google analytics case study pdf

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Google Analytics Performance Marketing Case Studies

When you change the way data is collected and analyzed, you gain insights into your customers and their purchase behaviors. The brands in the section below, including Westwing, Travelocity and PBS, did just that with products such as Google Analytics Premium and Universal Analytics.

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Lenovo: a radically new view of results, accuweather measures holistic analytics with google analytics premium, watchfinder clocks 1,300% roi using precision remarketing with google analytics, westwing uses universal analytics to better understand customers' purchase path, rooms to go improves the shopper experience by integrating google analytics premium.

Using Google Analytics to Understand Online Learning: A Case Study of a Graduate-Level Online Course

• October 2015
• Conference: 2015 International Conference of Educational Innovation through Technology (EITT)

• Central China Normal University
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Omni Hotels boosts conversions 4X by ditching cookies for Display & Video 360’s PAIR

From its roots in grand historic hotels to its collection of modern resort destinations, Omni Hotels & Resorts has been shaping the hospitality landscape for decades. With over 40 locations spanning across North America, Omni has continued to build upon its rich legacy that blends time-honored elegance with personalized experiences, offering guests a taste of genuine luxury. To navigate the privacy-focused landscape, Omni partnered with PMG, MiQ, and LiveRamp, adopting Google's Display & Video 360 Publisher Advertiser Identity Reconciliation (PAIR) solution to deliver relevant ads without compromising user data. This resulted in a remarkable 4X increase in ad conversion rates compared to traditional cookie-based methods, demonstrating success in delivering relevant experiences while respecting user privacy.

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Managing people strategically with people analytics: A case study of Google Inc

As the worldwide economic and political conditions continue to distress global business leaders, their attention has turned to the various levers that can help them foster success in undefined times. McKinsey once declared that there is a " War for Talent " coming. Globalization and fluctuation in population demographics are triggering competition to grow steadily and persistently among the organizations. But by shifting its focus on human resources an organization can put its strategic focus on people management as the innovations come from people, and it cannot simply maximize its innovations unless HR managers are capable of recruiting and retaining innovators. This has drastically changed the vision of People Management at Google. The basic premise of the " people analytics " approach at Google is that accurate people management decisions are the most important and impactful decisions that a firm can make. Organizations cannot simply produce superior business results unless the managers are making accurate people management decisions. The paper basically studies how Google uses People Analytics to make a strategic management decisions in people management. The paper is based on case study research from the secondary sources and analyzes how the use of analytics has helped Google to become the top business leader in this fierce competitive market. Introduction In today's fast emerging economy, competition has become steeper amongst organizations. Achieving the sustainable competitive edge has become a priority to all the organizations and hence they must align their HR strategy with their overall business goals. HR being the ultimate tool to attain competitive advantage aids every organization to address challenges like staffing & recruitment issue, succession planning, attrition, compensation, performance management, retention problem, training and development and many more. Being confronted and challenged with an emergent need for significant insights into the workforce, corporations are beginning to identify the enormous and unexploited potential in what they previously know, and what they possibly will know about their own people. Large firms are extensively capturing longitudinal data related to HR practices, implementation, leadership, firm strategy, environmental contingencies, etc., with an attempt to build and test more robust models related to the HR-performance relationship. As the search for the one universal model continues more focus is being given using data which will be designed to have a better understanding of contingency or configurational models of the HR-performance linkage. Managing people is one of the biggest challenges of the HR department, but with emergence of new IT technologies like People Analytics or HR Analytics software has touted to be the panacea of the various dilemmas of Human Resource Managers.

