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University of Pennsylvania

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-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

Status: Predictive Analytics
Status: Data Collection
Course14 hours

Featured reviews

PK

5.0Reviewed Feb 5, 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.

JB

5.0Reviewed Mar 23, 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

ZH

4.0Reviewed May 31, 2023

enjoyed the lectures especially from Fader and Bradlow, wish the course had more details on model construction and data analysis but i guess they do not fall into the scope of an introductory course

SJ

5.0Reviewed Oct 15, 2017

This course is designed with highly skilled faculties. The way they explains carry a lot of meaning in small format. One may easily understand without having any prior knowledge of this whole area.

MS

4.0Reviewed Nov 14, 2017

Interesting look at how analytics can be used at the individual customer level. Not intended to help participants do the actual data work, but good for providing awareness about what is possible.

AA

5.0Reviewed Apr 5, 2017

Perfect Course for those who want to inquire insight and knowledge of how tons of data that we generate in our day to day life is being utilized by big organizations in optimizing their productivity.

ND

5.0Reviewed Jan 30, 2019

Though I have worked on Customer Analytics with my previous work experiences and also on Surveys etc at George Brown College Canada, this module was more than insightful. Lots of learning to learn eh!

GL

4.0Reviewed Jun 5, 2020

Good course for starting to know the important of data and how to manage that into customer development - customer analytics. The presentation really clear combine with explanation from the lecturer.

EG

4.0Reviewed Apr 27, 2022

W​ell, this course is like a front page of Wharton for coursera users. At the end of the course the only thing I wanted was enroll in Wharton College, crazy idea - it very prestigious thus costly.

CC

5.0Reviewed Feb 11, 2016

A fantastic course to start wading into making sense of data and people. Would love to see some more exposure to the processes and approaches (CLV) at a deeper level, but outstanding non the less.

TN

4.0Reviewed Sep 15, 2017

The first lecturer sounds boring, but the quality of other lectures is increasing. I enjoy the last lecture most where I can see the real value of analytics in business. Thanks for a nice course.

AD

4.0Reviewed Dec 3, 2017

In 3rd week Video ,there is less content.Also some question cant be answered though there is no explanation given in video syllabus(Questio_ Genuine data mining Technique) as asked in assignment

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