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University of Pennsylvania

Customer Analytics

University of Pennsylvania via Coursera


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Data about our browsing and buying patterns are everywhere. From credit card transactions and online shopping carts, to customer loyalty programs and user-generated ratings/reviews, there is a staggering amount of data that can be used to describe our past buying behaviors, predict future ones, and prescribe new ways to influence future purchasing decisions. In this course, four of Wharton’s top marketing professors will provide an overview of key areas of customer analytics: descriptive analytics, predictive analytics, prescriptive analytics, and their application to real-world business practices including Amazon, Google, and Starbucks to name a few. This course provides an overview of the field of analytics so that you can make informed business decisions. It is an introduction to the theory of customer analytics, and is not intended to prepare learners to perform customer analytics. Course Learning Outcomes: After completing the course learners will be able to... Describe the major methods of customer data collection used by companies and understand how this data can inform business decisions Describe the main tools used to predict customer behavior and identify the appropriate uses for each tool Communicate key ideas about customer analytics and how the field informs business decisions Communicate the history of customer analytics and latest best practices at top firms


  • Introduction to Customer Analytics
    • What is Customer Analytics? How is this course structured? What will I learn in this course? What will I learn in the Business Analytics Specialization? These short videos will give you an overview of this course and the specialization; the substantive lectures begin in Week 2.
  • Descriptive Analytics
    • In this module, you’ll learn what data can and can’t describe about customer behavior as well as the most effective methods for collecting data and deciding what it means. You’ll understand the critical difference between data which describes a causal relationship and data which describes a correlative one as you explore the synergy between data and decisions, including the principles for systematically collecting and interpreting data to make better business decisions. You’ll also learn how data is used to explore a problem or question, and how to use that data to create products, marketing campaigns, and other strategies. By the end of this module, you’ll have a solid understanding of effective data collection and interpretation so that you can use the right data to make the right decision for your company or business.
  • Predictive Analytics
    • Once you’ve collected and interpreted data, what do you do with it? In this module, you’ll learn how to take the next step: how to use data about actions in the past to make to make predictions about actions in the future. You’ll examine the main tools used to predict behavior, and learn how to determine which tool is right for which decision purposes. Additionally, you’ll learn the language and the frameworks for making predictions of future behavior. At the end of this module, you’ll be able to determine what kinds of predictions you can make to create future strategies, understand the most powerful techniques for predictive models including regression analysis, and be prepared to take full advantage of analytics to create effective data-driven business decisions.
  • Prescriptive Analytics
    • How do you turn data into action? In this module, you’ll learn how prescriptive analytics provide recommendations for actions you can take to achieve your business goals. First, you’ll explore how to ask the right questions, how to define your objectives, and how to optimize for success. You’ll also examine critical examples of prescriptive models, including how quantity is impacted by price, how to maximize revenue, how to maximize profits, and how to best use online advertising. By the end of this module, you’ll be able to define a problem, define a good objective, and explore models for optimization which take competition into account, so that you can write prescriptions for data-driven actions that create success for your company or business.
  • Application/Case Studies
    • How do top firms put data to work? In this module, you’ll learn how successful businesses use data to create cutting-edge, customer-focused marketing practices. You’ll explore real-world examples of the five-pronged attack to apply customer analytics to marketing, starting with data collection and data exploration, moving toward building predictive models and optimization, and continuing all the way to data-driven decisions. At the end of this module, you’ll know the best way to put data to work in your own company or business, based on the most innovative and effective data-driven practices of today’s top firms.

Taught by

Eric Bradlow, Peter Fader, Raghu Iyengar and Ron Berman


2.7 rating, based on 17 Class Central reviews

4.6 rating at Coursera based on 11721 ratings

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  • Anonymous
    I'd like to say a few things about this course. Firstly, I literally withdrew from a $50K Business Analytics program with Harvard, not the extension school, but the actual Harvard Business School. I looked at Wharton, it was very cost effective and…
  • Dmitrijs Kass
    Do not expect any quantitative material in the lecture videos despite the "analytics" in its title. If you don't, then this course will provide you with a good overview of customer analytics, including terminology and intuition. Despite the fact that I was looking for a quantitative course, I really enjoyed it.

    For those interested in the probabilistic models behind the material covered, you will be provided with a link to a scientific paper (Customer-Base Analysis in a Discrete-Time) which describes the model in details. Above that it provides a detailed instruction for implementing it in both Excel and Matlab. This is a valuable paper.
  • Llama
    This course is probably not what you think it covers. It's for managers who need to know what is customer analytics at a high-level. If you expect to learn about how to 'do' customer analytics, look elsewhere.
  • Anonymous
    Very basic course feels like high school level course, Total waste of time. Don't even think about throwing away money on certificate.
  • Nan Halberg
    I like to take the quizzes the first time before I go through the lectures and class materials to establish a benchmark for myself. On most of the quizzes for this course I passed them on that first try using common sense. The lectures reminded me of high-priced business consultants who have a slick style and good spiel but really not very much to say.
  • This course is specifically dedicated to conceptual and theoretical understanding of customer analytics. Though there's enough here that you could find application of the theory on your own, no practical analytical examples are given, nor any technical understanding of analysis given. This would be a useful course for an individual on a pure management track, in understanding the results of an analyst, but not for technical analysts.
  • Anonymous
    I unenrolled from the course after finishing the week 3 lectures. I thought it was very badly taught. Concepts were breezed over very quickly without sufficient explanation, a whole stretch of lectures seemed like spontaneous off-the-cuff talk without real planning, and there was insufficient supportive material like diagrams, charts, to make visualizing certain concepts more easy. I felt that I would have gotten much more out of a textbook than this course.
  • Anonymous
    A good and distinguished course in all fields, especially the professional fields. This course is available to all countries.
  • Anonymous
    Too little content and engagement. Lack of practical cases and challenging assignements. And you are expected to pay for that.
  • Marc
    Completed the course in 2015. Provides an overall solid understanding of the use of analytics in business, marketing, HR and operations
  • Anonymous
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    Alex Ivanov
  • Mark Blackmore

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