Related Papers

Mohamed meri Meri

The topic of HRM at the level of predictive in modern organizations is very important and interesting in contemporary researches and practices, especially in Modern Organizations in developed countries. Currently, HRM has exceeded the descriptive traditional stage as (management of personnel or management of workers or descriptive HRM) towards the predictive stage based on Metrics and Analytics, which is why it attracts the attention of concerned academics and practitioners. This research deals with the topic by using HRM at the level of Analytics and Metrics in modern organizations through reviewing (possibilities, application, and successes). It shows the Analytics and Metrics as an effective administrative tools to develop strategies and executive plans, taking into consideration the overall work of the organizational system to achieve effective performance regarding the great importance of using predictive Models in all the management levels of organization as a whole and HRM in particular. It presents how to design and formulate HR Analytics and Metrics, their models in modern organizations, the levels of application at work, and the benefits achieved in organizations that live in the context of globalization. Also, this research shows the options offered by HR Analytics and Metrics to contemporary organizations in order to follow the correct approaches in modern management. The methodology followed in this research is to review several studies in the field of predictive HRM, and analysis data's and the results achieved in developed and emerging countries to clarify the relationship between high performance and HR Analytics or Metrics in modern organizations. Finally, this research presents a practical model for HR analytics and Metrics in order to be used by modern and efficient organizations.

Journal of Business and Economics, ISSN 2155-7950, USA

The topic of HRM at the level of predictive in modern organizations is very important and interesting in contemporary researches and practices, especially in Modern Organizations in developed countries. Currently, HRM has exceeded the descriptive traditional stage as (management of personnel or management of workers or descriptive HRM) towards the predictive stage based on Metrics and Analytics, that is why it attracts the attention of concerned academics and practitioners. This research deals with the topic by using HRM at the level of Analytics and Metrics in modern organizations through reviewing (possibilities, application, and successes). It shows the Analytics and Metrics as an effective administrative tools to develop strategies and executive plans, taking into consideration the overall work of the organizational system to achieve effective performance regarding the great importance of using predictive Models in all the management levels of organization as a whole and HRM in particular. It presents how to design and formulate HR Analytics and Metrics, their models in modern organizations, the levels of application at work, and the benefits achieved in organizations that live in the context of globalization. Also, this research shows the options offered by HR Analytics and Metrics to contemporary organizations in order to follow the correct approaches in modern management. The methodology followed in this research is to review several studies in the field of predictive HRM, and analysis data’s the and results achieved in developed and emerging countries to clarify the relationship between high performance and HR Analytics or Metrics in modern organizations. Finally, this research presents a practical model for HR analytics and Metrics in order to be used by modern and efficient organizations

IJESRT Journal

The HR analytics has emerged as a new fad for the management leaders that captured the head line for business world news. This paper tries to figure out the theoretical and conceptual the framework of analytics in higher education intuitions. The objectives of the study are to understand the role of HR analytics for education institution in increasing the organizational effectiveness and efficiency. The research used exploratory research design. Further study came up with categorizing the data analytics of industry it into Academic Analytics, Operational Analytics and special reference is given to HR analytic as its contribution in attaining the business excellence for the organizations.

Anupam Basu

Iveta Ludviga

Since the early 2000s, there is increasing pressure on Human Resource Departments to show their impact on organizational performance. This pressure is related to the shift from industrial based economies to knowledge based economies and positioning people as potential sources of competitive advantage, and to the rise of Evidence-Based Management (EBM), which requires making decisions based on data and analysis. New technologies have enabled HR departments to start a transition from HR metrics to HR Analytics, thus transforming from the traditional administrative HR function to a more strategic HR function that can express qualitative matters and its impact on organizational performance with numbers. This case study describes the implementation of HR analytics in an aviation sector company. Quantitative data gathered from an annual staff engagement survey are analyzed using a Structural Equation Modelling technique with Smart PLS software. The results show that the analysis offers insights which are much more valuable than traditional diagnosis of the level of employee engagement. Thus, management can trace an employee's journey within the organization and be able to predict their behavior in relationship to the time spent in the organization. Moreover, the changing needs of employees are seen form the analysis and Evidence-Based Management can be implemented.

Journal ijmr.net.in(UGC Approved)

HR analytics is most sought after word in the business world, though the practice of human resources analytics dates as far back as1984. It is evidence based approach and it improves individual and organaisational performance by making better decisions on the people side of the business. It enhances effectiveness of HR policies and practices of the organisation, helps in identifying where efforts, resources and budgets are not producing their intended impacts. It is a frame work that concerns data inputs about the employee and his/her role in the organisation to a set of outcomes or metrics that the organisation is interested in. In this study researchers tried to figure out the significance of HR analytics in People Management.

