machine learning case study on harley davidson

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How Harley-Davidson Used Artificial Intelligence to Increase New York Sales Leads by 2,930%

And it led to more revenue and more jobs.

It was winter in New York City, and Asaf Jacobi’s Harley-Davidson dealership was selling only one or two motorcycles a week. It wasn’t enough. A chance meeting led Jacobi to try out a new AI-driven marketing platform that autonomously optimizes marketing campaigns. That weekend Jacobi sold 15 motorcycles — almost twice his all-time summer weekend sales record. How? By analyzing existing customer data to isolate the defining characteristics and behaviors of high-value customers, the system identified “lookalikes” who resembled these past customers, and then used these lookalikes to test new marketing campaigns. The test results helped the algorithm predict which ad copy and visuals would resonate with the customers most likely to buy; for example, an email urging customers to “Call now” outperformed an email asking them to “Buy now” by 447%. Once it determined what was working and what wasn’t, the system autonomously chose and scaled the most effective marketing campaigns — and Jacobi’s business tripled. The spike in demand left him with a new dilemma: how to hire a bunch of new employees, fast.

It was winter in New York City and Asaf Jacobi’s Harley-Davidson dealership was selling one or two motorcycles a week. It wasn’t enough.

• Brad Power is a consultant who helps organizations that must make faster changes to their products, services, and systems in order to compete with start-ups and leading software companies.

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Machine Learning Case Studies with Powerful Insights

Explore the potential of machine learning through these practical machine learning case studies and success stories in various industries. | ProjectPro

Machine learning is revolutionizing how different industries function, from healthcare to finance to transportation. If you're curious about how this technology is applied in real-world scenarios, look no further. In this blog, we'll explore some exciting machine learning case studies that showcase the potential of this powerful emerging technology.

Machine-learning-based applications have quickly transformed work methods in the technological world. It is changing the way we work, live, and interact with the world around us. Machine learning is revolutionizing industries, from personalized recommendations on streaming platforms to self-driving cars.

But while the technology of artificial intelligence and machine learning may seem abstract or daunting to some, its applications are incredibly tangible and impactful. Data Scientists use machine learning algorithms to predict equipment failures in manufacturing, improve cancer diagnoses in healthcare , and even detect fraudulent activity in 5 . If you're interested in learning more about how machine learning is applied in real-world scenarios, you are on the right page. This blog will explore in depth how machine learning applications are used for solving real-world problems.

We'll start with a few case studies from GitHub that examine how machine learning is being used by businesses to retain their customers and improve customer satisfaction. We'll also look at how machine learning is being used with the help of Python programming language to detect and prevent fraud in the financial sector and how it can save companies millions of dollars in losses. Next, we will examine how top companies use machine learning to solve various business problems. Additionally, we'll explore how machine learning is used in the healthcare industry, and how this technology can improve patient outcomes and save lives.

By going through these case studies, you will better understand how machine learning is transforming work across different industries. So, let's get started!

Table of Contents

Machine learning case studies on github, machine learning case studies in python, company-specific machine learning case studies, machine learning case studies in biology and healthcare, aws machine learning case studies , azure machine learning case studies, how to prepare for machine learning case studies interview.

This section has machine learning case studies along with their GitHub repository that contains the sample code.

1. Customer Churn Prediction

Predicting customer churn is essential for businesses interested in retaining customers and maximizing their profits. By leveraging historical customer data, machine learning algorithms can identify patterns and factors that are correlated with churn, enabling businesses to take proactive steps to prevent it.

In this case study, you will study how a telecom company uses machine learning for customer churn prediction. The available data contains information about the services each customer signed up for, their contact information, monthly charges, and their demographics. The goal is to first analyze the data at hand with the help of methods used in Exploratory Data Analysis . It will assist in picking a suitable machine-learning algorithm. The five machine learning models used in this case-study are AdaBoost, Gradient Boost, Random Forest, Support Vector Machines, and K-Nearest Neighbors. These models are used to determine which customers are at risk of churn. 

By using machine learning for churn prediction, businesses can better understand customer behavior, identify areas for improvement, and implement targeted retention strategies. It can result in increased customer loyalty, higher revenue, and a better understanding of customer needs and preferences. This case study example will help you understand how machine learning is a valuable tool for any business looking to improve customer retention and stay ahead of the competition.

GitHub Repository: https://github.com/Pradnya1208/Telecom-Customer-Churn-prediction  

2. Market Basket Analysis

Market basket analysis is a common application of machine learning in retail and e-commerce, where it is used to identify patterns and relationships between products that are frequently purchased together. By leveraging this information, businesses can make informed decisions about product placement, promotions, and pricing strategies.

In this case study, you will utilize the EDA methods to carefully analyze the relationships among different variables in the data. Next, you will study how to use the Apriori algorithm to identify frequent itemsets and association rules, which describe the likelihood of a product being purchased given the presence of another product. These rules can generate recommendations, optimize product placement, and increase sales, and they can also be used for customer segmentation.  

Using machine learning for market basket analysis allows businesses to understand customer behavior better, identify cross-selling opportunities, and increase customer satisfaction. It has the potential to result in increased revenue, improved customer loyalty, and a better understanding of customer needs and preferences. 

GitHub Repository: https://github.com/kkrusere/Market-Basket-Analysis-on-the-Online-Retail-Data

3. Predicting Prices for Airbnb

Airbnb is a tech company that enables hosts to rent out their homes, apartments, or rooms to guests interested in temporary lodging. One of the key challenges hosts face is optimizing the rent prices for the customers. With the help of machine learning, hosts can have rough estimates of the rental costs based on various factors such as location, property type, amenities, and availability.

