytics, collected subscription and registration data (think newsletters), social media monitoring, among other methods.

These data will appear as different structures, including—but not limited to—the following:

The concept of big data —data that is so large, fast, or complex, that it is difficult or impossible to process using traditional methods—gained momentum in the early 2000s. Then, Doug Laney, an industry analyst, articulated what is now known as the mainstream definition of big data as the three Vs: volume, velocity, and variety. 

• Volume: As mentioned earlier, organizations are collecting data constantly. In the not-too-distant past it would have been a real issue to store, but nowadays storage is cheap and takes up little space.
• Velocity: Received data needs to be handled in a timely manner. With the growth of the Internet of Things, this can mean these data are coming in constantly, and at an unprecedented speed.
• Variety: The data being collected and stored by organizations comes in many forms, ranging from structured data—that is, more traditional, numerical data—to unstructured data—think emails, videos, audio, and so on. We’ll cover structured and unstructured data a little further on.

This is a form of data that provides information about other data, such as an image. In everyday life you’ll find this by, for example, right-clicking on a file in a folder and selecting “Get Info”, which will show you information such as file size and kind, date of creation, and so on.

Real-time data

This is data that is presented as soon as it is acquired. A good example of this is a stock market ticket, which provides information on the most-active stocks in real time.

Machine data

This is data that is produced wholly by machines, without human instruction. An example of this could be call logs automatically generated by your smartphone.

Quantitative and qualitative data

Quantitative data—otherwise known as structured data— may appear as a “traditional” database—that is, with rows and columns. Qualitative data—otherwise known as unstructured data—are the other types of data that don’t fit into rows and columns, which can include text, images, videos and more. We’ll discuss this further in the next section.

2. What is the difference between quantitative and qualitative data?

How you analyze your data depends on the type of data you’re dealing with— quantitative or qualitative . So what’s the difference?

Quantitative data is anything measurable , comprising specific quantities and numbers. Some examples of quantitative data include sales figures, email click-through rates, number of website visitors, and percentage revenue increase. Quantitative data analysis techniques focus on the statistical, mathematical, or numerical analysis of (usually large) datasets. This includes the manipulation of statistical data using computational techniques and algorithms. Quantitative analysis techniques are often used to explain certain phenomena or to make predictions.

Qualitative data cannot be measured objectively , and is therefore open to more subjective interpretation. Some examples of qualitative data include comments left in response to a survey question, things people have said during interviews, tweets and other social media posts, and the text included in product reviews. With qualitative data analysis, the focus is on making sense of unstructured data (such as written text, or transcripts of spoken conversations). Often, qualitative analysis will organize the data into themes—a process which, fortunately, can be automated.

Data analysts work with both quantitative and qualitative data , so it’s important to be familiar with a variety of analysis methods. Let’s take a look at some of the most useful techniques now.

3. Data analysis techniques

Now we’re familiar with some of the different types of data, let’s focus on the topic at hand: different methods for analyzing data. 

a. Regression analysis

Regression analysis is used to estimate the relationship between a set of variables. When conducting any type of regression analysis , you’re looking to see if there’s a correlation between a dependent variable (that’s the variable or outcome you want to measure or predict) and any number of independent variables (factors which may have an impact on the dependent variable). The aim of regression analysis is to estimate how one or more variables might impact the dependent variable, in order to identify trends and patterns. This is especially useful for making predictions and forecasting future trends.

Let’s imagine you work for an ecommerce company and you want to examine the relationship between: (a) how much money is spent on social media marketing, and (b) sales revenue. In this case, sales revenue is your dependent variable—it’s the factor you’re most interested in predicting and boosting. Social media spend is your independent variable; you want to determine whether or not it has an impact on sales and, ultimately, whether it’s worth increasing, decreasing, or keeping the same. Using regression analysis, you’d be able to see if there’s a relationship between the two variables. A positive correlation would imply that the more you spend on social media marketing, the more sales revenue you make. No correlation at all might suggest that social media marketing has no bearing on your sales. Understanding the relationship between these two variables would help you to make informed decisions about the social media budget going forward. However: It’s important to note that, on their own, regressions can only be used to determine whether or not there is a relationship between a set of variables—they don’t tell you anything about cause and effect. So, while a positive correlation between social media spend and sales revenue may suggest that one impacts the other, it’s impossible to draw definitive conclusions based on this analysis alone.

There are many different types of regression analysis, and the model you use depends on the type of data you have for the dependent variable. For example, your dependent variable might be continuous (i.e. something that can be measured on a continuous scale, such as sales revenue in USD), in which case you’d use a different type of regression analysis than if your dependent variable was categorical in nature (i.e. comprising values that can be categorised into a number of distinct groups based on a certain characteristic, such as customer location by continent). You can learn more about different types of dependent variables and how to choose the right regression analysis in this guide .

Regression analysis in action: Investigating the relationship between clothing brand Benetton’s advertising expenditure and sales

b. Monte Carlo simulation

When making decisions or taking certain actions, there are a range of different possible outcomes. If you take the bus, you might get stuck in traffic. If you walk, you might get caught in the rain or bump into your chatty neighbor, potentially delaying your journey. In everyday life, we tend to briefly weigh up the pros and cons before deciding which action to take; however, when the stakes are high, it’s essential to calculate, as thoroughly and accurately as possible, all the potential risks and rewards.

Monte Carlo simulation, otherwise known as the Monte Carlo method, is a computerized technique used to generate models of possible outcomes and their probability distributions. It essentially considers a range of possible outcomes and then calculates how likely it is that each particular outcome will be realized. The Monte Carlo method is used by data analysts to conduct advanced risk analysis, allowing them to better forecast what might happen in the future and make decisions accordingly.

So how does Monte Carlo simulation work, and what can it tell us? To run a Monte Carlo simulation, you’ll start with a mathematical model of your data—such as a spreadsheet. Within your spreadsheet, you’ll have one or several outputs that you’re interested in; profit, for example, or number of sales. You’ll also have a number of inputs; these are variables that may impact your output variable. If you’re looking at profit, relevant inputs might include the number of sales, total marketing spend, and employee salaries. If you knew the exact, definitive values of all your input variables, you’d quite easily be able to calculate what profit you’d be left with at the end. However, when these values are uncertain, a Monte Carlo simulation enables you to calculate all the possible options and their probabilities. What will your profit be if you make 100,000 sales and hire five new employees on a salary of $50,000 each? What is the likelihood of this outcome? What will your profit be if you only make 12,000 sales and hire five new employees? And so on. It does this by replacing all uncertain values with functions which generate random samples from distributions determined by you, and then running a series of calculations and recalculations to produce models of all the possible outcomes and their probability distributions. The Monte Carlo method is one of the most popular techniques for calculating the effect of unpredictable variables on a specific output variable, making it ideal for risk analysis.

Monte Carlo simulation in action: A case study using Monte Carlo simulation for risk analysis

 c. Factor analysis

Factor analysis is a technique used to reduce a large number of variables to a smaller number of factors. It works on the basis that multiple separate, observable variables correlate with each other because they are all associated with an underlying construct. This is useful not only because it condenses large datasets into smaller, more manageable samples, but also because it helps to uncover hidden patterns. This allows you to explore concepts that cannot be easily measured or observed—such as wealth, happiness, fitness, or, for a more business-relevant example, customer loyalty and satisfaction.

Let’s imagine you want to get to know your customers better, so you send out a rather long survey comprising one hundred questions. Some of the questions relate to how they feel about your company and product; for example, “Would you recommend us to a friend?” and “How would you rate the overall customer experience?” Other questions ask things like “What is your yearly household income?” and “How much are you willing to spend on skincare each month?”

Once your survey has been sent out and completed by lots of customers, you end up with a large dataset that essentially tells you one hundred different things about each customer (assuming each customer gives one hundred responses). Instead of looking at each of these responses (or variables) individually, you can use factor analysis to group them into factors that belong together—in other words, to relate them to a single underlying construct. In this example, factor analysis works by finding survey items that are strongly correlated. This is known as covariance . So, if there’s a strong positive correlation between household income and how much they’re willing to spend on skincare each month (i.e. as one increases, so does the other), these items may be grouped together. Together with other variables (survey responses), you may find that they can be reduced to a single factor such as “consumer purchasing power”. Likewise, if a customer experience rating of 10/10 correlates strongly with “yes” responses regarding how likely they are to recommend your product to a friend, these items may be reduced to a single factor such as “customer satisfaction”.

In the end, you have a smaller number of factors rather than hundreds of individual variables. These factors are then taken forward for further analysis, allowing you to learn more about your customers (or any other area you’re interested in exploring).

Factor analysis in action: Using factor analysis to explore customer behavior patterns in Tehran

d. Cohort analysis

Cohort analysis is a data analytics technique that groups users based on a shared characteristic , such as the date they signed up for a service or the product they purchased. Once users are grouped into cohorts, analysts can track their behavior over time to identify trends and patterns.

So what does this mean and why is it useful? Let’s break down the above definition further. A cohort is a group of people who share a common characteristic (or action) during a given time period. Students who enrolled at university in 2020 may be referred to as the 2020 cohort. Customers who purchased something from your online store via the app in the month of December may also be considered a cohort.

With cohort analysis, you’re dividing your customers or users into groups and looking at how these groups behave over time. So, rather than looking at a single, isolated snapshot of all your customers at a given moment in time (with each customer at a different point in their journey), you’re examining your customers’ behavior in the context of the customer lifecycle. As a result, you can start to identify patterns of behavior at various points in the customer journey—say, from their first ever visit to your website, through to email newsletter sign-up, to their first purchase, and so on. As such, cohort analysis is dynamic, allowing you to uncover valuable insights about the customer lifecycle.

This is useful because it allows companies to tailor their service to specific customer segments (or cohorts). Let’s imagine you run a 50% discount campaign in order to attract potential new customers to your website. Once you’ve attracted a group of new customers (a cohort), you’ll want to track whether they actually buy anything and, if they do, whether or not (and how frequently) they make a repeat purchase. With these insights, you’ll start to gain a much better understanding of when this particular cohort might benefit from another discount offer or retargeting ads on social media, for example. Ultimately, cohort analysis allows companies to optimize their service offerings (and marketing) to provide a more targeted, personalized experience. You can learn more about how to run cohort analysis using Google Analytics .

Cohort analysis in action: How Ticketmaster used cohort analysis to boost revenue

e. Cluster analysis

Cluster analysis is an exploratory technique that seeks to identify structures within a dataset. The goal of cluster analysis is to sort different data points into groups (or clusters) that are internally homogeneous and externally heterogeneous. This means that data points within a cluster are similar to each other, and dissimilar to data points in another cluster. Clustering is used to gain insight into how data is distributed in a given dataset, or as a preprocessing step for other algorithms.