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Domino’s: Increasing monthly revenue by 6% with Google Analytics Premium, Google Tag Manager, and Google BigQuery

About Domino’s

Pizza seller and digital innovator, Domino’s is the leading pizza delivery chain in the UK and the Republic of Ireland. It is also the master franchise in Germany and Switzerland. UK Headquarters: Milton Keynes, England.

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DBi marketing technology and data experts enable brands to leverage analytics to drive business performance. DBi is a Google Analytics Certified Partner and a Google Analytics Premium Authorized Reseller. Headquarters: London, England. Learn more at www.dbi.io .

Google Cloud Platform results

• Realizes an immediate 6% increase in monthly revenue
• Saves 80% YOY in ad serving and operations costs
• Increases agility with streamlined tag management
• Obtains easy access to powerful reporting and customized dashboards

Well-known pizza purveyor Domino’s is dominating pizza delivery sales in countries around the world. Today, Domino’s is the most popular pizza delivery chain operating in the UK, the Republic of Ireland, Germany, and Switzerland — and sales just keep growing.

In these regions in 2014, Domino’s sold 76 million pizzas and generated £766.6 million in revenue — a 14.6% increase from the previous year. In the UK and Ireland, online sales are increasing 30% year-over-year and currently account for almost 70% of all sales. Notably, 44% of those online sales are now made via mobile devices.

Multi-device pizza purchasing delivers fresh data opportunities

Domino’s has been a consistent digital innovator. Much of the pizza purveyor’s success stems from its early investments in strong ecommerce and m-commerce platforms that enable customers to purchase pizzas easily.

Domino’s sold its first pizza online in 1999. It launched an iPhone app in 2010, quickly followed by apps for Android and iPad in 2011, and a Windows app in 2012. By late 2014, Domino’s customers could even order pizzas from their Xboxes. The Domino’s marketing team had assembled a variety of tools to measure marketing performance, keeping pace with the company’s rapid innovations. Unfortunately, dealing with siloed analytics data from channel-focused tools was restricting the team’s ability to gain insights across all devices and channels.

The Domino’s team knew that valuable data insights were waiting just out of reach. To drive success, the team knew they must break down silos, connect datasets, and gain efficient reporting to get a more holistic and actionable view of customer behavior.

Better tag management propels agility across channels

The company's three main goals:

• Integrate marketing measurement across devices
• Connect CRM and digital data to create a holistic view of customer behavior
• Make cross-channel marketing performance analysis easy and efficient

The approach they decided would help them get there:

• Use Google Analytics Premium , Google Tag Manager, and BigQuery to integrate digital data sources and CRM data

Having taken strategic steps in its partnership with DBi, a Google Analytics Premium Authorized Reseller, Domino’s has turned its team goal of unified marketing measurement, holistic insights, and efficient actionability into a day-to-day reality.

For all of this to be possible, DBi leveraged the power of the data layer, a repository of information written into the page code used to store and send information to Google Tag Manager. Because the data layer stays independent of the HTML page structure, it remains consistent when the page content is updated and provides reliable, unchanging data sources for Google Tag Manager containers to pull from.

DBi deployed Google Tag Manager across many of Domino’s apps and websites, setting customized tags for all of the company’s ecommerce tracking and reporting needs. Despite there being a large number of unique containers, data layer consistency makes it easy to duplicate tags and rules — a significant time-saver and error preventor for Domino’s.

Connecting datasets provides holistic customer insights

Next, Domino’s and DBi turned their efforts toward connecting valuable datasets. Although Domino’s had extensive customer data, including demographic information, order frequency, and order method, the company needed to merge this data with digital analytics to enable deeper analysis of consumer behaviors and preferences.

With that goal in mind, DBi developed a custom solution using BigQuery to store and query Domino’s massive datasets in a fast, efficient, and affordable way. Using the BigQuery export feature in Google Analytics Premium, Domino’s can automatically export raw data to a BigQuery project on a daily basis. A secured FTP location and the BigQuery API enable daily automated uploads of CRM data into the BigQuery database on the Google Cloud.