The first step, in this case study, is to clean the dataset to handle missing values, duplicates, and outliers. In the same step, the data is transformed, and the data is prepared for modeling with the help of feature engineering methods. The next step is to perform EDA to understand how the rental listings are spread across different cities in the US. Next, you will learn how to visualize how prices change over time, looking at trends for different seasons, months, days of the week, and times of the day.

The final step involves implementing ML models like linear regression (ridge and lasso), Naive Bayes, and Random Forests to produce price estimates for listings. You will learn how to compare the outcome of these models and evaluate their performance.

GitHub Repository: https://github.com/samuelklam/airbnb-pricing-prediction  

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4. Titanic Disaster Analysis

The Titanic Machine Learning Case Study is a classic example in the field of data science and machine learning. The study is based on the dataset of passengers aboard the Titanic when it sank in 1912. The study's goal is to predict whether a passenger survived or not based on their demographic and other information.

The dataset contains information on 891 passengers, including their age, gender, ticket class, fare paid, as well as whether or not they survived the disaster. The first step in the analysis is to explore the dataset and identify any missing values or outliers. Once this is done, the data is preprocessed to prepare it for modeling.

The next step is to build a predictive model using various machine learning algorithms, such as logistic regression, decision trees, and random forests. These models are trained on a subset of the data and evaluated on another subset to ensure they can generalize well to new data.

Finally, the model is used to make predictions on a test dataset, and the model performance is measured using various metrics such as accuracy, precision, and recall. The study results can be used to improve safety protocols and inform future disaster response efforts.

GitHub Repository: https://github.com/ashishpatel26/Titanic-Machine-Learning-from-Disaster  

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If you are looking for a sample of machine learning case study in python, then keep reading this space.

5. Loan Application Classification

Financial institutions receive tons of requests for lending money by borrowers and making decisions for each request is a crucial task. Manually processing these requests can be a time-consuming and error-prone process, so there is an increasing demand for machine learning to improve this process by automation.

You can work on this Loan Dataset on Kaggle to get started on this one of the most real-world case studies in the financial industry. The dataset contains 614 unique values for 13 columns: Follow the below-mentioned steps to get started on this case study.

Analyze the dataset and explore how various factors such as gender, marital status, and employment affect the loan amount and status of the loan application .

Select the features to automate the process of classification of loan applications.

Apply machine learning models such as logistic regression, decision trees, and random forests to the features and compare their performance using statistical metrics.

This case study falls under the umbrella of supervised learning problems in machine learning and demonstrates how ML models are used to automate tasks in the financial industry.

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6. Computer Price Estimation

Whenever one thinks of buying a new computer, the first thing that comes to mind is to curate a list of hardware specifications that best suit their needs. The next step is browsing different websites and looking for the cheapest option available. Performing all these processes can be time-consuming and require a lot of effort. But you don’t have to worry as machine learning can help you build a system that can estimate the price of a computer system by taking into account its various features.

This sample basic computer dataset on Kaggle can help you develop a price estimation model that can analyze historical data and identify patterns and trends in the relationship between computer specifications and prices. By training a machine learning model on this data, the model can learn to make accurate predictions of prices for new or unseen computer components. Machine learning algorithms such as K-Nearest Neighbours, Decision Trees, Random Forests, ADA Boost and XGBoost can effectively capture complex relationships between features and prices, leading to more accurate price estimates. 

Besides saving time and effort compared to manual estimation methods, this project also has a business use case as it can provide stakeholders with valuable insights into market trends and consumer preferences.

7. House Price Prediction

Here is a machine learning case study that aims to predict the median value of owner-occupied homes in Boston suburbs based on various features such as crime rate, number of rooms, and pupil-teacher ratio.

Start working on this study by collecting the data from the publicly available UCI Machine Learning Repository, which contains information about 506 neighborhoods in the Boston area. The dataset includes 13 features such as per capita crime rate, average number of rooms per dwelling, and the proportion of owner-occupied units built before 1940. You can gain more insights into this data by using EDA techniques. Then prepare the dataset for implementing ML models by handling missing values, converting categorical features to numerical ones, and scaling the data.

Use machine learning algorithms such as Linear Regression, Lasso Regression, and Random Forest to predict house prices for different neighborhoods in the Boston area. Select the best model by comparing the performance of each one using metrics such as mean squared error, mean absolute error, and R-squared.

This section has machine learning case studies of different firms across various industries.

8. Machine Learning Case Study on Dell

Dell Technologies is a multinational technology company that designs, develops, and sells computers, servers, data storage devices, network switches, software, and other technology products and services. Dell is one of the world's most prominent PC vendors and serves customers in over 180 countries. As Data is an integral component of Dell's hard drive, the marketing team of Dell required a data-focused solution that would improve response rates and demonstrate why some words and phrases are more effective than others.

Dell contacted Persado and partnered with the firm that utilizes AI to create marketing content. Persado helped Dell revamp the email marketing strategy and leverage the data analytics to garner their audiences' attention. The statistics revealed that the partnership resulted in a noticeable increase in customer engagement as the page visits by 22% on average and a 50% average increase in CTR.

Dell currently relies on ML methods to improve their marketing strategy for emails, banners, direct mail, Facebook ads, and radio content.

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9. Machine Learning Case Study on Harley Davidson

In the current environment, it is challenging to overcome traditional marketing. An artificial intelligence powered robot, Albert is appealing for a business like Harley Davidson. Robots are now directing traffic, creating news stories, working in hotels, and even running McDonald's, thanks to machine learning and artificial intelligence.

There are many marketing channels that Albert can be applied to, including Email and social media.It automatically prepares customized creative copies and forecasts which customers will most likely convert.

The only company to make use of Albert is Harley Davidson. The business examined customer data to ascertain the activities of past clients who successfully made purchases and invested more time than usual across different pages on the website. With this knowledge, Albert divided the customer base into groups and adjusted the scale of test campaigns accordingly.

