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.

Customer Analytics

Customer Analytics
This course is part of Business Analytics Specialization



Instructors: Eric Bradlow
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Skills you'll gain
- Analytics
- Consumer Behaviour
- Descriptive Analytics
- Customer Analysis
- Data-Driven Marketing
- Customer Insights
- Correlation Analysis
- Predictive Modeling
- Business Marketing
- Revenue Management
- Data-Driven Decision-Making
- Customer Data Management
- Regression Analysis
- Marketing Analytics
- Model Optimization
- Advanced Analytics
- Predictive Analytics
- Business Analytics
- Data Collection
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Reviewed on Feb 6, 2019
Provides very good overview and understanding of cutomer analytics, how to collect data, measure, predict outcomes and what techniques to use in different scenarios. Highly recommend for beginners.
Reviewed on Mar 24, 2021
Excellent for learning different types of analytics, the different tools, learning which type of analytics and tool to use in a specific situation. Furthermore how to implement analytics in business
Reviewed on Feb 1, 2016
The undoubted plus is comprehensible for learners content. A big thank you for that!But in my opinion course is also too abstract, it may be better to give more practical examples of data usage.
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