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Customer Analytics

Data about our browsing and buying patterns are everywhere. From credit card transactions and online shopping carts, to customer loyalty programs and user-g...
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Platform: Coursera
Video: 5h 48m
Language: English

Table of contents


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

You will learn


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This course is for

The course creator hasn’t defined the level of this course.
The Wharton School
University of Pennsylvania
Professor Eric T. Bradlow is the K.P. Chao Professor, Professor of Marketing, Statistics and Education, Vice-Dean and Director of Wharton Doctoral Programs, and Co-Director of the Wharton Customer Analytics Initiative. An applied statistician, Professor Bradlow uses high-powered statistical models to solve problems on everything from Internet search engines to product assortment issues. Specifically, his research interests include Bayesian modeling, statistical computing, and developing new methodology for unique data structures with application to business problems. Eric was recently named a fellow of the American Statistical Association, American Educational Research Association, is past chair of the American Statistical Association Section on Statistics in Marketing, past Editor-in-Chief of Marketing Science, is a past statistical fellow of Bell Labs, and worked at DuPont Corporation’s Corporate Marketing and Business Research Division and the Educational Testing Service.
Platform: Coursera
Video: 5h 48m
Language: English

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