Results reveal that using Albert increased Harley Davidson's sales by 40%. The brand also saw a 2,930% spike in leads, 50% of which came from very effective "lookalikes" found by machine learning and artificial intelligence.

10. Machine Learning Case Study on Zomato

Zomato is a popular online platform that provides restaurant search and discovery services, online ordering and delivery, and customer reviews and ratings. Founded in India in 2008, the company has expanded to over 24 countries and serves millions of users globally. Over the years, it has become a popular choice for consumers to browse the ratings of different restaurants in their area. 

To provide the best restaurant options to their customers, Zomato ensures to hand-pick the ones likely to perform well in the future. Machine Learning can help zomato in making such decisions by considering the different restaurant features. You can work on this sample Zomato Restaurants Data and experiment with how machine learning can be useful to Zomato. The dataset has the details of 9551 restaurants. The first step should involve careful analysis of the data and identifying outliers and missing values in the dataset. Treat them using statistical methods and then use regression models to predict the rating of different restaurants.

The Zomato Case study is one of the most popular machine learning startup case studies among data science enthusiasts.

11. Machine Learning Case Study on Tesla

Tesla, Inc. is an American electric vehicle and clean energy company founded in 2003 by Elon Musk. The company designs, manufactures, and sells electric cars, battery storage systems, and solar products. Tesla has pioneered the electric vehicle industry and has popularized high-capacity lithium-ion batteries and regenerative braking systems. The company strongly focuses on innovation, sustainability, and reducing the world's dependence on fossil fuels.

Tesla uses machine learning in various ways to enhance the performance and features of its electric vehicles. One of the most notable applications of machine learning at Tesla is in its Autopilot system, which uses a combination of cameras, sensors, and machine learning algorithms to enable advanced driver assistance features such as lane centering, adaptive cruise control, and automatic emergency braking.

Tesla's Autopilot system uses deep neural networks to process large amounts of real-world driving data and accurately predict driving behavior and potential hazards. It enables the system to learn and adapt over time, improving its accuracy and responsiveness.

Additionally, Tesla also uses machine learning in its battery management systems to optimize the performance and longevity of its batteries. Machine learning algorithms are used to model and predict the behavior of the batteries under different conditions, enabling Tesla to optimize charging rates, temperature control, and other factors to maximize the lifespan and performance of its batteries.

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12. Machine Learning Case Study on Amazon

Amazon Prime Video uses machine learning to ensure high video quality for its users. The company has developed a system that analyzes video content and applies various techniques to enhance the viewing experience.

The system uses machine learning algorithms to automatically detect and correct issues such as unexpected black frames, blocky frames, and audio noise. For detecting block corruption, residual neural networks are used. After training the algorithm on the large dataset, a threshold of 0.07 was set for the corrupted-area ratio to mark the areas of the frame that have block corruption. For detecting unwanted noise in the audio, a model based on a pre-trained audio neural network is used to classify a one-second audio sample into one of these classes: audio hum, audio distortion, audio diss, audio clicks, and no defect. The lip sync is handled using the SynNet architecture.

By using machine learning to optimize video quality, Amazon can deliver a consistent and high-quality viewing experience to its users, regardless of the device or network conditions they are using. It helps maintain customer satisfaction and loyalty and ensures that Amazon remains a competitive video streaming market leader.

Machine Learning applications are not only limited to financial and tech use cases. It also finds its use in the Healthcare industry. So, here are a few machine learning case studies that showcase the use of this technology in the Biology and Healthcare domain.

13. Microbiome Therapeutics Development

The development of microbiome therapeutics involves the study of the interactions between the human microbiome and various diseases and identifying specific microbial strains or compositions that can be used to treat or prevent these diseases. Machine learning plays a crucial role in this process by enabling the analysis of large, complex datasets and identifying patterns and correlations that would be difficult or impossible to detect through traditional methods.

Machine learning algorithms can analyze microbiome data at various levels, including taxonomic composition, functional pathways, and gene expression profiles. These algorithms can identify specific microbial strains or communities associated with different diseases or conditions and can be used to develop targeted therapies.

Besides that, machine learning can be used to optimize the design and delivery of microbiome therapeutics. For example, machine learning algorithms can be used to predict the efficacy of different microbial strains or compositions and optimize these therapies' dosage and delivery mechanisms.

14. Mental Illness Diagnosis

Machine learning is increasingly being used to develop predictive models for diagnosing and managing mental illness. One of the critical advantages of machine learning in this context is its ability to analyze large, complex datasets and identify patterns and correlations that would be difficult for human experts to detect.

Machine learning algorithms can be trained on various data sources, including clinical assessments, self-reported symptoms, and physiological measures such as brain imaging or heart rate variability. These algorithms can then be used to develop predictive models to identify individuals at high risk of developing a mental illness or who are likely to experience a particular symptom or condition.

One example of machine learning being used to predict mental illness is in the development of suicide risk assessment tools. These tools use machine learning algorithms to analyze various risk factors, such as demographic information, medical history, and social media activity, to identify individuals at risk of suicide. These tools can be used to guide early intervention and support for individuals struggling with mental health issues.

One can also a build a Chatbot using Machine learning and Natural Lanaguage Processing that can analyze the responses of the user and recommend them the necessary steps that they can immediately take.

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15. 3D Bioprinting

Another popular subject in the biotechnology industry is Bioprinting. Based on a computerized blueprint, the printer prints biological tissues like skin, organs, blood arteries, and bones layer by layer using cells and biomaterials, also known as bioinks.

They can be made in printers more ethically and economically than by relying on organ donations. Additionally, synthetic construct tissue is used for drug testing instead of testing on animals or people. Due to its tremendous complexity, the entire technology is still in its early stages of maturity. Data science is one of the most essential components to handle this complexity of printing.