Following the process described above, CRM data became easily merged with Google Analytics digital data via transaction IDs. Because BigQuery can process gigabytes of data in seconds, reporting queries are easy to build and automate. A report examining customer type by marketing channel, for example, reveals which marketing channels or keywords influence customer segments the most.

Google Analytics Premium drives significant results

Since implementing Google Analytics Premium, the ability to access a single Google Analytics account to evaluate web and app performance has made reporting easier and more efficient, and it has furthered the company’s ability to analyze and capture opportunities.

Integrated cross-device tracking has uncovered new insights into customer behavior, allowing the Domino’s marketing team to save 80% year-over-year in ad serving and operations costs.

The new Google Tag Manager implementation allows Domino’s to act fast. Tags can now be created, reviewed, and published in days, rather than having to wait months to catch the next development cycle. In fact, Domino’s used Google Tag Manager to quickly implement an on-site targeting tool that captured and realized an immediate 6% increase in monthly revenue — percentage points that would have been lost each month the project was delayed.

Lastly, connecting CRM data with digital analytics data provided Domino’s with greater visibility into how marketing efforts influence customers. This has enabled the Domino’s marketing team to make better budget allocation decisions and further improve ROI. The team can also customize powerful reports and dashboards to communicate its successes to business stakeholders.

With Google Analytics Premium in place, Domino’s benefits from data-informed decision-making. Going forward, DBi will continue to help Domino’s leverage every ounce of value made possible by Google Analytics Premium. Customized solutions, including Google Tag Manager and BigQuery, drive deeper customer understanding and better marketing strategies.

“Google Analytics Premium, combined with Google Tag Manager and BigQuery, has become an integral solution that gives us the technical agility and the analytics power we need to advance our marketing strategies."

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SomiaNasir/Google-Data-Analytics-Capstone-Cyclistic-Case-Study

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Google data analytics capstone: cyclistic case study.

Course: Google Data Analytics Capstone: Complete a Case Study

Introduction

In this case study, I will perform many real-world tasks of a junior data analyst at a fictional company, Cyclistic. In order to answer the key business questions, I will follow the steps of the data analysis process: Ask , Prepare , Process , Analyze , Share , and Act .

Quick links:

Data Source: divvy_tripdata [accessed on 04/03/23]

SQL Queries: 01. Data Combining 02. Data Exploration 03. Data Cleaning 04. Data Analysis

Data Visualizations: Tableau

A bike-share program that features more than 5,800 bicycles and 600 docking stations. Cyclistic sets itself apart by also offering reclining bikes, hand tricycles, and cargo bikes, making bike-share more inclusive to people with disabilities and riders who can’t use a standard two-wheeled bike. The majority of riders opt for traditional bikes; about 8% of riders use the assistive options. Cyclistic users are more likely to ride for leisure, but about 30% use them to commute to work each day.

Until now, Cyclistic’s marketing strategy relied on building general awareness and appealing to broad consumer segments. One approach that helped make these things possible was the flexibility of its pricing plans: single-ride passes, full-day passes, and annual memberships. Customers who purchase single-ride or full-day passes are referred to as casual riders. Customers who purchase annual memberships are Cyclistic members.

Cyclistic’s finance analysts have concluded that annual members are much more profitable than casual riders. Although the pricing flexibility helps Cyclistic attract more customers, Moreno (the director of marketing and my manager) believes that maximizing the number of annual members will be key to future growth. Rather than creating a marketing campaign that targets all-new customers, Moreno believes there is a very good chance to convert casual riders into members. She notes that casual riders are already aware of the Cyclistic program and have chosen Cyclistic for their mobility needs.

Moreno has set a clear goal: Design marketing strategies aimed at converting casual riders into annual members. In order to do that, however, the marketing analyst team needs to better understand how annual members and casual riders differ, why casual riders would buy a membership, and how digital media could affect their marketing tactics. Moreno and her team are interested in analyzing the Cyclistic historical bike trip data to identify trends.

I am assuming to be a junior data analyst working in the marketing analyst team at Cyclistic, a bike-share company in Chicago. The director of marketing believes the company’s future success depends on maximizing the number of annual memberships. Therefore, my team wants to understand how casual riders and annual members use Cyclistic bikes differently. From these insights, my team will design a new marketing strategy to convert casual riders into annual members. But first, Cyclistic executives must approve our recommendations, so they must be backed up with compelling data insights and professional data visualizations.