There are many real-world applications of cluster analysis. In marketing, cluster analysis is commonly used to group a large customer base into distinct segments, allowing for a more targeted approach to advertising and communication. Insurance firms might use cluster analysis to investigate why certain locations are associated with a high number of insurance claims. Another common application is in geology, where experts will use cluster analysis to evaluate which cities are at greatest risk of earthquakes (and thus try to mitigate the risk with protective measures).

It’s important to note that, while cluster analysis may reveal structures within your data, it won’t explain why those structures exist. With that in mind, cluster analysis is a useful starting point for understanding your data and informing further analysis. Clustering algorithms are also used in machine learning—you can learn more about clustering in machine learning in our guide .

Cluster analysis in action: Using cluster analysis for customer segmentation—a telecoms case study example

f. Time series analysis

Time series analysis is a statistical technique used to identify trends and cycles over time. Time series data is a sequence of data points which measure the same variable at different points in time (for example, weekly sales figures or monthly email sign-ups). By looking at time-related trends, analysts are able to forecast how the variable of interest may fluctuate in the future.

When conducting time series analysis, the main patterns you’ll be looking out for in your data are:

• Trends: Stable, linear increases or decreases over an extended time period.
• Seasonality: Predictable fluctuations in the data due to seasonal factors over a short period of time. For example, you might see a peak in swimwear sales in summer around the same time every year.
• Cyclic patterns: Unpredictable cycles where the data fluctuates. Cyclical trends are not due to seasonality, but rather, may occur as a result of economic or industry-related conditions.

As you can imagine, the ability to make informed predictions about the future has immense value for business. Time series analysis and forecasting is used across a variety of industries, most commonly for stock market analysis, economic forecasting, and sales forecasting. There are different types of time series models depending on the data you’re using and the outcomes you want to predict. These models are typically classified into three broad types: the autoregressive (AR) models, the integrated (I) models, and the moving average (MA) models. For an in-depth look at time series analysis, refer to our guide .

Time series analysis in action: Developing a time series model to predict jute yarn demand in Bangladesh

g. Sentiment analysis

When you think of data, your mind probably automatically goes to numbers and spreadsheets.

Many companies overlook the value of qualitative data, but in reality, there are untold insights to be gained from what people (especially customers) write and say about you. So how do you go about analyzing textual data?

One highly useful qualitative technique is sentiment analysis , a technique which belongs to the broader category of text analysis —the (usually automated) process of sorting and understanding textual data.

With sentiment analysis, the goal is to interpret and classify the emotions conveyed within textual data. From a business perspective, this allows you to ascertain how your customers feel about various aspects of your brand, product, or service.

There are several different types of sentiment analysis models, each with a slightly different focus. The three main types include:

Fine-grained sentiment analysis

If you want to focus on opinion polarity (i.e. positive, neutral, or negative) in depth, fine-grained sentiment analysis will allow you to do so.

For example, if you wanted to interpret star ratings given by customers, you might use fine-grained sentiment analysis to categorize the various ratings along a scale ranging from very positive to very negative.

Emotion detection

This model often uses complex machine learning algorithms to pick out various emotions from your textual data.

You might use an emotion detection model to identify words associated with happiness, anger, frustration, and excitement, giving you insight into how your customers feel when writing about you or your product on, say, a product review site.

Aspect-based sentiment analysis

This type of analysis allows you to identify what specific aspects the emotions or opinions relate to, such as a certain product feature or a new ad campaign.

If a customer writes that they “find the new Instagram advert so annoying”, your model should detect not only a negative sentiment, but also the object towards which it’s directed.

In a nutshell, sentiment analysis uses various Natural Language Processing (NLP) algorithms and systems which are trained to associate certain inputs (for example, certain words) with certain outputs.

For example, the input “annoying” would be recognized and tagged as “negative”. Sentiment analysis is crucial to understanding how your customers feel about you and your products, for identifying areas for improvement, and even for averting PR disasters in real-time!

Sentiment analysis in action: 5 Real-world sentiment analysis case studies

4. The data analysis process

In order to gain meaningful insights from data, data analysts will perform a rigorous step-by-step process. We go over this in detail in our step by step guide to the data analysis process —but, to briefly summarize, the data analysis process generally consists of the following phases:

Defining the question

The first step for any data analyst will be to define the objective of the analysis, sometimes called a ‘problem statement’. Essentially, you’re asking a question with regards to a business problem you’re trying to solve. Once you’ve defined this, you’ll then need to determine which data sources will help you answer this question.

Collecting the data

Now that you’ve defined your objective, the next step will be to set up a strategy for collecting and aggregating the appropriate data. Will you be using quantitative (numeric) or qualitative (descriptive) data? Do these data fit into first-party, second-party, or third-party data?

Learn more: Quantitative vs. Qualitative Data: What’s the Difference? 

Cleaning the data

Unfortunately, your collected data isn’t automatically ready for analysis—you’ll have to clean it first. As a data analyst, this phase of the process will take up the most time. During the data cleaning process, you will likely be:

• Removing major errors, duplicates, and outliers
• Removing unwanted data points
• Structuring the data—that is, fixing typos, layout issues, etc.
• Filling in major gaps in data

Analyzing the data

Now that we’ve finished cleaning the data, it’s time to analyze it! Many analysis methods have already been described in this article, and it’s up to you to decide which one will best suit the assigned objective. It may fall under one of the following categories:

• Descriptive analysis , which identifies what has already happened
• Diagnostic analysis , which focuses on understanding why something has happened
• Predictive analysis , which identifies future trends based on historical data
• Prescriptive analysis , which allows you to make recommendations for the future

Visualizing and sharing your findings

We’re almost at the end of the road! Analyses have been made, insights have been gleaned—all that remains to be done is to share this information with others. This is usually done with a data visualization tool, such as Google Charts, or Tableau.

Learn more: 13 of the Most Common Types of Data Visualization

To sum up the process, Will’s explained it all excellently in the following video:

5. The best tools for data analysis

As you can imagine, every phase of the data analysis process requires the data analyst to have a variety of tools under their belt that assist in gaining valuable insights from data. We cover these tools in greater detail in this article , but, in summary, here’s our best-of-the-best list, with links to each product:

The top 9 tools for data analysts

• Microsoft Excel
• Jupyter Notebook
• Apache Spark
• Microsoft Power BI

6. Key takeaways and further reading

As you can see, there are many different data analysis techniques at your disposal. In order to turn your raw data into actionable insights, it’s important to consider what kind of data you have (is it qualitative or quantitative?) as well as the kinds of insights that will be useful within the given context. In this post, we’ve introduced seven of the most useful data analysis techniques—but there are many more out there to be discovered!

So what now? If you haven’t already, we recommend reading the case studies for each analysis technique discussed in this post (you’ll find a link at the end of each section). For a more hands-on introduction to the kinds of methods and techniques that data analysts use, try out this free introductory data analytics short course. In the meantime, you might also want to read the following:

• The Best Online Data Analytics Courses for 2024
• What Is Time Series Data and How Is It Analyzed?
• What is Spatial Analysis?

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Data Analytics in Marketing Research: Definition, Types, Process, and More

Data Analytics is a critical function affecting all aspects of the business. This article covers broad data analytic topics for those new to the area of data analytics. At Sawtooth Software, we focus on marketing research and primary data collection through survey research, so this article specifically calls out the use of data analytics in marketing sciences.

Before diving deep into the breadth of data analytics, let’s summarize key takeaways you will gain from this guide:

With that introduction, let’s dive deeper into the field of Data Analytics .

Table of Contents

What Is Data Analytics?

At its core, Data Analytics involves the computational analysis of data or statistics. Data can involve numeric values, text, graphics, video or audio files. The value of data analytics lies in its ability to transform vast amounts of raw, often unstructured data into actionable insights. These insights can then guide decision-making, optimize operations, and unveil opportunities for innovation.

Consider a retail business that leverages data analytics to understand customer purchasing patterns, preferences, and behaviors. By analyzing sales data, customer feedback, social media trends, along with primary survey data, the business can tailor its product offerings, improve customer service, predict future trends, and optimize products and pricing for new or existing products. This practical application underscores the transformative power of data analytics in driving business strategy and growth.

Data Analytics vs. Data Science

While often used interchangeably, Data Analytics and Data Science involve nuanced differences, with complementary roles within an organization. Data Analytics focuses on processing and performing statistical analysis on existing datasets. In contrast, Data Science typically involves heavier programming, developing algorithms, and model-building to derive additional insights to solve complex problems and predict future outcomes. Data scientists often leverage machine learning and AI (Artificial Intelligence) in building algorithms, models, and applications.

The impact of both fields on Decision-Making is important. Data analytics provides a more immediate, focused insight primarily aimed at enhancing operational efficiency and answering specific questions. Data Science, on the other hand, dives deeper into predictive analysis, machine learning, and AI to forecast future trends and behaviors.

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Types of Data Analysis

Data Analysis can be broadly categorized into four main types, each serving a unique purpose in the data analytics landscape. Understanding these types helps you to apply the right analytical approach to your data to derive meaningful conclusions and strategies.

Descriptive Analytics

This type of analytics focuses on the “what” and is the most basic and commonly used. For market research surveys, descriptive analytics summarizes responses to demographic, psychographic, attitudinal, brand usage data, and the like. For historical data, it aims to provide a clear picture of what has happened in the past by summarizing such things as sales data, operations data, advertising data, and website click traffic. Descriptive analytics answers the "What happened?" question by analyzing key performance indicators (KPIs) and metrics. For example, a business might use descriptive analytics to understand its sales trends, customer engagement levels, or production efficiencies over the past year.

Diagnostic Analytics

Moving beyond the “what” to understand the “why,” diagnostic analytics involves a deeper dive into data to examine patterns of association or correlation, with the hope to uncover root causes of attitudes, preference, events or trends. It employs techniques such as correlation analysis, t-tests, chi-square tests, key drivers analysis, and tree-based analysis (such as CART or random forests). For customer satisfaction research key drivers analysis tries to explain how overall customer satisfaction or loyalty can be improved by improving the features or elements of the product or service delivery. An organization might also leverage diagnostic analytics to identify why certain groups of respondents are more likely to be price sensitive or why customer churn increased in a specific period.

Predictive Analytics

This forward-looking analysis leverages data and models that can predict future outcomes. Conjoint analysis is a widely used predictive analytics approach for studying how changes to product features and prices affect demand. MaxDiff (best-worst scaling) is often used to assess which product claims will likely increase new product trial, or which side effects would most discourage patients from undergoing a cancer treatment therapy. Machine learning algorithms such as random forests can score a database to predict which customers are most likely to be receptive to an offer. As another example, a financial institution might use predictive analytics to assess the risk of loan default based on a customer's credit history, transaction data, and market conditions.

Prescriptive Analytics

An advanced form of analytics, prescriptive analytics, goes a step further by recommending actions you can take to affect desired outcomes. It not only predicts what will happen but also suggests various courses of action and the potential implications of each. This type of analytics is particularly valuable in complex decision-making environments. For example, a conjoint analysis market simulator leveraging optimization search routines can determine the right mix of product features and price to reach a particularly valuable market segment .