The qualities of the bioinks, which have inherent variability, or the many printing parameters, are just a couple of the many variables that affect the printing process and quality. For instance, Bayesian optimization improves the likelihood of producing useable output and optimizes the printing process.

A crucial element of the procedure is the printing speed. To estimate the optimal speed, siamese network models are used. Convolutional neural networks are applied to photographs of the layer-by-layer tissue to detect material, or tissue abnormalities.

In this section, you will find a list of machine learning case studies that have utilized Amazon Web Services to create machine learning based solutions.

16. Machine Learning Case Study on AutoDesk

Autodesk is a US-based software company that provides solutions for 3D design, engineering, and entertainment industries. The company offers a wide range of software products and services, including computer-aided design (CAD) software, 3D animation software, and other tools used in architecture, construction, engineering, manufacturing, media and entertainment industries.

Autodesk utilizes machine learning (ML) models that are constructed on Amazon SageMaker, a managed ML service provided by Amazon Web Services (AWS), to assist designers in categorizing and sifting through a multitude of versions created by generative design procedures and selecting the most optimal design.  ML techniques built with Amazon SageMaker help Autodesk progress from intuitive design to exploring the boundaries of generative design for their customers to produce innovative products that can even be life-changing. As an example, Edera Safety, a design studio located in Austria, created a superior and more effective spine protector by utilizing Autodesk's generative design process constructed on AWS.

17. Machine Learning Case Study on Capital One

Capital One is a financial services company in the United States that offers a range of financial products and services to consumers, small businesses, and commercial clients. The company provides credit cards, loans, savings and checking accounts, investment services, and other financial products and services.

Capital One leverages AWS to transform data into valuable insights using machine learning, enabling the company to innovate rapidly on behalf of its customers.  To power its machine-learning innovation, Capital One utilizes a range of AWS services such as Amazon Elastic Compute Cloud (Amazon EC2), Amazon Relational Database Service (Amazon RDS), and AWS Lambda. AWS is enabling Capital One to implement flexible DevOps processes, enabling the company to introduce new products and features to the market in just a few weeks instead of several months or years. Additionally, AWS assists Capital One in providing data to and facilitating the training of sophisticated machine-learning analysis and customer-service solutions. The company also integrates its contact centers with its CRM and other critical systems, while simultaneously attracting promising entry-level and mid-career developers and engineers with the opportunity to gain knowledge and innovate with the most up-to-date cloud technologies.

18. Machine Learning Case Study on BuildFax

In 2008, BuildFax began by collecting widely scattered building permit data from different parts of the United States and distributing it to various businesses, including building inspectors, insurance companies, and economic analysts. Today, it offers custom-made solutions to these professions and several other services. These services comprise indices that monitor trends like commercial construction, and housing remodels.

Source: aws.amazon.com/solutions/case-studies

The primary customer base of BuildFax is insurance companies that splurge billion dollars on rood losses. BuildFax assists its customers in developing policies and premiums by evaluating the roof losses for them. Initially, it relied on general data and ZIP codes for building predictive models but they did not prove to be useful as they were not accurate and were slightly complex in nature. It thus required a way out of building a solution that could support more accurate results for property-specific estimates. It thus chose Amazon Machine Learning for predictive modeling. By employing Amazon Machine Learning, it is possible for the company to offer insurance companies and builders personalized estimations of roof-age and job-cost, which are specific to a particular property and it does not have to depend on more generalized estimates based on ZIP codes.  It now utilizes customers' data and data from public sources to create predictive models.

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This section will present you with a list of machine learning case studies that showcase how companies have leveraged Microsoft Azure Services for completing machine learning tasks in their firm.

19. Machine Learning Case Study for an Enterprise Company

Consider a company (Azure customer) in the Electronic Design Automation industry that provides software, hardware, and IP for electronic systems and semiconductor companies. Their finance team was struggling to manage account receivables efficiently, so they wanted to use machine learning to predict payment outcomes and reduce outstanding receivables. The team faced a major challenge with managing change data capture using Azure Data Factory . A3S provided a solution by automating data migration from SAP ECC to Azure Synapse and offering fully automated analytics as a service, which helped the company streamline their account receivables management. It was able to achieve the entire scenario from data ingestion to analytics within a week, and they plan to use A3S for other analytics initiatives.

20. Machine Learning Case Study on Shell

Royal Dutch Shell, a global company managing oil wells to retail petrol stations, is using computer vision technology to automate safety checks at its service stations. In partnership with Microsoft, it has developed the project called Video Analytics for Downstream Retail (VADR) that uses machine vision and image processing to detect dangerous behavior and alert the servicemen. It uses OpenCV and Azure Databricks in the background highlighting how Azure can be used for personalised applications. Once the projects shows decent results in the countries where it has been deployed (Thailand and Singapore), Shell plans to expand the project further by going global with the VADR project. 

21. Machine Learning Case Study on TransLink

TransLink, a transportation company in Vancouver, deployed 18,000 different sets of machine learning models using Azure Machine Learning to predict bus departure times and determine bus crowdedness. The models take into account factors such as traffic, bad weather and at-capacity buses. The deployment led to an improvement in predicted bus departure times of 74%. The company also created a mobile app that allows people to plan their trips based on how at-capacity a bus might be at different times of day.

22. Machine Learning Case Study on XBox

Microsoft Azure Personaliser is a cloud-based service that uses reinforcement learning to select the best content for customers based on up-to-date information about them, the context, and the application. Custom recommender services can also be created using Azure Machine Learning. The Xbox One group used Cognitive Services Personaliser to find content suited to each user, which resulted in a 40% increase in user engagement compared to a random personalisation policy on the Xbox platform.