Business Task

Devise marketing strategies to convert casual riders to members.

Analysis Questions

Three questions will guide the future marketing program:

• How do annual members and casual riders use Cyclistic bikes differently?
• Why would casual riders buy Cyclistic annual memberships?
• How can Cyclistic use digital media to influence casual riders to become members?

Moreno has assigned me the first question to answer: How do annual members and casual riders use Cyclistic bikes differently?

Data Source

I will use Cyclistic’s historical trip data to analyze and identify trends from Jan 2022 to Dec 2022 which can be downloaded from divvy_tripdata . The data has been made available by Motivate International Inc. under this license .

This is public data that can be used to explore how different customer types are using Cyclistic bikes. But note that data-privacy issues prohibit from using riders’ personally identifiable information. This means that we won’t be able to connect pass purchases to credit card numbers to determine if casual riders live in the Cyclistic service area or if they have purchased multiple single passes.

Data Organization

There are 12 files with naming convention of YYYYMM-divvy-tripdata and each file includes information for one month, such as the ride id, bike type, start time, end time, start station, end station, start location, end location, and whether the rider is a member or not. The corresponding column names are ride_id, rideable_type, started_at, ended_at, start_station_name, start_station_id, end_station_name, end_station_id, start_lat, start_lng, end_lat, end_lng and member_casual.

BigQuery is used to combine the various datasets into one dataset and clean it. Reason: A worksheet can only have 1,048,576 rows in Microsoft Excel because of its inability to manage large amounts of data. Because the Cyclistic dataset has more than 5.6 million rows, it is essential to use a platform like BigQuery that supports huge volumes of data.

Combining the Data

SQL Query: Data Combining 12 csv files are uploaded as tables in the dataset '2022_tripdata'. Another table named "combined_data" is created, containing 5,667,717 rows of data for the entire year.

Data Exploration

SQL Query: Data Exploration Before cleaning the data, I am familiarizing myself with the data to find the inconsistencies.

Observations:

The table below shows the all column names and their data types. The ride_id column is our primary key.

The following table shows number of null values in each column.

Note that some columns have same number of missing values. This may be due to missing information in the same row i.e. station's name and id for the same station and latitude and longitude for the same ending station.

As ride_id has no null values, let's use it to check for duplicates.

There are no duplicate rows in the data.

All ride_id values have length of 16 so no need to clean it.

There are 3 unique types of bikes( rideable_type ) in our data.

The started_at and ended_at shows start and end time of the trip in YYYY-MM-DD hh:mm:ss UTC format. New column ride_length can be created to find the total trip duration. There are 5360 trips which has duration longer than a day and 122283 trips having less than a minute duration or having end time earlier than start time so need to remove them. Other columns day_of_week and month can also be helpful in analysis of trips at different times in a year.

Total of 833064 rows have both start_station_name and start_station_id missing which needs to be removed.

Total of 892742 rows have both end_station_name and end_station_id missing which needs to be removed.

Total of 5858 rows have both end_lat and end_lng missing which needs to be removed.

member_casual column has 2 uniqued values as member or casual rider.

Columns that need to be removed are start_station_id and end_station_id as they do not add value to analysis of our current problem. Longitude and latitude location columns may not be used in analysis but can be used to visualise a map.

Data Cleaning

SQL Query: Data Cleaning

• All the rows having missing values are deleted.
• 3 more columns ride_length for duration of the trip, day_of_week and month are added.
• Trips with duration less than a minute and longer than a day are excluded.
• Total 1,375,912 rows are removed in this step.

Analyze and Share

SQL Query: Data Analysis Data Visualization: Tableau The data is stored appropriately and is now prepared for analysis. I queried multiple relevant tables for the analysis and visualized them in Tableau. The analysis question is: How do annual members and casual riders use Cyclistic bikes differently?

First of all, member and casual riders are compared by the type of bikes they are using.

The members make 59.7% of the total while remaining 40.3% constitutes casual riders. Each bike type chart shows percentage from the total. Most used bike is classic bike followed by the electric bike. Docked bikes are used the least by only casual riders.