Each of these types of data analysis plays a critical role in an organization's data-driven decision-making process, enabling businesses to understand their past performance, diagnose issues, create successful products and services, predict future trends, and make informed choices that align with their strategic objectives.

Data Analytics Real-World Example

Consider the case of a data analyst working for an e-commerce platform. By analyzing customer purchase history, the analyst identifies a trend of increased sales in eco-friendly products ( descriptive analytics , the “what”). A survey is designed and conducted to dig deeper into which customers are preferring eco-friendly products, why they prefer them, and for which usage occasions ( diagnostic analytics , the “why”). Within another market research survey, a conjoint analysis or MaxDiff study is included for determining the right product claims, product features, and pricing, targeted to which market segments to develop new products for sales growth ( predictive and prescriptive analytics ).

The role of a data analyst is dynamic and impactful, bridging the gap between data and strategic decision-making. It's a role that requires not only technical skills but also curiosity, creativity, and a keen understanding of the business landscape.

The Data Analysis Process

Breaking down a data analytics process into systematic steps can demystify the journey, making it more approachable and manageable. The Data Analysis Process is a structured approach that guides data analysts from the initial phase of understanding the business problem to the final stage of delivering actionable insights.

Step 1: Defining the Question

The first and perhaps most critical step in the data analysis process is defining the question . This involves understanding the business objectives, the decisions that need to be supported by the data, and the specific questions that the analysis aims to answer. A well-defined question not only provides direction for the analysis but also ensures that the outcomes are relevant and actionable.

Step 2: Collecting Clean Data

Data collection is the next step, where data analysts gather the necessary data from various sources. This could include internal databases, secondary sources of data, customer surveys, and more. Ensuring the cleanliness of the data is paramount at this stage; hence, data cleaning and preprocessing become essential tasks. This involves removing inaccuracies, inconsistencies, handling missing values, and trimming outliers to ensure the data is reliable and accurate for analysis. For market research surveys, this also involves identifying unreliable respondents, fraudulent respondents, and records completed by survey bots.

Step 3: Data Analysis and Interpretation

With clean data in hand, analysts proceed to the heart of the process: data analysis and interpretation . This involves applying statistical methods and analytical models to the data to identify patterns, trends, and correlations. The choice of techniques varies depending on the data and the questions at hand, ranging from simple descriptive statistics to complex predictive models.

Step 4: Data Visualization and Sharing Findings

Data visualization plays a crucial role in this phase, as it transforms complex data sets into visual representations that are easier to understand and interpret. Tools like charts, graphs, and dashboards are used to illustrate the findings compellingly and intuitively.

Finally, sharing the findings with stakeholders is an integral part of the data analysis process. This involves not just presenting the data, but also providing insights, recommendations, and potential implications in a clear and persuasive manner. Effective communication is key here, as the ultimate goal is to inform decision-making and drive action based on the data insights.

For product optimization and pricing research, market simulators from conjoint analysis can be even more useful to a decision-maker than charts and graphs. They allow the manager to test thousands of potential product formulations and prices, to find the right products to best reach target market segments.

Example Scenario

Imagine a data analyst working for a healthcare provider, tasked with reducing patient wait times. By following the data analysis process, the analyst:

• Defines the question: What factors contribute to increased wait times?
• Collects and cleans data from patient records, appointment systems, and feedback surveys.
• Analyzes the data to identify patterns, such as peak times for appointments and common delays in the patient check-in process.
• Visualizes the findings using graphs that highlight peak congestion times and the factors causing delays.
• Shares the insights with the healthcare management team, recommending adjustments to appointment scheduling and check-in processes to reduce wait times.

This systematic approach not only provides actionable insights but also showcases the power of data analytics in solving real-world problems.

Understanding the data analysis process is foundational for anyone looking to delve into data analytics, providing a roadmap for transforming data into insights that can drive informed decision-making.

Tools and Techniques

The field of Data Analytics is supported by a variety of tools and techniques designed to extract, analyze, and interpret data. Market research surveys are often a key source of data. The choice of the right analytics tools and the application of specific analytical techniques can significantly impact the quality of the insights generated. In this section, we will explore some of the key data analytics techniques and highlight commonly used tools, especially for primary survey research, providing tips on how to choose the right ones for specific projects.

Key Data Analytics Techniques

Statistical Testing: When summarizing data using means (for continuous data) or percent of observations falling into different categories (for categorical or nominal data), we often want to know whether the differences we’re observing between groups of respondents, branches of a company, or time periods are statistically meaningful (that they were unlikely to occur by chance).

Correlation Analysis : A statistical approach that examines whether there is a positive, negative, or no correlation between two continuous variables. The square of the correlation coefficient indicates the percent of variance in one variable that is explained by the other.

Regression Analysis : A statistical method used to examine the relationship between dependent (outcome) and independent (predictor) variables. There are regression techniques for predicting continuous variables (ordinary least squares) as well as for categorical outcomes (logistic regression). Regression analysis is particularly useful for identifying relationships between variables, making predictions, and forecasting.

Tree-Based Analysis : These techniques are used for finding which variables tend to predict or explain some outcome, such as purchase of a product, or diagnosis with a disease. Common examples are Classification and Regression Trees (CART) and Random Forests, a combination of multiple trees that can be ensembled for a more accurate consensus prediction.

Time-Series Analysis : Focused on analyzing data points collected or recorded at specific time intervals. This technique is crucial for trend analysis, seasonal pattern identification, and forecasting.

Cluster Analysis : A family of methods used to group a set of objects (such as respondents) in such a way that objects in the same group (called a cluster) are more similar to each other than to those in other groups. It’s extensively used in market segmentation and targeting strategies. Common approaches include k-means clustering, latent class clustering, and ensemble approaches that leverage multiple techniques to achieve a more robust consensus solution.

Conjoint Analysis and MaxDiff: Discrete choice methods often used in market research and economics for assessing the importance of features, measuring price sensitivity , and predicting demand for products or services.

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Commonly Used Data Analytics Tools

Excel : A versatile tool for basic data analysis, familiar to most professionals, capable of handling various data analysis functions including pivot tables, basic statistical functions, and data visualization.

SQL : Essential for data extraction, especially from relational databases. SQL allows analysts to query specific data from large databases efficiently.

Python/R : Both are powerful programming languages favored in data analytics for their libraries and packages that support data manipulation, statistical analysis, and machine learning.

Tableau/Power BI : These tools are leaders in data visualization, providing robust platforms for creating dynamic and interactive dashboards and reports.

Sawtooth Software : Provides tools, support services, and consulting services for designing and fielding market research surveys, as well as conducting conjoint analysis, MaxDiff, and cluster analysis.

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Choosing the Right Tools and Techniques

Selecting the appropriate tools and techniques depends on several factors:

Project Requirements : The nature of the data and the specific questions you are trying to answer will guide your choice. For instance, Python might be preferred for its machine learning capabilities, while Tableau is chosen for sophisticated visualizations.

Data Size and Complexity : Large datasets and complex analyses might require more advanced tools like Python or R, whereas Excel (limited to around 1 million rows and 16 thousand columns) could suffice for smaller, simpler datasets.

Skill Set : The proficiency of the data analyst in using these tools also plays a significant role. It’s essential to balance the choice of tool with the analyst's comfort level and expertise.

Budget and Resources : Some tools require significant investment, both in terms of licenses and training. Open-source options like Python and R offer powerful functionalities at no cost.

Example Application

Consider a retail company looking to optimize its inventory levels based on historical sales data. The data analyst might use:

• SQL to extract sales data from the company's database.
• Python for conducting time-series analysis to identify sales trends and predict future demand.
• Tableau to create visualizations that illustrate these trends and forecasts, facilitating strategic discussions on inventory management.

Through the strategic application of these tools and techniques, data analysts can uncover valuable insights that drive informed decision-making and strategic planning within organizations.

The exploration of tools and techniques underscores the versatility and power of data analytics. Whether through statistical analysis, predictive modeling, or insightful visualizations, these tools empower analysts to turn data into strategic assets.

Importance and Uses of Data Analytics

Data analytics has become a pivotal element of business strategy, influencing decisions across all levels of an organization. Its importance cannot be overstated, as it provides the insights needed for businesses to innovate, stay competitive, and improve operational efficiency. This section explores the significance of data analytics across various domains, including healthcare, product optimization and pricing, and its relevance for small enterprises and startups.

Embracing data analytics allows organizations to move from intuition-based decisions to informed strategies. As we advance, the integration of data analytics into every aspect of business operations and strategy will become more pronounced, highlighting its critical role in shaping the future of industries worldwide.

Transforming Business Success

Data analytics empowers businesses to make informed decisions by providing a deep understanding of customer behavior, market trends, and operational performance. It enables companies to:

• Optimize Operations : By analyzing data, businesses can identify inefficiencies in their operations and find ways to reduce costs and improve productivity.
• Enhance Customer Experience : Data analytics allows businesses to understand their customers' preferences and behaviors, leading to improved revenues, customer satisfaction and loyalty.
• Product Innovation/Optimization and Pricing : Survey research methods such as conjoint analysis and MaxDiff are especially useful for optimizing features for and pricing products/services, keeping companies at the forefront of innovation and competitiveness.

In healthcare, data analytics plays a critical role in improving patient outcomes and operational efficiency. By analyzing patient data, healthcare providers can:

• Predict Outbreaks : Data analytics can help in predicting disease outbreaks, enabling healthcare systems to prepare and respond effectively.
• Personalize Treatment : Analytics (including MaxDiff and conjoint analysis) can elicit real-time preferences from patients that can lead to better personalized treatment plans, improving patient care and outcomes. Several groups of physicians and academic researchers have presented research at Sawtooth Software conferences on using these tools for facilitating better communication between patients and doctors and selecting treatment plans for diseases such as cancer to result in improved outcomes.
• Improve Operational Efficiency : Data analytics can optimize hospital operations, reducing wait times and improving patient flow.

Product Optimization and Pricing

Repositioning existing products, developing new products, and setting effective pricing strategies are vital to most any business. By using gold standard tools for survey research such as conjoint analysis and MaxDiff, businesses can:

• Find Optimal Sets of Features : Conjoint analysis can within a single survey research project evaluate 1000s of potential feature configurations, determining which feature sets will compete best relative to specific competitors.
• Identify Profitable Target Segments : Conjoint analysis or MaxDiff are excellent techniques for identifying and sizing market segments that have specific needs and are associated with different levels of price sensitivity.
• Measure Price Elasticity: Choice-Based Conjoint (CBC) analysis is particularly valuable for estimating price elasticity of demand for the firm’s brand(s), as well as assessing how changes to competitor’s prices affect quantity demanded for the firm’s brand(s) ( cross-elasticity ).

Relevance for Small Enterprises and Startups

For small enterprises and startups, data analytics offers a competitive edge, enabling them to:

• Make Informed Decisions : Even with limited resources, small businesses can use data analytics to make strategic decisions based on market trends and customer feedback.
• Identify Opportunities : Analytics can reveal market gaps and customer needs, providing startups with insights to innovate and capture new markets.