All the mentioned case studies in this blog will help you explore the application of machine learning in solving real problems across different industries. But you must not stop after working on them if you are preparing for an interview and intend to showcase that you have mastered the art of implementing ML algorithms, and you must practice more such caste studies in machine learning.

And if you have decided to dive deeper into machine learning, data science, and big data, be sure to check out ProjectPro , which offers a repository of solved projects in data science and big data. With a wide range of projects, you can explore different techniques and approaches and build your machine learning and data science skills . Our repository has a project for each one of you, irrespective of your academic and professional background. The customer-specific learning path is likely to help you find your way to making a mark in this newly emerging field. So why wait? Start exploring today and see what you can accomplish with big data and data science ! 

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1. What is a case study in machine learning?

A case study in machine learning is an in-depth analysis of a real-world problem or scenario, where machine learning techniques are applied to solve the problem or provide insights. Case studies can provide valuable insights into the application of machine learning and can be used as a basis for further research or development.

2. What is a good use case for machine learning?

A good use case for machine learning is any scenario with a large and complex dataset and where there is a need to identify patterns, predict outcomes, or automate decision-making based on that data. It could include fraud detection, predictive maintenance, recommendation systems, and image or speech recognition, among others.

3. What are the 3 basic types of machine learning problems?

The three basic types of machine learning problems are supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the algorithm is trained on labeled data. In unsupervised learning, the algorithm seeks to identify patterns in unstructured data. In reinforcement learning, the algorithm learns through trial and error based on feedback from the environment.

4. What are the 4 basics of machine learning?

The four basics of machine learning are data preparation, model selection, model training, and model evaluation. Data preparation involves collecting, cleaning, and preparing data for use in training models. Model selection involves choosing the appropriate algorithm for a given task. Model training involves optimizing the chosen algorithm to achieve the desired outcome. Model evaluation consists of assessing the performance of the trained model on new data.

About the Author

Manika Nagpal is a versatile professional with a strong background in both Physics and Data Science. As a Senior Analyst at ProjectPro, she leverages her expertise in data science and writing to create engaging and insightful blogs that help businesses and individuals stay up-to-date with the

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Künstliche Intelligenz in Unternehmen: Innovative Anwendungen in 50 erfolgreichen Firmen

Der Bestsellerautor und Geschäfts renommierter KI-Experte Bernard zeigt, wie sterben Technologie des maschinellen Lernens das von Unternehmen verändert. Das Buch bietet einen Überblick über einzelne Unternehmen, beschreibt das spezifische Problem und erklärt, wie KI die Lösung erleichtert. Jede Fallstudie bietet einen umfassenden Einblick, der einige technische Details wichtige Lernzusammenfassungen enthält. Marrs Buch ist eine aufschlussreiche und informative Untersuchung der transformativen Kraft der Technologie in der Wirtschaft des 21. Jahrhunderts.

Bernard Marr

Bernard Marr is a world-renowned futurist, influencer and thought leader in the fields of business and technology, with a passion for using technology for the good of humanity. He is a best-selling author of over 20 books, writes a regular column for Forbes and advises and coaches many of the world’s best-known organisations. He has a combined following of 4 million people across his social media channels and newsletters and was ranked by LinkedIn as one of the top 5 business influencers in the world.

Bernard’s latest books are ‘Future Skills’, ‘The Future Internet’, ‘Business Trends in Practice’ and ‘ Generative AI in Practice ’.

Bernard Marr ist ein weltbekannter Futurist, Influencer und Vordenker in den Bereichen Wirtschaft und Technologie mit einer Leidenschaft für den Einsatz von Technologie zum Wohle der Menschheit. Er ist Bestsellerautor von 20 Büchern, schreibt eine regelmäßige Kolumne für Forbes und berät und coacht viele der weltweit bekanntesten Organisationen. Er hat über 2 Millionen Social-Media-Follower, 1 Million Newsletter-Abonnenten und wurde von LinkedIn als einer der Top-5-Business-Influencer der Welt und von Xing als Top Mind 2021 ausgezeichnet.

Bernards neueste Bücher sind ‘Künstliche Intelligenz im Unternehmen: Innovative Anwendungen in 50 Erfolgreichen Unternehmen’

How Harley Davidson Is Using AI And Robots To Prepare For The 4th Industrial Revolution

2 July 2021

The Fourth Industrial Revolution, or Industry 4.0, where advanced digital technologies such as artificial intelligence, robotics, machine learning and the internet of things interact with the physical world and will impact our daily lives, how we relate to one another and how we work, has the world’s top executives and companies preparing for the changes that the revolution will create. American motorcycle manufacturer Harley-Davidson is one of those companies who has already begun to use artificial intelligence, machine learning, and robotics. Let’s look at a few ways Harley-Davidson is getting ready for the Fourth Industrial Revolution.

Artificial intelligence increased sales leads by 2,930%

When you think about effective marketing for a Harley-Davidson dealership, the first thing that pops into your mind is probably not the ways you can use artificial intelligence to ramp up your results. It’s a good thing the owner of New York City’s Harley dealership, Asaf Jacobi decided to give AI a try, because it increased the dealership’s leads by 2,930% in just three periods. 

That is a remarkable number for a start-up, but for an established brand such as Harley-Davidson, that was extraordinary. 

Although Jacobi had started researching options to boost sales at his dealership in the off-season and came across some AI tools for marketing and advertising, it was his chance meeting with Or Shani, the CEO of AI firm Algorithm, which had an AI-driven marketing platform called Albert that convinced Jacobi to give it a try.

How Albert works and the results it got for Harley-Davidson

The first test of Albert was a weekend promotion called “48 Bikes in 48 Hours.” They sold 15 motorcycles that weekend, nearly doubling the summer sales record of eight bikes sold in one weekend.

