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Learner Reviews & Feedback for Customer Analytics by University of Pennsylvania

4.6
stars
10,506 ratings
2,298 reviews

About the Course

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...

Top reviews

MA
Aug 4, 2020

This course includes a comprehensive overview of the all the basic models that are used to analyze data concerning customer behavior. The real-life examples made it easier to relate to those theories.

ND
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!

Filter by:

1976 - 2000 of 2,246 Reviews for Customer Analytics

By Adam D

Sep 27, 2021

Thank you

By Khurram S K

Mar 26, 2016

Helpful!!

By Gourav S

Aug 8, 2019

Good one

By PARK K B

Sep 9, 2020

success

By Joshua S

Apr 29, 2019

Amazing

By Nazatul A B A R

Nov 4, 2016

awesome

By Alvin L

Mar 16, 2016

Great!

By waseem s

May 1, 2020

tttt

By yash

Apr 28, 2020

nice

By 马雨禾

Nov 17, 2019

easy

By Yash D

Jun 1, 2019

good

By thulasiram .

Sep 11, 2016

good

By Venkatesh C

Mar 2, 2016

good

By HymaPrashanthi

Jan 16, 2018

g

By Govind R A

Aug 1, 2017

F

By Hemant M

Jun 10, 2020

It was a good hands on basic Introduction for Customer analytics. However The introduction of descriptive,predictive and prescriptive analytics was mixed with marketing so the amount of time that could be dedicated to marketing was dedicated to introducing analytics. This course was more general knowledge and E-Commerce rather than marketing. I wanted the course to be more specific: How marketing theories help in building predictive models and What a marketing analyst knows that other data analysts don't know. If you are an absolute beginner in analytics, give it a try. Otherwise it may be too simple for you.

By James P

Jun 30, 2020

The lectures cover material at a strategic-high level that is easy to understand and apply (very limited technical guidance). I do wish the lectures provided a bit more technical guidance, but I got value from the strategic delivery. The quizzes sometimes dove into a level of detail that the lectures really didn't prepare me for. They also covered content that wasn't really aligned with the themes of the lectures and didn't add much value to me to learn. I found this confusing and really did not find the quizzes to be a good representation of the material, or a very useful component of the courses.

By Christine A

Nov 9, 2018

The information was useful - some material was review and some was new to me. I found that several of the quiz questions were not answerable based on the content presented in the lectures, PPT slides and additional reading material. This was very frustrating. I listed to the lectures and took copious notes. I reviewed the PPT slides and transcripts of the lectures. I read the additional material and took notes on it. But I had to take the quizzes multiple times in order to pass because some of the questions were not directly or indirectly related to the material.

By Courtney F

Jul 20, 2020

The lecturer for Predictive Analytics had barely any slides so being successful meant following him completely through the video which I do not think he was as good as explaining certain concepts as the first and third professors were. The last quiz also had barely to do with the Applications content in the final week of lectures - looking through the slides after failing on the first attempt was pointless. Overall, I learned a lot but the teaching style of the second professor and the final quiz were not really indicative of what was taught during the session.

By Andreas D

Nov 20, 2018

The general overview was good and a refresher what I was taught in uni 15 years ago. The material looks a bit old-fashioned and some slides look like the content was 'thrown together'.

I am missing going into details, particularly in calculating optimum price. The use of Excel Solver was not even mentioned to calculate the exact price, rather than determining it through a graph which is there or there about.

Also was missing kind of how to actually calculate CLV with Excel (I have seen a formula in the slides once, but that doesn't tell me much).

By Guilherme S

Oct 7, 2015

You can see that the teachers now what they are talking about... but this seems to be a stripped and then again stripped version of a real class. Some courses at coursera are the entire university classes and you learn a lot from them. In those 5 weeks of this course there is not that much content, so you end up thinking... where can i get more information about each single topic mentioned. References to external resources are pretty weak so far. I've loved the classes, but it each "week" could be a course on its own, with much more content.

By Dejan H

Mar 24, 2018

Unfortunately I did not feel that this course entirely delivered on its objective. At moments I felt that the number of topics being covered was too large and the scope too broad. Probably the best lecture was week 3, followed by week 2 and week 5. Topics covered should build on each-other in a more natural progression and some introduction of practical examples and actual modeling exercises would allow the learners to try to apply some of the concepts being covered. More examples and tie-ins to real-world usage are a must.

By Toby P

Oct 18, 2015

Pros: Concentrated material covered clearly and efficiently. For the limited number of video material (~1/week), there is a lot of basic concepts explained well.

Cons: Few specific examples, which I think reinforce the abstract material presented. I think going though some numbers really drives a point home, and not having examples left me feeling a bit adrift.

Overall: Good information and food for though, but not a lot of actual analytics. Take if you don't have but want experience in this area.

By Scott T

Feb 8, 2021

Fader's content made this course worth it because he had concepts and training you could actually apply and work with hands on but the lectures from Berman felt very disjointed and all over the place - sometimes the title of the module didn't match the content. Bradlow's content was very high level. He talked more about examples rather than teaching how to calculate customer life time value.

I think this course could be made a lot better by teaching a section on how to calculate lifetime value.

By Nitin B

May 6, 2020

The course gave a good overview of the techniques in customer analytics that have been used and are currently employed by the industry. However, I felt the course could've been better if it helped develop an intuitive understanding of the concepts. Taking real-life case studies and actually going about solving the problems would have more useful. For instance, a case like "Suppose you're a category manager and want to identify low-value customers, how to go about it?" would have helped a lot.