Next the number of trips distributed by the months, days of the week and hours of the day are examined.

Months: When it comes to monthly trips, both casual and members exhibit comparable behavior, with more trips in the spring and summer and fewer in the winter. The gap between casuals and members is closest in the month of july in summmer. Days of Week: When the days of the week are compared, it is discovered that casual riders make more journeys on the weekends while members show a decline over the weekend in contrast to the other days of the week. Hours of the Day: The members shows 2 peaks throughout the day in terms of number of trips. One is early in the morning at around 6 am to 8 am and other is in the evening at around 4 pm to 8 pm while number of trips for casual riders increase consistently over the day till evening and then decrease afterwards.

We can infer from the previous observations that member may be using bikes for commuting to and from the work in the week days while casual riders are using bikes throughout the day, more frequently over the weekends for leisure purposes. Both are most active in summer and spring.

Ride duration of the trips are compared to find the differences in the behavior of casual and member riders.

Take note that casual riders tend to cycle longer than members do on average. The length of the average journey for members doesn't change throughout the year, week, or day. However, there are variations in how long casual riders cycle. In the spring and summer, on weekends, and from 10 am to 2 pm during the day, they travel greater distances. Between five and eight in the morning, they have brief trips.

These findings lead to the conclusion that casual commuters travel longer (approximately 2x more) but less frequently than members. They make longer journeys on weekends and during the day outside of commuting hours and in spring and summer season, so they might be doing so for recreation purposes.

To further understand the differences in casual and member riders, locations of starting and ending stations can be analysed. Stations with the most trips are considered using filters to draw out the following conclusions.

Casual riders have frequently started their trips from the stations in vicinity of museums, parks, beach, harbor points and aquarium while members have begun their journeys from stations close to universities, residential areas, restaurants, hospitals, grocery stores, theatre, schools, banks, factories, train stations, parks and plazas.

Similar trend can be observed in ending station locations. Casual riders end their journay near parks, museums and other recreational sites whereas members end their trips close to universities, residential and commmercial areas. So this proves that casual riders use bikes for leisure activities while members extensively rely on them for daily commute.

After identifying the differences between casual and member riders, marketing strategies to target casual riders can be developed to persuade them to become members. Recommendations:

• Marketing campaigns might be conducted in spring and summer at tourist/recreational locations popular among casual riders.
• Casual riders are most active on weekends and during the summer and spring, thus they may be offered seasonal or weekend-only memberships.
• Casual riders use their bikes for longer durations than members. Offering discounts for longer rides may incentivize casual riders and entice members to ride for longer periods of time.

From Productivity to Turnover: How 15 Real Companies Gained a Workforce Edge

Learn how 15 companies use people analytics to solve workforce challenges like turnover, manager effectiveness, and more.

Organizations are being forced to adapt faster than ever. Cost volatility, skills shortages, and the arrival of AI are squeezing margins and challenging traditional operating models. What isn’t changing? People.

Despite the continuously evolving world of work, people—the workforce—are any organization’s most valuable asset and are the source of competitive advantage and business growth. Organizations that use people analytics successfully connect the dots between people, work, and outcomes.

In this ebook, we’re highlighting 15 examples of how real companies use people analytics to solve workforce challenges like low productivity, high turnover, and high hiring costs. Each case study reveals how organizations—from financial services to healthcare—used data-driven insights to make impactful changes, improving both employee experience and business outcomes.

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Comparative Analysis of Google Sheets and Microsoft Excel: : A Discussion Case in Spreadsheet Software

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Applied computing

Computers in other domains

Personal computers and PC applications

Spreadsheets

Enterprise computing

Business process management

Enterprise information systems

Enterprise applications

Information systems

Information systems applications

Social and professional topics

Professional topics

Computing and business

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Microsoft excel functions and formulas, microsoft excel 2017: an easy guide to learning the basics, microsoft office excel 2007 brief, information, published in.

United States

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• Spreadsheet Software
• Microsoft Excel
• Google Sheets
• Business Operations
• Collaboration
• Resource Allocation
• Client Management

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