Albert used business logic, the KPIs available for Harley-Davidson NYC and past campaign performance to identify unknown audiences, the best budget allocation across digital channels and even evaluate the performance of different word choices or colors on the creative. Albert processed the data it had been given to figure out trending behavior. It continued to optimize the marketing and ad performance as new data continued to come in.

Albert executes digital ad campaigns autonomously and adjusts them automatically based on performance. It can figure out the audiences that are most likely to convert, compare platforms and implement learning across platforms, and discover what creative worked better. AI can do this exponentially faster than humans, in virtually real-time. 

Here’s what Albert was able to do for Harley-Davidson NYC:

• The dealership credits Albert with 40% of its motorcycle sales over a six-month period.
• In just three months, they had an increase of 2,930% in leads, with 50% of those being from lookalikes, prospects with similar buying patterns and preferences as those likely to purchase Harley-Davidsons. This insight opened up an entirely new audience that they had previously not marketed to.
•         Albert also discovered that Facebook ads converted 8.5 times higher than other Harley ads, so they focused their advertising efforts only on platforms that worked.

New factory optimized for more expensive workforce

Unlike many manufacturers, when you walk into the Harley-Davidson plant in York, Pa, you will still find human workers assembling the iconic motorcycles by hand. Although the factory was redesigned, it still has people everywhere. Workers operate in teams of five or six to build each bike. Since virtually every bike is one-of-a-kind, humans are uniquely qualified to adjust on the fly when it’s necessary to create these customized bikes.

Humans are also better equipped to solve problems that contribute to inefficiencies. In one example, a plastic piece that held electrical parts wasn’t fitting correctly. It caused the workers to have to spend an additional 1.2 seconds to push it into place. Seconds seem like a tiny amount of time, but when you add that up for each bike that is manufactured, this slight inefficiency resulted in 2,200 lost bikes annually and millions in revenue . It took one 20-year veteran and a team of other skilled workers to develop a simple solution.

Harley-Davidson uses robotic solutions when it makes sense in what is known as collaborative robotics, but prefers to use humans exclusively on the assembly floor. Robots are used for dangerous or repetitive work, and humans handle the work that requires higher technical skills and on-the-fly decision-making.

Harley-Davidson illustrates how a company can leverage the technology of the Fourth Industrial Revolution in a way that honors its brand and creates a collaborative work environment between humans and machines to create efficiencies, increased sales and lower costs. 

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How did Harley Davidson use AI to improve their sales

Key Takeaways in bullets

• AI helped Harley Davison to: Reach new audiences- Personalize their messaging - Make more impactful campaigns- Increase customer loyalty and engagement with the brand. Harley Davidson found that using AI in their marketing strategies allowed for better targeting, content curation and segmentation of customers, resulting in higher ROI on campaigns and increased customer satisfaction
• Automated segmentation and personalization of marketing messages is key for Harley Davidson. They help them uncover entire demographics that the business has never considered before.
• AI-guided marketing is no longer big-tech-only. Any company now can make use of many software-as-a-service platform to adopt cutting-edge technologies

Who is Harley Davidson?

Harley Davidson is an iconic American motorcycle manufacturer founded in 1903. It was originally a family-owned business but has since become one of the largest motorcycle companies in the world with dealerships located across the globe. The company is known for its classic designs, quality craftsmanship and customizing options that bring out each owners unique style. In recent years, Harley Davidson has embraced the use of Artificial Intelligence (AI) to help them stay ahead of the competition and increase their market share.

The Problem They Are Facing

Selling a high-end, hand-assembled product is a low-volume business; One branch in New York only makes one or two weekly sales. Therefore, it's important to spend money to attract customers, especially on attracting the right customers. They need to determine who, and where their customers can be reached and they need the data.

How is Artificial Intelligence (AI) used in practice?

Harley Davidson is using AI to reduce the cost of acquiring and retaining customers, allowing them to better target their customers. For example, AI can help Harley Davidson customize advertisements, analyze customer profiles and predict when a customer is likely to buy or service a motorcycle. In addition, they use AI technology to track their dealerships' performance and sales trends, so that they can quickly respond to changes in the market. AI helps them analyze data quicker and more precise and it helps them spot patterns and correlations that would never be seen by a human analyst.

The Results

Harley Davidson is seeing great results from their AI initiatives. The company has experienced an increase in sales and higher customer satisfaction scores. Due to their improved marketing efforts, customers are spending more on accessories and services when they visit dealerships. After moving to AI-driven customer targeting, Harley-Davidson New York increased its lead generation by 2930%. It also opened the company up to entire new audiences that it had never previously marketed to.

They also found what wordings in their advertisement is more effective. They realize their customers responded far better to the word "call" in adverts than "buy" - 447% better. Their AI system was able to recognize this and help marketers to better improve their content.

If you are keen to adopt AI in your association, contact us now!

We can help you to find the right AI solution for your business and make sure it is implemented correctly. Don't miss out on all these opportunities that AI has to offer - start with us today!

Harley Davidson's use of Artificial Intelligence (AI) shows just how powerful this technology can be. By leveraging data and machine learning, Harley Davidson was able to reduce costs, increase lead generation, and customize their ads for maximum customer satisfaction. If you're looking to get the most out of your marketing efforts, consider investing in AI today!

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How harley davidson used predictive analytics to boost sales in new york.

Harley-Davidson managed to turn their fortunes around by making use of predictive analytics. Here is a detailed analysis of how they made it work.  

Asaf Jacobi was selling one or two Harley-Davidson motorcycles a week at his New York dealership. One day during winter, he decided to take a walk in Riverside Park and came upon Or Shani, the CEO of Adgorithms. After talking about his low sales numbers, Shani suggested that Jacobi tries Albert. This AI developed by Adgorithms is designed for marketing purposes. It works on various platforms such as Google and Facebook to optimize ad campaigns.

Impressive Results.