The Role of a Data Analyst

In the heart of data-driven organizations lies the Data Analyst , a professional whose responsibilities are as varied as they are critical. Understanding the role of a data analyst not only highlights the importance of data analytics in modern business but also sheds light on the skills and perspectives needed to excel in this field.

Responsibilities and Tasks

A data analyst's journey often begins with problem formulation and developing hypotheses and strategies for solving a business or organizational problem. Next often follows data collection, ensuring the quality and accuracy of the data sourced from various channels, including survey research. This foundational step is critical, as the integrity of the data directly impacts the insights derived from it. The analyst then proceeds to clean and preprocess the data, preparing it for analysis. This involves handling missing values, removing duplicates, trimming outliers, and ensuring the data is in a format suitable for analysis.

The core of a data analyst's role involves statistical analysis and data modeling to interpret the data. They employ a range of techniques, from simple descriptive statistics to more complex predictive models, to unearth trends, patterns, and correlations within the data.

However, the role extends beyond just analyzing data. Data visualization and reporting are equally important, as these allow the analyst to communicate their findings in a clear, compelling manner. Whether through dashboards, reports, or presentations, the ability to present data in an accessible way is crucial for informing decision-making processes within an organization.

Professional Insights

From the perspective of a seasoned data analyst, the job is not just about numbers and algorithms; it's about solving challenging business and organizational problems and storytelling with data. It involves translating complex datasets into actionable insights that can drive strategy and impact. An effective data analyst combines analytical skills with business acumen, understanding the broader context in which the data exists.

Career Opportunities in Data Analytics

The field of data analytics offers a dynamic career landscape, characterized by a high demand for skilled professionals capable of turning data into actionable insights. As businesses across industries continue to recognize the value of data-driven decision-making, the demand for data analysts has surged, creating a wealth of opportunities for those equipped with the right skills and knowledge. This section will explore career prospects, including job growth, and discuss the relevance of degrees and certifications in data analytics.

Job Growth and Demand

The demand for data analysts is projected to grow significantly in the coming years. According to industry reports and labor statistics, the job market for data analysts is expected to grow much faster than the average for all occupations. This growth is driven by the increasing volume of data generated by businesses and the need to analyze this data to make informed decisions.

• Projected Job Growth : Data analytics roles are expected to see one of the highest rates of job growth across all sectors.
• Industries Hiring : While technology and finance traditionally lead in hiring data analysts, healthcare, marketing, and retail are rapidly catching up, reflecting the broad applicability of data analytics skills.

Salary Ranges

Salaries for data analysts can vary widely based on experience, location, and industry. However, data analysts typically command competitive salaries, reflecting the high demand and specialized skill set required for the role.

• Entry-Level Positions : Even at entry levels, data analysts can expect salaries that are competitive, with potential for rapid growth as experience and skills develop.
• Senior Roles : Experienced data analysts, especially those with specialized skills or leadership roles, can command significantly higher salaries.

Degrees and Certifications

While a degree in data science, statistics, computer science, or a related field can provide a strong foundation, the field of data analytics also values practical experience and specialized skills.

• Relevant Degrees : Bachelors and masters degrees in relevant fields are highly valued, but not always required.
• Certifications : Certifications can supplement academic degrees and provide evidence of specialized skills in data analytics tools and methodologies. Popular certifications include Certified Analytics Professional (CAP), Google Data Analytics Professional Certificate, and various platform-specific certifications (e.g., Tableau, SAS).

Making It in Data Analytics

Success in a data analytics career is not solely determined by technical skills. Employers also value problem-solving abilities, business acumen, and the capacity to communicate complex findings in a clear and actionable manner. Continuous learning and adaptation to new tools, technologies, and methodologies are essential in this rapidly evolving field.

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Data analytics is not just a tool but a strategic asset that can drive significant business value, enhance operational efficiency, and foster innovation across various sectors. From improving healthcare outcomes to enabling small businesses to compete more effectively, the applications of data analytics are vast and varied.

As we embrace the future, the importance of data analytics in driving business success and societal improvement will only continue to grow. For those considering a career in data analytics or looking to implement data-driven strategies in their operations, the potential is limitless. The benefits of data-driven decision-making underscore the transformative power of data analytics, making it an indispensable part of modern business and governance.

Whether you are a budding data analyst, a business leader, or simply curious about the potential of data analytics, the journey into this field is not only rewarding but essential for those looking to make an impact in the digital age. 

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• Advanced Analytic Techniques

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• Market Research

Learn how and when to use advanced analytic techniques in your market research projects. 

This Principles Express course, Advanced Analytic Techniques , serves as a primer for some of the more advanced statistical methods you may encounter as a researcher, with greater attention to techniques which are frequently used with secondary data. Topics include: conjoint analysis, multiple regression, cluster analysis for segmentation, linear regression, perceptual mapping and factor analysis. You are not expected to memorize complicated formulas; rather, this course teaches the principles behind commonly used advanced statistical methods and when to use them.

What You'll Learn

Learning objectives, who should attend, course information.

Learn which analytic techniques to use with primary and secondary research data.

As more and more data primary and secondary research sources emerge in the "age of big data," selecting appropriate advanced analysis techniques to extract insights is becoming increasingly essential to decision making. The first step is to understand the business question at hand. The second is to assess the data available for you to address the business question. 

Certain analysis techniques are only appropriate with primary research data, whereas other analysis techniques are only appropriate with secondary data. Some techniques can be applied to either data source. 

This course will introduce you to the most common advanced analytical techniques in use today, with greater attention to techniques which are applied to secondary data. Examples are presented with each technique to demonstrate how insights can be extracted with the technique along with a conversation on what actions might be taken based on such insight. While statistical methods and terminology are discussed, explanations are purposely not detailed in order to help you focus on the overarching applied concepts behind each.

As more and more data primary and secondary research sources emerge in the "age of big data," learning to select appropriate analysis techniques to extract insights is becoming increasingly essential to decision making. Understand the business question at hand and properly assess the available data.

Certain analysis techniques are only appropriate with primary research data, whereas other analysis techniques are only appropriate with secondary data. Some techniques can be applied to either data source.

Learn the most common advanced analytical techniques in use today with greater attention to techniques which are applied to secondary data. Practice techniques through presented examples and discuss statistical methods to provide focus on the overarching applied concepts behind each.

After completing this course you should be able to:

• Describe a common framework that distinguishes between multivariate analytic techniques and helps guide the decision of what technique to use when, based on the following factors—dependence, interdependence, number of dependent variables, type of relationship, item being analyzed, nature of metric, and the nature of the business question being addressed.
• Compare and contrast the different patterns that express the relationship between two variables (e.g., nonlinear, linear, curvilinear, s-shaped, etc.).
• Distinguish between interpolation and extrapolation.
• Describe what Factor Analysis is, what it does, what type of input data is generally acceptable, and common applications in market research.
• Describe the concept of Segmentation Analysis, what it does, what type of input data is generally acceptable, various techniques on how one may cluster data (e.g., K-Means, RFM, Pareto, etc.) and common segmentation applications in market research.
• Describe what Perceptual Mapping (including the use of Multidimensional Scaling) is and common applications in market research.
• Describe the different techniques used to measure association (i.e., Correlation, Simple Regression, and Multiple Regression), what they do, what type of input data is generally acceptable, and common applications in market research.
• Describe Conjoint Analysis and Choice Modeling, what they do, what type of input data is generally acceptable, and common applications in market research.
• Describe more advanced measures of association (e.g., Logistical Regression and Structural Equation Modeling), what they do, what type of input data is generally acceptable, and common applications in market research.
• Describe what Discriminant Analysis is, what it does, what type of input data is generally acceptable, and common applications in market research.
• Identify the most popular machine learning techniques and describe how researchers can use them to generate insight.
• Describe what neural network analysis is, what it does, what type of input data is generally acceptable. Describe common applications in market research.
• Describe the concept of Marketing Mix Modeling, what it does, what type of input data is generally acceptable, techniques that are used (e.g., multiple regression, Bayesian regression, etc.) and common applications in market research.
• Describe Time Series Analysis, what it does, what type of input data is generally acceptable, what techniques are used, and common applications in market research.
• Describe the difference between statistical significance and business significance.
• Entry-level researchers looking for a solid introduction to quantitative data analysis.
• Mid-level staff seeking to expand their skillset.
• Experienced researchers looking to catch up with the latest developments.
• Corporations seeking professional development options for their internal training portfolio.
• Suppliers seeking courses for new-employee onboarding.
• Researchers whose job involves leading or contributing to project design, particularly those around secondary data.
• Analysts needing to understand how best to analyze quantitative data, and the pitfalls to avoid.
• Client-side researchers responsible for designing research and ensuring that the analysis leads to reliable insights.
• People just entering the research field who want to understand this important aspect of the research process.
• Entry-level researchers looking for a solid introduction to quantitative data analysis or mid-level staff seeking to expand their skillset.
• Suppliers seeking courses for new-employee onboarding for peoplejust entering the research field.
• Enroll at any time
• Complete the course's required graded components within 30 days
• For more details on How Does the “Advanced Analytic Techniques” Course Work , please download the file.
• For Frequently Asked Questions , please download the file.
• View  Frequently Asked Questions  for more details.

$359 - Standard Fee

$329 - Association Discount (Members* of: Insights Association; ESOMAR; Canadian Research Insights Council, The Research Society, Intellus Worldwide, QRCA, AMAI, WAPOR-Latinoamérica, MRII Board of Directors, UGA MMR Advisory Board.)

$50 - One-Month Extension (only one extension is granted per participant)

*Membership will be verified.

Prepayment is required to be registered. The prices listed are per person (US Funds). Prices are subject to change.

Ray Poynter – Managing Director, The Future Place and Founder, NewMR

Ray has spent the last 35 years at the intersection of innovation, technology, and Market Research, during which time Ray has held director level positions with Vision Critical, Virtual Surveys, The Research Business, Millward Brown, Sandpiper and IntelliQuest.

Continuing Education Information

Students successfully completing graded components earn a Digital Badge and 1.2 University of Georgia Continuing Education Unit (CEU) from The University of Georgia.

As a graduate of the course you will be recognized by industry associations, employers, peer groups and other professionals as understanding how to translate your research findings into reports and presentations that grab your audience’s attention, address the business decision your client needs to make, and offer sound and useful recommendations. This recognition will help you advance in your company and the industry.

There are no prerequisites for enrolling in Advanced Analytic Techniques . However, the course assumes some knowledge of basic research design and quantitative research practices.

What knowledge is assumed by the Course?

You should be familiar with:

• The differences, strengths and limitations between primary and secondary data sources.
• An elementary understanding and exposure to statistical analysis.
• The ability to detect outlier observations.
• How to frame business problems and suggestions around data which might assist in solving such problems.