When Jacobi gave it a trial, he sold over 15 motorcycles that week. It was almost double, what he had sold all summer. Jacobi kept at it. He went from getting a qualified lead a day to more than 40. In the first month, 15% of the new leads were similar to past high-value customers and were thus likely to purchase. By the third month, leads had grown by 29-30%. Out of those leads, 50% were similar to past high-value customers. Jacobi had to expand the call center to handle all the new business.

How the AI did that.

Today, Facebook, Google, and Amazon are leading in the use of AI. This has given them a significant advantage over customers by enabling them to target them with highly personalized marketing campaigns. However, other companies are also starting to join the AI race.

In the case of the Harley-Davidson dealership, Albert the Ai helped to grow store traffic by generating leads, which were simply customers who filled out a form expressing interest.

Making Data work for you.

Albert was given creative content from Harley-Davidson and some performance targets. The AI began to analyze existing data from the dealership. It was able to find characteristics and behaviors of high-value customers within New York. The Ai targeted those who completed purchases, added an item to their online cart, or were among the top 25% of people who spent time on the site.

With this data, Albert went after people on the internet that had similar characteristics. The AI ran tests on a few sample groups and collected data from the tests. It then conducted further tests with more variables. At the end of it all, it had gathered enough data to develop what a high-value customer would look like. It then scaled up the campaigns to reach out to these people.

The Benefits of AI in Predictive Analytics.

For Harley-Davidson, the AI analyzed what was working and what was a waste of money. The AI used what it gathered from these tests to create opportunities that were likely to lead to successful conversions. In short, it allocated resources to what had worked, which helped to improve the ROI for digital marketing campaigns.

The traditional method used by marketers is to create buyer personas. The personas are based partly on historical data, the marketer’s experiences, guesswork, and gut feelings. When a company decides to use personas, it has to rely on guesswork to determine what worked and what failed.

An Ai does need to come up with personas. They find real opportunities by checking the behaviors of online profiles and determining who has behaviors that are indicative of a high degree of conversion.

AI Could Help to Save Time.

Even when equipped with the most complex digital tools, humans can only manage a few tasks at a time. They will also have a hard time making an unbiased application of any insights that they gather. On the other hand, an AI can process millions of interactions a minute. They can also run more than thousands of tests at the same time and make predictions at the same time.

Another advantage of an AI is that it never gets tired. While humans can only manage a few hours of analysis, the AI can work all the time all year round. Also, the AI modifies its marketing strategy in real time, which means that no money is wasted at any time.

Making Use of AI.

Since AI is still quite new, and since marketers may be wary of relinquishing control to a device, it is wise to adopt AI incrementally. At first, it can be used to run small and short experiments. As the results trickle in, the AI can then be modified.

When carrying out experiments, it is important to define goals. For instance, the goals can be increased leads or better ROI.

When choosing the tool to use with AI, you need to know what you want. Some tools can focus on a single task such as optimizing web content. Other sophisticated tools offer more capabilities. Still, others can produce results but will not act on them without being prompted.

However, you should not be afraid to take the plunge. You will be able to have the edge over the competition that is still not sure whether they should use the technology.

How You Can Ensure Success.

If you decide to use AI for Predictive Analytics, some best practices will help you to succeed. Here are some of them:

1.   Have Accurate CRM.

The AI will work with your current CRM. Thus, if it is full of garbage, it will generate garbage. The predictions will only be as good as the underlying data. It is better to have less data, which is accurate than to have silos of data that is full of errors.

2.   Take Your Time.

Although the AI is fast, you do not have to be. A good example is Harley Davidson that started with micro-segments and numerous tests. This helped them to adjust the campaign accurately before they ever went full scale.

3.   Let the AI do its Magic.

It can be difficult for business owners to wrap their minds around the concept of a program controlling their marketing campaign. However, they will have to learn how to let go and let the AI work. The AI will work based on proven data and not personal insights. It will allow you to make predictions that are more accurate.

Conclusion.

Although the AI will take away some of the decision-making power, it can help to motivate the sales team and keep the business moving forward. You will also have more time to make other decisions about your business.

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Python Machine Learning Case Studies

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Case Flash Forward: Harley-Davidson Motor Company

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Each Case Flash Forward provides educators and students with a brief update of key changes at a particular company covered in a related case study. It is a compilation of publicly-available content…

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Each Case Flash Forward provides educators and students with a brief update of key changes at a particular company covered in a related case study. It is a compilation of publicly-available content prepared by an experienced editor. This Case Flash Forward provides an update on Harley-Davidson, including significant developments, current executives, key readings, and basic financials.

Jan 8, 2016 (Revised: Mar 21, 2023)

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Publication date: 24 April 2020

Teaching notes

Harley-Davidson's first-ever chief marketing officer has his work cut out for him as the classic American motorcycle manufacturer seeks to curb slowing sales from aging customers. The dilemma: what to do with its less known and unprofitable Buell brand, which has a younger customer base? Which of five options continue its dual-brand strategy, double down on Buell, operate Buell as an endorsement brand, sell it, or discontinue the brand entirely will best attract younger buyers without alienating current diehard customers?

• Consumer marketing
• Corporate strategy
• Decision-making
• Growth strategy
• Market research
• Marketing planning
• Marketing strategy
• Product management

Roese, N.J. and Chernev, A. (2020), "Harley-Davidson: Rejuvenating an Iconic Brand", . https://doi.org/10.1108/case.kellogg.2021.000017

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Copyright © 2019, The Kellogg School of Management at Northwestern University

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Harley-Davidson in India (A)

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• Harley-Davidson in India (B)  By: Krishna Palepu and David Lane

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» Operations Case Studies » Operations Short Case Studies » View Detailed Pricing Info » How To Order This Case » Business Case Studies » Case Studies by Area » Case Studies by Industry » Case Studies by Company

process control, and JIT. The company soon realized that in order to beat Japanese competition, it had to implement these practices as well. The company successfully implemented JIT practices and reaped several benefits. After spectacular growth in the 1990s and the early 2000s, Harley-Davidson again faced hard times from 2007. The case also looks at the challenges faced by the company in the latter part of the first decade of the new millennium, and how it was trying to focus on ‘continuous improvement' in a bid to bring itself back into profits.