These topics are covered in detail in two separate Principles Express courses. A more thorough overview of some of these techniques can be found in: Introduction to Data Analysis and Working with Secondary Data: Syndicated and Big Data . See Principles Express courses for more details.

Preferred Browser: To take advantage of the different features (PDF files, URLs/links to external websites, animated exercises, audio and video clips) you should use a Windows or Macintosh-based browser . A robust browser such as Chrome , Firefox , Microsoft Edge , or Safari and a fast internet connection provide the best experience. The online platform supports many popular web browser versions. To find out if your computer's current software configuration is compatible, see System & Software Requirements .

None, however the course assumes some knowledge of basic research design and quantitative research practices.

Suggested Textbook (not required) Chakrapani, Chuck, Analytics for Customer Insights: A Non-Technical Introduction . ©2018. ISBN: 978-­0-920219-52-2 (print version recommended) or ISBN 978-­0-920219-52-2 (eBook).

Included in the online course are suggested reading assignments from the above textbook. These readings are not required content and will not be part of the testing for the course. The textbook suggestions are simply intended to add additional depth to your understanding of the topic.

Details are subject to change.

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Data Analysis Techniques in Research – Methods, Tools & Examples

Varun Saharawat is a seasoned professional in the fields of SEO and content writing. With a profound knowledge of the intricate aspects of these disciplines, Varun has established himself as a valuable asset in the world of digital marketing and online content creation.

Data analysis techniques in research are essential because they allow researchers to derive meaningful insights from data sets to support their hypotheses or research objectives.

Data Analysis Techniques in Research : While various groups, institutions, and professionals may have diverse approaches to data analysis, a universal definition captures its essence. Data analysis involves refining, transforming, and interpreting raw data to derive actionable insights that guide informed decision-making for businesses.

A straightforward illustration of data analysis emerges when we make everyday decisions, basing our choices on past experiences or predictions of potential outcomes.

If you want to learn more about this topic and acquire valuable skills that will set you apart in today’s data-driven world, we highly recommend enrolling in the Data Analytics Course by Physics Wallah . And as a special offer for our readers, use the coupon code “READER” to get a discount on this course.

Table of Contents

What is Data Analysis?

Data analysis is the systematic process of inspecting, cleaning, transforming, and interpreting data with the objective of discovering valuable insights and drawing meaningful conclusions. This process involves several steps:

• Inspecting : Initial examination of data to understand its structure, quality, and completeness.
• Cleaning : Removing errors, inconsistencies, or irrelevant information to ensure accurate analysis.
• Transforming : Converting data into a format suitable for analysis, such as normalization or aggregation.
• Interpreting : Analyzing the transformed data to identify patterns, trends, and relationships.

Types of Data Analysis Techniques in Research

Data analysis techniques in research are categorized into qualitative and quantitative methods, each with its specific approaches and tools. These techniques are instrumental in extracting meaningful insights, patterns, and relationships from data to support informed decision-making, validate hypotheses, and derive actionable recommendations. Below is an in-depth exploration of the various types of data analysis techniques commonly employed in research:

1) Qualitative Analysis:

Definition: Qualitative analysis focuses on understanding non-numerical data, such as opinions, concepts, or experiences, to derive insights into human behavior, attitudes, and perceptions.

• Content Analysis: Examines textual data, such as interview transcripts, articles, or open-ended survey responses, to identify themes, patterns, or trends.
• Narrative Analysis: Analyzes personal stories or narratives to understand individuals’ experiences, emotions, or perspectives.
• Ethnographic Studies: Involves observing and analyzing cultural practices, behaviors, and norms within specific communities or settings.

2) Quantitative Analysis:

Quantitative analysis emphasizes numerical data and employs statistical methods to explore relationships, patterns, and trends. It encompasses several approaches:

Descriptive Analysis:

• Frequency Distribution: Represents the number of occurrences of distinct values within a dataset.
• Central Tendency: Measures such as mean, median, and mode provide insights into the central values of a dataset.
• Dispersion: Techniques like variance and standard deviation indicate the spread or variability of data.

Diagnostic Analysis:

• Regression Analysis: Assesses the relationship between dependent and independent variables, enabling prediction or understanding causality.
• ANOVA (Analysis of Variance): Examines differences between groups to identify significant variations or effects.

Predictive Analysis:

• Time Series Forecasting: Uses historical data points to predict future trends or outcomes.
• Machine Learning Algorithms: Techniques like decision trees, random forests, and neural networks predict outcomes based on patterns in data.

Prescriptive Analysis:

• Optimization Models: Utilizes linear programming, integer programming, or other optimization techniques to identify the best solutions or strategies.
• Simulation: Mimics real-world scenarios to evaluate various strategies or decisions and determine optimal outcomes.

Specific Techniques:

• Monte Carlo Simulation: Models probabilistic outcomes to assess risk and uncertainty.
• Factor Analysis: Reduces the dimensionality of data by identifying underlying factors or components.
• Cohort Analysis: Studies specific groups or cohorts over time to understand trends, behaviors, or patterns within these groups.
• Cluster Analysis: Classifies objects or individuals into homogeneous groups or clusters based on similarities or attributes.
• Sentiment Analysis: Uses natural language processing and machine learning techniques to determine sentiment, emotions, or opinions from textual data.

Also Read: AI and Predictive Analytics: Examples, Tools, Uses, Ai Vs Predictive Analytics

Data Analysis Techniques in Research Examples

To provide a clearer understanding of how data analysis techniques are applied in research, let’s consider a hypothetical research study focused on evaluating the impact of online learning platforms on students’ academic performance.

Research Objective:

Determine if students using online learning platforms achieve higher academic performance compared to those relying solely on traditional classroom instruction.

Data Collection:

• Quantitative Data: Academic scores (grades) of students using online platforms and those using traditional classroom methods.
• Qualitative Data: Feedback from students regarding their learning experiences, challenges faced, and preferences.

Data Analysis Techniques Applied:

1) Descriptive Analysis:

• Calculate the mean, median, and mode of academic scores for both groups.
• Create frequency distributions to represent the distribution of grades in each group.

2) Diagnostic Analysis:

• Conduct an Analysis of Variance (ANOVA) to determine if there’s a statistically significant difference in academic scores between the two groups.
• Perform Regression Analysis to assess the relationship between the time spent on online platforms and academic performance.

3) Predictive Analysis:

• Utilize Time Series Forecasting to predict future academic performance trends based on historical data.
• Implement Machine Learning algorithms to develop a predictive model that identifies factors contributing to academic success on online platforms.

4) Prescriptive Analysis:

• Apply Optimization Models to identify the optimal combination of online learning resources (e.g., video lectures, interactive quizzes) that maximize academic performance.
• Use Simulation Techniques to evaluate different scenarios, such as varying student engagement levels with online resources, to determine the most effective strategies for improving learning outcomes.

5) Specific Techniques:

• Conduct Factor Analysis on qualitative feedback to identify common themes or factors influencing students’ perceptions and experiences with online learning.
• Perform Cluster Analysis to segment students based on their engagement levels, preferences, or academic outcomes, enabling targeted interventions or personalized learning strategies.
• Apply Sentiment Analysis on textual feedback to categorize students’ sentiments as positive, negative, or neutral regarding online learning experiences.

By applying a combination of qualitative and quantitative data analysis techniques, this research example aims to provide comprehensive insights into the effectiveness of online learning platforms.

Also Read: Learning Path to Become a Data Analyst in 2024

Data Analysis Techniques in Quantitative Research

Quantitative research involves collecting numerical data to examine relationships, test hypotheses, and make predictions. Various data analysis techniques are employed to interpret and draw conclusions from quantitative data. Here are some key data analysis techniques commonly used in quantitative research:

1) Descriptive Statistics:

• Description: Descriptive statistics are used to summarize and describe the main aspects of a dataset, such as central tendency (mean, median, mode), variability (range, variance, standard deviation), and distribution (skewness, kurtosis).
• Applications: Summarizing data, identifying patterns, and providing initial insights into the dataset.

2) Inferential Statistics:

• Description: Inferential statistics involve making predictions or inferences about a population based on a sample of data. This technique includes hypothesis testing, confidence intervals, t-tests, chi-square tests, analysis of variance (ANOVA), regression analysis, and correlation analysis.
• Applications: Testing hypotheses, making predictions, and generalizing findings from a sample to a larger population.

3) Regression Analysis:

• Description: Regression analysis is a statistical technique used to model and examine the relationship between a dependent variable and one or more independent variables. Linear regression, multiple regression, logistic regression, and nonlinear regression are common types of regression analysis .
• Applications: Predicting outcomes, identifying relationships between variables, and understanding the impact of independent variables on the dependent variable.

4) Correlation Analysis:

• Description: Correlation analysis is used to measure and assess the strength and direction of the relationship between two or more variables. The Pearson correlation coefficient, Spearman rank correlation coefficient, and Kendall’s tau are commonly used measures of correlation.
• Applications: Identifying associations between variables and assessing the degree and nature of the relationship.

5) Factor Analysis:

• Description: Factor analysis is a multivariate statistical technique used to identify and analyze underlying relationships or factors among a set of observed variables. It helps in reducing the dimensionality of data and identifying latent variables or constructs.
• Applications: Identifying underlying factors or constructs, simplifying data structures, and understanding the underlying relationships among variables.

6) Time Series Analysis:

• Description: Time series analysis involves analyzing data collected or recorded over a specific period at regular intervals to identify patterns, trends, and seasonality. Techniques such as moving averages, exponential smoothing, autoregressive integrated moving average (ARIMA), and Fourier analysis are used.
• Applications: Forecasting future trends, analyzing seasonal patterns, and understanding time-dependent relationships in data.

7) ANOVA (Analysis of Variance):

• Description: Analysis of variance (ANOVA) is a statistical technique used to analyze and compare the means of two or more groups or treatments to determine if they are statistically different from each other. One-way ANOVA, two-way ANOVA, and MANOVA (Multivariate Analysis of Variance) are common types of ANOVA.
• Applications: Comparing group means, testing hypotheses, and determining the effects of categorical independent variables on a continuous dependent variable.

8) Chi-Square Tests:

• Description: Chi-square tests are non-parametric statistical tests used to assess the association between categorical variables in a contingency table. The Chi-square test of independence, goodness-of-fit test, and test of homogeneity are common chi-square tests.
• Applications: Testing relationships between categorical variables, assessing goodness-of-fit, and evaluating independence.

These quantitative data analysis techniques provide researchers with valuable tools and methods to analyze, interpret, and derive meaningful insights from numerical data. The selection of a specific technique often depends on the research objectives, the nature of the data, and the underlying assumptions of the statistical methods being used.

Also Read: Analysis vs. Analytics: How Are They Different?

Data Analysis Methods

Data analysis methods refer to the techniques and procedures used to analyze, interpret, and draw conclusions from data. These methods are essential for transforming raw data into meaningful insights, facilitating decision-making processes, and driving strategies across various fields. Here are some common data analysis methods:

• Description: Descriptive statistics summarize and organize data to provide a clear and concise overview of the dataset. Measures such as mean, median, mode, range, variance, and standard deviation are commonly used.
• Description: Inferential statistics involve making predictions or inferences about a population based on a sample of data. Techniques such as hypothesis testing, confidence intervals, and regression analysis are used.

3) Exploratory Data Analysis (EDA):

• Description: EDA techniques involve visually exploring and analyzing data to discover patterns, relationships, anomalies, and insights. Methods such as scatter plots, histograms, box plots, and correlation matrices are utilized.
• Applications: Identifying trends, patterns, outliers, and relationships within the dataset.

4) Predictive Analytics:

• Description: Predictive analytics use statistical algorithms and machine learning techniques to analyze historical data and make predictions about future events or outcomes. Techniques such as regression analysis, time series forecasting, and machine learning algorithms (e.g., decision trees, random forests, neural networks) are employed.
• Applications: Forecasting future trends, predicting outcomes, and identifying potential risks or opportunities.

5) Prescriptive Analytics:

• Description: Prescriptive analytics involve analyzing data to recommend actions or strategies that optimize specific objectives or outcomes. Optimization techniques, simulation models, and decision-making algorithms are utilized.
• Applications: Recommending optimal strategies, decision-making support, and resource allocation.

6) Qualitative Data Analysis:

• Description: Qualitative data analysis involves analyzing non-numerical data, such as text, images, videos, or audio, to identify themes, patterns, and insights. Methods such as content analysis, thematic analysis, and narrative analysis are used.
• Applications: Understanding human behavior, attitudes, perceptions, and experiences.

7) Big Data Analytics:

• Description: Big data analytics methods are designed to analyze large volumes of structured and unstructured data to extract valuable insights. Technologies such as Hadoop, Spark, and NoSQL databases are used to process and analyze big data.
• Applications: Analyzing large datasets, identifying trends, patterns, and insights from big data sources.

8) Text Analytics:

• Description: Text analytics methods involve analyzing textual data, such as customer reviews, social media posts, emails, and documents, to extract meaningful information and insights. Techniques such as sentiment analysis, text mining, and natural language processing (NLP) are used.
• Applications: Analyzing customer feedback, monitoring brand reputation, and extracting insights from textual data sources.

These data analysis methods are instrumental in transforming data into actionable insights, informing decision-making processes, and driving organizational success across various sectors, including business, healthcare, finance, marketing, and research. The selection of a specific method often depends on the nature of the data, the research objectives, and the analytical requirements of the project or organization.

Also Read: Quantitative Data Analysis: Types, Analysis & Examples

Data Analysis Tools

Data analysis tools are essential instruments that facilitate the process of examining, cleaning, transforming, and modeling data to uncover useful information, make informed decisions, and drive strategies. Here are some prominent data analysis tools widely used across various industries:

1) Microsoft Excel:

• Description: A spreadsheet software that offers basic to advanced data analysis features, including pivot tables, data visualization tools, and statistical functions.
• Applications: Data cleaning, basic statistical analysis, visualization, and reporting.

2) R Programming Language:

• Description: An open-source programming language specifically designed for statistical computing and data visualization.
• Applications: Advanced statistical analysis, data manipulation, visualization, and machine learning.

3) Python (with Libraries like Pandas, NumPy, Matplotlib, and Seaborn):

• Description: A versatile programming language with libraries that support data manipulation, analysis, and visualization.
• Applications: Data cleaning, statistical analysis, machine learning, and data visualization.

4) SPSS (Statistical Package for the Social Sciences):

• Description: A comprehensive statistical software suite used for data analysis, data mining, and predictive analytics.
• Applications: Descriptive statistics, hypothesis testing, regression analysis, and advanced analytics.

5) SAS (Statistical Analysis System):

• Description: A software suite used for advanced analytics, multivariate analysis, and predictive modeling.
• Applications: Data management, statistical analysis, predictive modeling, and business intelligence.

6) Tableau:

• Description: A data visualization tool that allows users to create interactive and shareable dashboards and reports.
• Applications: Data visualization , business intelligence , and interactive dashboard creation.

7) Power BI:

• Description: A business analytics tool developed by Microsoft that provides interactive visualizations and business intelligence capabilities.
• Applications: Data visualization, business intelligence, reporting, and dashboard creation.

8) SQL (Structured Query Language) Databases (e.g., MySQL, PostgreSQL, Microsoft SQL Server):

• Description: Database management systems that support data storage, retrieval, and manipulation using SQL queries.
• Applications: Data retrieval, data cleaning, data transformation, and database management.

9) Apache Spark:

• Description: A fast and general-purpose distributed computing system designed for big data processing and analytics.
• Applications: Big data processing, machine learning, data streaming, and real-time analytics.

10) IBM SPSS Modeler:

• Description: A data mining software application used for building predictive models and conducting advanced analytics.
• Applications: Predictive modeling, data mining, statistical analysis, and decision optimization.

These tools serve various purposes and cater to different data analysis needs, from basic statistical analysis and data visualization to advanced analytics, machine learning, and big data processing. The choice of a specific tool often depends on the nature of the data, the complexity of the analysis, and the specific requirements of the project or organization.

Also Read: How to Analyze Survey Data: Methods & Examples

Importance of Data Analysis in Research

The importance of data analysis in research cannot be overstated; it serves as the backbone of any scientific investigation or study. Here are several key reasons why data analysis is crucial in the research process:

• Data analysis helps ensure that the results obtained are valid and reliable. By systematically examining the data, researchers can identify any inconsistencies or anomalies that may affect the credibility of the findings.
• Effective data analysis provides researchers with the necessary information to make informed decisions. By interpreting the collected data, researchers can draw conclusions, make predictions, or formulate recommendations based on evidence rather than intuition or guesswork.
• Data analysis allows researchers to identify patterns, trends, and relationships within the data. This can lead to a deeper understanding of the research topic, enabling researchers to uncover insights that may not be immediately apparent.
• In empirical research, data analysis plays a critical role in testing hypotheses. Researchers collect data to either support or refute their hypotheses, and data analysis provides the tools and techniques to evaluate these hypotheses rigorously.
• Transparent and well-executed data analysis enhances the credibility of research findings. By clearly documenting the data analysis methods and procedures, researchers allow others to replicate the study, thereby contributing to the reproducibility of research findings.
• In fields such as business or healthcare, data analysis helps organizations allocate resources more efficiently. By analyzing data on consumer behavior, market trends, or patient outcomes, organizations can make strategic decisions about resource allocation, budgeting, and planning.
• In public policy and social sciences, data analysis is instrumental in developing and evaluating policies and interventions. By analyzing data on social, economic, or environmental factors, policymakers can assess the effectiveness of existing policies and inform the development of new ones.
• Data analysis allows for continuous improvement in research methods and practices. By analyzing past research projects, identifying areas for improvement, and implementing changes based on data-driven insights, researchers can refine their approaches and enhance the quality of future research endeavors.

However, it is important to remember that mastering these techniques requires practice and continuous learning. That’s why we highly recommend the Data Analytics Course by Physics Wallah . Not only does it cover all the fundamentals of data analysis, but it also provides hands-on experience with various tools such as Excel, Python, and Tableau. Plus, if you use the “ READER ” coupon code at checkout, you can get a special discount on the course.

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Data Analysis Techniques in Research FAQs

What are the 5 techniques for data analysis.

The five techniques for data analysis include: Descriptive Analysis Diagnostic Analysis Predictive Analysis Prescriptive Analysis Qualitative Analysis

What are techniques of data analysis in research?

Techniques of data analysis in research encompass both qualitative and quantitative methods. These techniques involve processes like summarizing raw data, investigating causes of events, forecasting future outcomes, offering recommendations based on predictions, and examining non-numerical data to understand concepts or experiences.

What are the 3 methods of data analysis?

The three primary methods of data analysis are: Qualitative Analysis Quantitative Analysis Mixed-Methods Analysis

What are the four types of data analysis techniques?

The four types of data analysis techniques are: Descriptive Analysis Diagnostic Analysis Predictive Analysis Prescriptive Analysis

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Marketing Analytics Tool Must-Have Features in 2024

In the digital age, an abundance of data and customer touch-points means businesses can track and measure the impact of their marketing campaigns at a granular level. This capability is partly due to the data collection and analysis functionality inherent in marketing analytics tools.

Given that these tools are manifold, market leaders can find it challenging to choose one that meets their business needs.

This guide will help you evaluate marketing analytics solutions. You’ll learn the criteria your chosen analytics solution must fulfil to ensure your organization has the data to make informed business decisions.

• Data Integration with all your marketing tools but also your CRM and financial data to build meaningful metrics such as customer acquisition cost, lifetime value and ROI,
• Historical data storage to compare performance over time,
• Data management features to ensure data quality and reliability,
• Data analytics capabilities,
• Clear data visualization features to make your data understandable by your team and stakeholders,
• Ease of use for better user experience and long-term adoption.

But first, let’s look at the impact of a marketing analytics tool on your business growth.

The Business Impact of a Marketing Analytics Tool

Marketing analytics tools are vital for several reasons. They help you:

1. Gain a Comprehensive View of Your Marketing Activities

Marketing analytics tools give you the full picture of marketing campaign performance across disparate marketing channels. 

It doesn’t matter whether your company engages in social media marketing, email campaigns, paid digital advertising, or a combination of the three. You’ll always know how each channel contributes to your business objectives individually or as part of a collective.

2. Understand Your Customers

You can gain valuable insights into customer behaviour using marketing analytics tools. By analyzing past and present campaign performance, marketing teams can determine which campaigns provided the best customer experience and model future campaigns on them. 

You can also use the tool to help with customer segmentation , predict customer responses, and personalize customer journeys.

3. Manage Your Marketing Budget

Having a bird’s eye view of your marketing strategy across all the channels you use provides actionable insights you can rely on when creating your marketing budget. You can glean which marketing initiatives provide the best ROI and identify the channels where you acquire your highest-paying customers. 

Conversely, you can identify poor-performing strategies and reallocate marketing dollars to the most productive strategies and channels with the best conversion rates.

4. Make Better Decisions

Ultimately, marketing analytics tools help you make better decisions. Collecting, analyzing, and interpreting marketing data lets you know how your marketing efforts are faring. 

When you can identify winning (and losing) strategies, you can make informed decisions regarding resource allocation, customer engagement , and more. In turn, these strategic decisions will bring you closer to attaining business growth.

Marketing analytics also helps with strategy refinement and campaign forecasting. You can predict customer interactions with subsequent campaigns and make adjustments as needed.

Before You Go Out Shopping, Do You Have a Marketing Analytics Process?