» To understand Just-in-time philosophy and its importance in reducing overall production cost and enhancing product quality. » To understand how the JIT philosophy requires the alignment of operational strategies to achieve the goal. » To understand the important role of having a stable supplier network for achieving JIT. » To understand that besides the use of statistical techniques in achieving JIT, employees' involvement is equally important. » To discuss the challenges faced by Harley-Davidson since 2007. » To explore operational strategies that Harley-Davidson can adopt to overcome those strategies.

Operations Management, Just in Time (JIT), Supplier Network, Operational Strategies, Statistical Process Control, Employee Involvement, Statistical Operator Control, Inventory Management, Material As Needed (MAN), Continuous Improvement, Supply Chain, Low Cost manufacturing, Harley Davidson, Quality, Japanese Manufacturing

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One-line summary

Harley-Davidson Australia used Instagram to successfully reach a younger audience in the 18−35 year old millennial market.

The challenge

Harley-Davidson is an iconic American motorcycle brand. The brand has come to symbolise freedom and individuality, but is associated with an older market. The brand wanted to raise brand awareness and engage a younger audience.

The solution

Harley Davidson decided to use Instagram ads, for the first time, to reach 18−to 35-year old men in Australia and New Zealand.

Harley-Davidson used a series of illustrations to fit in with the visual nature of Instagram. They wanted to appeal to young, adventurous people who appreciate the world through a more visual lens. The brand identified three themes they felt resonated with their intended audience, and tapped in to their own artistic heritage of seeing motorcycles as pieces of art. The brand commissioned local Australian artists to interpret these themes.

The illustrations were displayed as carousel ads for each theme. When swiped the images connected to form a storyline. The visual style was designed to mimic tattoo-like images and motifs that are popular in biking culture.

The results

Over a run of just over two weeks, the ads reached almost 1.4 million men, aged between 18 and 35.

The ad drove over 8 000 clicks through to the brand’s website.

Harley-Davidson achieved their objective of resonating with a younger audience, and ultimately expanding their demographic (Digital Training Academy, 2016).

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How Innovation and Lean Management Work at Harley-Davidson ?

November 20, 2016

WE understand that sometimes, lean management and innovation can hardly coexist. How can you streamline and reduce costs and variation when you’re always developing something new? But we are also glad to let our readers know that it can actually be done; Harley-Davidson has proved to be the case study you should look at.

If your company exists in an industry that does not require constant and rapid innovation, you could be either lucky or in a dying industry. But at Harley-Davidson, it was innovate or die. With other companies from overseas competing directly for a small market, they had to do something good, something fast and something solid.

CEOs at Harley-Davidson decided that product development simplified was the way to keep their customers and maintain their dominance. Even if they started this trend in the 80’s, it’s proved effective and the right decision; the world is fast, interconnected and digital. People have more money and want to spend it, and you have to make the decision easy for them so that they spend it on you. Harley-Davidson decided that they could capture the market by keeping up with the pace of technology.

Can you believe that the company averages 4 new motorcycles a year? Thanks to lean management and innovation working together, they’ve been able to do that. Let’s take a look at how:

1. harley-davidson adheres to innovation by letting creativity flourish and generate learning within the company..

No one’s opinion is disregarded. The company holds regular meetings where anyone regardless of rank or job can input something in product development. The firm knows that it cannot innovate in a vacuum but its leaders also understand that innovation must be controlled as part of lean management. They create a space and time for innovation or at the very least, collaboration. A learning organization always thinks for the long-term, uses the mastery of all of its employees, is open to paradigm shifts and reworking of mental models, builds a shared vision with everyone from the CEO to the clerks and engages their team in dialogue without fear.

2. Harley-Davidson avoids panic by accepting constant change.

For a motorcycle company, it’s ingeniously adaptive. It leads the pack because it knows it has to go with the customer’s flow and the current trends. Whereas other companies resist change all the time and try to impose their products on clients, Harley-Davidson is ready to give the customer what he or she wants and develop ideas on that. Since they’re fluid, releasing so many motorcycles in a year just got easier.

3. The company doesn’t want to put fires so it always institutes good, sound strategies and plans.

Fires in production and in other aspects of the company is inevitable. Lean management seeks to reduce these to the minimum and at the same time, innovation allows managers to respond to these concerns swiftly and smartly. The two ideas can coexist, it’s just a matter of providing context. Although fire fighting may be inspiring and motivational but the fact that the fires happen so often is going to adversely affect your company.

4. Leaders are trained to be effective and think critically so that the system is just as effective.

When Harley-Davidson adopted lean management, they made sure they had leaders who could think critically about implementing it and following-through—even in the face of upper management resistance. What’s funny about their story is that there was pushback from both upper and lower management but the Black Belts and the experts handled it. In the end, the efforts of the lean management team paid off!

5. Arriving at the realization that innovation should be grounded on learning and not feasibility.

Most of the time, innovators are given free reign; this costs a lot of money. Harley-Davidson did not have a lot of capital so they said they would stop projects that drained too much but not let the efforts go to waste. Each project despite success or failure was a learning experience and they could use the salvageable components on new innovations. Lean management happened in the firm when they stopped looking at feasibility and instead focused on strategy and anticipating variable determined from good analysis.

 Please share this insight to inspire operational excellence and customer satisfaction in every organisation. It’s free like this ad free article. Thank you

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