Although there are many marketing analytics tools you can use to gather relevant data, the process to get the most out of this data is the same. Follow these steps for effective marketing analytics, from data collection to analysis: 

• Identify What You’ll Measure: The first step involves deciding the data you’ll track and analyze. To do that, you’ll need to identify the goal of your marketing strategy. Is it to increase brand awareness, boost website traffic, or enhance content engagement? Once you’re clear about your overall goal, it’ll be easier to create subgoals and determine your metrics of success for individual campaigns and marketing channels.  For instance, if your goal is to enhance content engagement, one of your subgoals could be to increase Instagram engagement by X% this month, with Instagram likes being one of your metrics.
• Collect Past and Present Data: The next step involves collecting data from your past and present marketing campaigns. You’ll collect data from past efforts to identify your best-performing marketing outings. The data from your current campaigns lets you know how you’re doing in real time.
• Develop and Optimize Marketing Strategies: Finally, you’ll use marketing analytics models to develop and optimize your strategies. The main models are descriptive, prescriptive, predictive, and diagnostic.
• Descriptive models entail using the data from past campaigns to guide present and future decisions. 
• Predictive models involve using data for consumer behaviour analysis . 
• Prescriptive models, meanwhile, involve improving customer experiences based on data obtained from customer touchpoints and engagement. 
• The diagnostic model entails uncovering the causes of specific outcomes and correlations among variables.

The purpose of the marketing analytics process is to base your decisions and campaigns on data. You can use it to shape any strategy, whether it’s an e-commerce, social media, or SaaS marketing strategy .

Checklist: What to Consider When Evaluating a Marketing Analytics Tool

Now that you know how to perform marketing analytics, it’s time to choose the best tool to help you. To evaluate marketing analytics solutions effectively, look for the following factors:

1 – Extensive Data Integration

Data integration refers to the process of combining and harmonizing data from multiple sources. These data sources may include:

• marketing tools – CDP, marketing automation, ad platforms…
• Social Platforms and Web Analytics 
• email marketing analytics tools
• sales outreach tools 
• financial systems to measure the impact of your marketing strategies on the business growth.

Since you probably already have some of these systems in place, you’ll need to check if the marketing analytics tool can connect with them first.

Integrating your data sources prevents data silos . Data integration ensures that you have a comprehensive view of all your marketing campaigns and track your customer journey through the entire journey in your sales and marketing funnel . So, you can easily identify trends, opportunities, and patterns across the entirety of your marketing channels.

Check whether the digital marketing analytics tool is compatible with several formats and offers flexibility and scalability. Also, make sure it offers quick and efficient data syncs. If it has data governance features that align with your business goals, it’s a keeper.

2 – Historical Data Storage

Keeping track of historical data is vital to identify changes in customers’ and prospects’ behaviours and adapt quickly.

Not to mention that social media platforms such as Facebook, Instagram, or LinkedIn stop showing data after a certain amount of time. If you haven’t exported that data into your marketing analytics tool, you simply lost the data. We’re not saying you should keep ALL the data – not sure it’s relevant to look at what happened on your Facebook ads campaigns in January 2010 – but always keeping 2-3 years of data is critical to put the current data into context. Don’t look for terabytes of storage, depending on the volume of data you need for your analysis, just a couple of hundred GB should suffice.

3 – Data Quality

Data quality is the most essential factor to consider when evaluating a marketing analytics tool. In simple terms, it’s a measure of a data set’s fitness for purpose about an organization’s marketing and data strategy needs. High-quality data has the following characteristics:

• Reliability
• Completeness

If your chosen marketing analytics software doesn’t have the data management and cleaning features to make data accessible, up-to-date, and consistent across marketing systems, it can cause you to make poor marketing decisions that lead to wasted time and marketing spend.

So how do you assess a marketing analytics solution for data quality?

You can check the tool’s data sources and collection methods to see whether they’re reliable and relevant to your business. It also helps to look at how it stores data and whether the said method is safe and complies with data safety regulations.

4 – Data Analytics

Data analysis refers to the tool’s ability to process data and transform it into information you can act on. The tool will use four different types of data analytics : 

• descriptive analytics
• diagnostic analytics
• predictive analytics
• prescriptive analytics

Through data analysis, you’ll understand not only the what of your marketing performance but also the why . In addition, the tool can help you predict what’ll happen in the future and uncover any improvements you can make.

You can confirm that a tool gets data analysis right by examining its data visualization features , reporting capability, and how accurate, actionable, and relevant its interpretation and recommendation features are. 

Also, look into its data modelling functionality, particularly whether it’s robust and rigorous.

5 – Interactive Data Visualization 

You should adopt a marketing analysis tool that’s intuitive, not just for digital marketers, but also for other employees. This ease of use will ensure that everyone in your organization can gain insights from the data and make smart data-backed decisions.

Thus, data visualization is the final criterion you can use to evaluate a marketing analytics solution. It refers to a tool’s ability to provide every stakeholder in an organization with insights, no matter their discipline.

Look at the tool’s aesthetics and its customization and collaboration features when evaluating how well it does data representation. Does it have a user-friendly interface or include customizable templates ? Can the tool create custom reports for a wide range of company stakeholders? Or, at the very least, can you easily create reports from the volume of data the tool provides? As an organization, you can check online resources on generative AI to gain insights and use cases for your marketing, finance, sales and other departments

The answers you arrive at will determine how easily your company’s stakeholders can interpret and use available data to the organization’s advantage.

Marketing Analytics Tool Integration with Parallel Systems 

There’s a high likelihood that your business uses several parallel software systems in its day-to-day operations. You may use tools for customer relationship management, email lookup , and project management, among others, that all contribute to your business processes and bottom line. You’ll have your sales and finance systems in place, too.

So, although many marketing analytics solutions integrate with marketing systems, you likely need one that integrates with other types of systems. 

ClicData is one tool that goes the extra mile. 

Our platform integrates with hundreds of data sources, letting you sync data from each. Here are some of the benefits a data analytics tool like ClicData provides:

• Standardized Data: With each year your business grows, it’ll get more complex to keep the various teams—from your finance to your sales and marketing teams—in your organization aligned. 

Thankfully, you won’t face this issue with a tool like ClicData. Our software’s native connectors let you share consistent data across teams, giving them unparalleled access that aids their productivity and decision-making.

• Reliable Data: Integration makes data importation and exportation across parallel systems a breeze. Gone are the days when manual data entry caused inaccurate and inconsistent data. Instead, with features like automated syncing, data cleaning and standardization, an analytics tool like ClicData will always provide accurate up-to-date data your teams can trust and rely on.
• Impactful metrics: Once all your data is in one place, you can use it in meaningful, calculated metrics such as conversion rates between each stage of the funnel, customer acquisition cost and lifetime value, and of course, your ROI.

Better company customer experiences with ClicData

But that’s not all. ClicData is also easy to use, which means you don’t need to be technologically inclined to avail of its benefits. There’s nothing worse than spending 6 months implementing an analytics tool to see everybody go back to Excel because it’s too complex.

With ClicData, you don’t have to download or install anything. You can easily get started with just a modern browser on Windows, Mac, or Linux.  If you want to design your own dashboard, just use the over 70 drag-and-drop widgets, or use a template from our extensive library .

Ultimately, an easy-to-use, intuitive, and yet very powerful tool like ClicData will foster stakeholders’ buy-in, increase end-users’ adoption, and will turn out to be a better investment in the long run. 

Supercharge Your Performance With a Solid Marketing Analytics Tool

Marketing analytics solutions ensure your organization has the data it needs to determine its marketing performance and time and resource-wasting strategies. 

But you need to choose the right tool.

To evaluate marketing analytics solutions effectively, look at their data integration, quality, analysis, visualization and ease of use. Make sure you also choose a solution that integrates with parallel systems like sales and finance. 

If you follow these tips, you can expect your marketing campaigns (and others) to yield the best results. Good luck!

About the author

David Campbell is a digital marketing specialist at Ramp Ventures. He helps manage the content marketing team at Right Inbox . When he’s not working, he enjoys traveling and trying to learn Spanish.

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• Property Insurance Analysis: 'Cautiously Optimistic'

Property Insurance Analysis: 'Cautiously Optimistic'

A financial-rating agency said it was “cautiously optimistic” about the Florida market, in part due to 2022 legislative reforms and declining Citizens’ policies.

TALLAHASSEE, Fla. — With a potentially volatile hurricane season ready to start, the AM Best financial-rating agency released a report Thursday that said Florida’s property insurance market is showing signs of improvement — but that time will tell.

The report said AM Best is “cautiously optimistic” about the Florida market, in part because of an overhaul passed by lawmakers and Gov. Ron DeSantis in 2022 that included trying to shield insurers from costly lawsuits. Other factors include a decrease since September in the number of homeowners getting coverage from the state’s Citizens Property Insurance Corp.

“While still too early to declare a win in the Florida personal property market, the signals look promising,” the report said. “The legislative reforms and declining Citizens’ policies in force mark a step in the right direction. Time will tell if favorable market results will continue and effectively managing hurricane risk is an ongoing challenge.”

AM Best analysts and industry officials largely echoed those conclusions during an online discussion held Thursday by the rating agency. They also cited issues such as improved profitability of insurers and a stable market for reinsurance, which is critical backup coverage in Florida’s hurricane-prone market.

“Let’s hope this is sustainable into the future,” said Dave Newell, vice president of membership and industry relations at the Florida Association of Insurance Agents and one of the speakers during the online event.

Homeowners have faced soaring rates in recent years and, in many cases, have gotten dropped by their insurers as the industry grappled with financial problems. The AM Best report said premiums doubled for typical homeowners.

“With more frequent and severe weather-related losses in recent years — including Hurricane Ian in 2022 and non-named storms — carriers have seen material volatility in both their operating results and (financial) surplus levels,” the report said. “To combat these market challenges, carriers needed significant rate increases. The average Florida homeowners policy premium doubled, increasing 102% in just the past three years, according to the Insurance Information Institute. In 2024, a slightly positive signal to the market has emerged, with a handful of companies nearing rate adequacy filing rate reductions, albeit marginally.”

Along with higher rates, the problems led to explosive growth at Citizens, which was created as an insurer of last resort but has become by far the largest property insurer in the state. Citizens had as few as 420,000 policies in 2019 but grew to 1.4 million in September 2023.

That number has gradually decreased through a “depopulation” program designed to shift policies into the private market. As of last week, Citizens had nearly 1.19 million policies.

State officials this spring have touted improvements in the market. As an example, the Florida Office of Insurance Regulation this month said at least eight carriers had filed for rate decreases in 2024, while 10 had filed to keep rates flat. It was not clear how many of the more than 7.4 million residential policies in the state would be affected by the decreases.

The AM Best report said that while “there are signs of stabilization, sustaining these improving market conditions will be critical.”

A big test could come during the six-month hurricane season that will start Saturday. Forecasters, including from the National Oceanic and Atmospheric Administration, are predicting a busier-than-average season, in part because of warm ocean waters that can fuel punishing storms.

The AM Best report, while describing improved signs in the industry, noted that Florida was hit in 2023 by only one hurricane, Idalia, which did not go through heavily populated areas.

“With the National Oceanic and Atmospheric Administration forecasting a higher frequency of named storms for 2024, it will be a good test for Florida insurers to see if these favorable results are sustainable,” the report said.

© 2024 The News Service of Florida. All rights reserved.

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2024 Housing Market Forecast and Predictions: Housing Affordability Finally Begins to Turnaround

As we look ahead to 2024 , we see a mix of continuity and change in both the housing market and economy. Against a backdrop of modest economic growth, slightly higher unemployment, and easing inflation longer term interest rates including mortgage rates begin a slow retreat. The shift from climbing to falling mortgage rates improves housing affordability, but saps some of the urgency home shoppers had previously sensed. Less frenzied housing demand and plenty of rental home options keep home sales relatively stable at low levels in 2024, helping home prices to adjust slightly lower even as the number of for-sale homes continues to dwindle. 

Realtor.com ® 2024 Forecast for Key Housing Indicators

Home Prices Dip, Improving Affordability

Home prices grew at a double-digit annual clip for the better part of two years spanning the second half of 2020 through 2022, a notable burst following a growing streak that spanned back to 2012. As mortgage rates climbed, home price growth flatlined, actually declining on an annual basis in early 2023 before an early-year dip in mortgage rates spurred enough buyer demand to reignite competition for still-limited inventory. Home prices began to climb again, and while they did not reach a new monthly peak, on average for the year we expect that the 2023 median home price will slightly exceed the 2022 annual median.

Nevertheless, even during the brief period when prices eased, using a mortgage to buy a home remained expensive. Since May 2022, purchasing the typical for-sale home listing at the prevailing rate for a 30-year fixed-rate mortgage with a 20% down payment meant forking over a quarter or more of the typical household paycheck. In fact, in October 2023, it required 39% of the typical household income and this share is expected to average 36.7% for the full calendar year in 2023. This figure has typically ranged around 21%, so it is well above historical average. We expect that the return to pricing in line with financing costs will begin in 2024, and home prices, mortgage rates, and income growth will each contribute to the improvement. Home prices are expected to ease slightly, dropping less than 2% for the year on average. Combined with lower mortgage rates and income growth this will improve the home purchase mortgage payment share relative to median income to an average 34.9% in 2024, with the share slipping under 30% by the end of the year.

Home Sales Barely Budge Above 2023’s Likely Record Low

After soaring during the pandemic, existing home sales were weighed down in the latter half of 2022 as mortgage rates took off, climbing from just over 3% at the start of the year to a peak of more than 7% in the fourth quarter. The reprieve in mortgage rates in early 2023, when they dipped to around 6%, brought some life to home sales, but the renewed climb of mortgage rates has again exerted significant pressure on home sales that is exacerbated by the fact that a greater than usual number of households bought homes over the past few years, and despite stories of pandemic purchase regret , for the most part, these homeowners continue to be happy in their homes. 

This is consistent with what visitors to Realtor.com report when asked why they are not planning to sell their homes. The number one reason homeowners aren’t trying to sell is that they just don’t need to; concern about losing an existing low-rate mortgage is the top financial concern cited. Our current projection is for 2023 home sales to tally just over 4 million, a dip of 19% over the 2022 5 million total. 

With many of the same forces at play heading into 2024, the housing chill will continue, with sales expected to remain essentially unchanged at just over 4 million. Although mortgage rates are expected to ease throughout the course of the year, the continuation of high costs will mean that existing homeowners will have a very high threshold for deciding to move, with many likely choosing to stay in place.  Moves of necessity–for job changes, family situation changes, and downsizing to a more affordable market–are likely to drive home sales in 2024. 

Shoppers Find Even Fewer Existing Homes For Sale

Even before the pandemic, housing inventory was on a long, slow downward trajectory. Insufficient building meant that the supply of houses did not keep up with household formation and left little slack in the housing market. Both homeowner and rental vacancy remain below historic averages . In contrast with the existing home market, which remains sluggish, builders have been catching up, with construction remaining near pre-pandemic highs for single-family and hitting record levels for multi-family . 

Despite this, the lack of excess capacity in housing has been painfully obvious in the for-sale home market. The number of existing homes on the market has dwindled. With home sales activity to continue at a relatively low pace, the number of unsold homes on the market is also expected to remain low.  Although mortgage rates are expected to begin to ease, they are expected to exceed 6.5% for the calendar year. This means that the lock-in effect, in which the gap between market mortgage rates and the mortgage rates existing homeowners enjoy on their outstanding mortgage, will remain a factor. Roughly two-thirds of outstanding mortgages have a rate under 4% and more than 90% have a rate less than 6%.

Rental Supply Outpaces Demand to Drive Mild Further Decline in Rents

After almost a full year of double-digit rent growth between mid-2021 and mid-2022, the rental market has finally cooled down, as evidenced by the year-over-year decline that started in May 2023 . In 2024, we expect the rental market will closely resemble the dynamics witnessed in 2023, as the tug of war between supply and demand results in a mild annual decline of -0.2% in the median asking rent.

New multi-family supply will continue to be a key element shaping the 2024 rental market.  In the third quarter of 2023, the annual pace of newly completed multi-family homes stood at 385,000 units. Although absorption rates remained elevated in the second quarter, especially at lower price points, the rental vacancy rate ticked up to 6.6% in the third quarter. This uptick in rental vacancy suggests the recent supply has outpaced demand, but context is important. After recent gains, the rental vacancy rate is on par with its level right before the onset of the pandemic in early 2020, still below its 7.2% average from the 2013 to 2019 period.  Looking ahead, the strong construction pipeline– which hit a record high for units under construction this summer –is expected to continue fueling rental supply growth in 2024 pushing rental vacancy back toward its long-run average. 

While the surge in new multi-family supply gives renters options, the sheer number of renters will minimize the potential price impact. The median asking rent in 2024 is expected to drop only slightly below its 2023 level. Renting is expected to continue to be a more budget friendly option than buying in the vast majority of markets, even though home prices and mortgage rates are both expected to dip, helping pull the purchase market down slightly from record unaffordability. 

Young adult renters who lack the benefit of historically high home equity to tap into for a home purchase will continue to find the housing market challenging. Specifically, as many Millennials age past first-time home buying age and more Gen Z approach these years, the current housing landscape is likely to keep these households in the rental market for a longer period as they work to save up more money for the growing down payment needed to buy a first home. This trend is expected to sustain robust demand for rental properties. Consequently, we anticipate that rental markets favored by young adults , a list which includes a mix of affordable areas and tech-heavy job markets in the South, Midwest, and West, will be rental markets to watch in 2024.

Key Wildcards:

• Wildcard 1: Mortgage Rates With both mortgage rates and home prices expected to turn the corner in 2024, record high unaffordability will become a thing of the past, though as noted above, the return to normal won’t be accomplished within the year. This prediction hinges on the expectation that inflation will continue to subside, enabling the recent declines in longer-term interest rates to continue. If inflation were to instead see a surprise resurgence, this aspect of the forecast would change, and home sales could slip lower instead of steadying.
• Wildcard 2: Geopolitics In our forecast for 2023 , we cited the risk of geopolitical instability on trade and energy costs as something to watch. In addition to Russia’s ongoing war in Ukraine, instability in the Middle East has not only had a catastrophic human toll, both conflicts have the potential to impact the economic outlook in ways that cannot be fully anticipated. 
• Wildcard 3: Domestic Politics: 2024 Elections In 2020, amid the upheaval of pandemic-era adaptations, many Americans were on the move. We noted that Realtor.com traffic patterns indicated that home shoppers in very traditionally ‘blue’ or Democratic areas were tending to look for homes in markets where voters have more typically voted ‘red’ or Republican. While consumers also reported preferring to live in locations where their political views align with the majority , few actually reported wanting to move for this reason alone. 

Housing Perspectives:

What will the market be like for homebuyers, especially first-time homebuyers.

First-time homebuyers will continue to face a challenging housing market in 2024, but there are some green shoots. The record-high share of income required to purchase the median priced home is expected to begin to decline as mortgage rates ease, home prices soften, and incomes grow. In 2023 we expect that for the year as a whole, the monthly cost of financing the typical for-sale home will average more than $2,240, a nearly 20% increase over the mortgage payment in 2022, and roughly double the typical payment for buyers in 2020. This amounted to a whopping nearly 37% of the typical household income. In 2024 as modest price declines take hold and mortgage rates dip, the typical purchase cost is expected to slip just under $2,200 which would amount to nearly 35% of income. While far higher than historically average, this is a significant first step in a buyer-friendly direction.

How can homebuyers prepare? 

Homebuyers can prepare for this year’s housing market by getting financially ready. Buyers can use a home affordability calculator , like this one at Realtor.com to translate their income and savings into a home price range. And shoppers can pressure test the results by using a mortgage calculator to consider different down payment, price, and loan scenarios to see how their monthly costs would be impacted. Working with a lender can help potential buyers explore different loan products such as FHA or VA loans that may offer lower mortgage interest rates or more flexible credit criteria. 

Although prices are anticipated to fall in 2024, housing costs remain high, and a down payment can be a big obstacle for buyers. Recent research shows that the typical down payment on a home reached a record high of $30,000 .  To make it easier to cobble together a down payment, shoppers can access information about down payment assistance options at Realtor.com/fairhousing and in the monthly payment section of home listing pages. Furthermore, home shoppers can explore loan products geared toward helping families access homeownership by enabling down payments as low as 3.5% in the case of FHA loans and 0% in the case of VA loans .

What will the market be like for home sellers?

Home sellers are likely to face more competition from builders than from other sellers in 2024. Because builders are continuing to maintain supply and increasingly adapting to market conditions, they are increasingly focused on lower-priced homes and willing to make price adjustments when needed. As a result, potential sellers will want to consider the landscape for new construction housing in their markets and any implications for pricing and marketing before listing their home for sale.

What will the market be like for renters?

In 2024, renting is expected to continue to be a more cost-effective option than buying in the short term even though we anticipate the advantage for renting to diminish as home prices and mortgage rates decline. 

However, for those considering the pursuit of long-term equity through homeownership, it’s essential to not only stay alert about market trends but also to carefully consider the intended duration of residence in their next home. When home prices rise rapidly, like they did during the pandemic, the higher cost of purchasing a home may break even with the cost of renting in as little as 3 years. Generally, it takes longer to reach the breakeven point, typically within a 5 to 7-year timeframe. Importantly, when home prices are falling and rents are also declining, as is expected to be the case in 2024, it can take longer to recoup some of the higher costs of buying a home. Individuals using Realtor.com’s Rent vs. Buy Calculator can thoroughly evaluate the costs and benefits associated with renting versus buying over time and how many years current market trends suggest it will take before buying is the better financial decision. This comprehensive tool can provide insights tailored to a household’s specific rent versus buying decision and empowers consumers to consider not only the optimal choice for the current month but also how the trade-offs evolve over several years.

Local Market Predictions:

All real estate is local and while the national trends are instructive, what matters most is what’s expected in your local market. 

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