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学生对 宾夕法尼亚大学 提供的 消费者分析 的评价和反馈

4.6
9,854 个评分
2,181 条评论

课程概述

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...

热门审阅

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!

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.

筛选依据:

2076 - 消费者分析 的 2100 个评论(共 2,122 个)

创建者 Lauri S

Apr 10, 2016

While these courses have interesting subjects, the content is very light, requires little effort and hence results in little learning.

创建者 sebastien k

Sep 26, 2017

Good introduction to get an helicopter view on the topic.

Lacking of hands on exercises and explanation of more advanced concepts.

创建者 Clifford N D

Dec 12, 2020

Basic course on customer analytics, was expecting a little more advanced content looking at the course content and structure.

创建者 Cackowska D

Feb 11, 2017

it was quite difficult to follow , due the language barrier and it was only overview , they could gave a bit more informa

创建者 Manoj J

Aug 6, 2016

I was expecting some models will be designed and solved in the class sessions. But the lectures contain only theories.

创建者 Hussein M

Jul 25, 2020

the course is introduction and it's not deep , i felt bored actually plus i was afraid form the Indian professor

创建者 Ananth V B

Feb 8, 2017

Lots of theory and concepts, but little in the way of how to solve customer analytics problems yourself.

创建者 Hugo S C

Nov 30, 2020

The material is very superficial and there is not much practice; all quizzes are conceptual only.

创建者 Gladys L

Dec 26, 2016

Compared to Operations Analytics class, this class does not provide as much applicable skills.

创建者 Noel F

Jan 25, 2016

Very high level course, interesting as a background but doesn't really deep dive in any topic.

创建者 Harsh P

May 22, 2020

Need some practical application assignments instead of a quiz!

创建者 Semen G

Nov 22, 2020

Субтитры не работают, тесты в любом случае на английском

创建者 Daniel A

Oct 24, 2019

It did not satisfy my thirst for knowledge on this topic

创建者 Зотов В А

Mar 18, 2019

Too basic. Rather an overview, than an actual course

创建者 Maria K

Oct 14, 2020

Maybe could be more extensive and profound

创建者 Jonathan Z

Feb 6, 2016

Almost no practical application

创建者 Timothy D

Jan 17, 2019

Not enough real world examples

创建者 Svetlana K

Sep 22, 2018

Outdated slides and lectures

创建者 J R

Aug 18, 2016

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创建者 Jesus V

Oct 3, 2020

The examination at the very end of every week must be taken once and you can retake it after 24 hrs, the questions must be different everytime you repaet it, add additional choices like instead of 5 choices make it 10, or use the Choose which all that applies principle. By making it rig free you are giving credibility to the exam and the test takers itself. The reason why on line course are not taken seriously is just because you can rig the result of the exam by keep on repeating taking it and memorizing the choices in the questions. YOU HAVE TO RESTRATEGIZE HOW NOT TO RIG THE EXAM so that it has credibilty and taken seriously.

创建者 Diane M

Sep 21, 2015

Poorly designed. The lectures just consist of people talking to the camera with little visual aids. There aren't any materials to read over or study. The transcripts of the videos come in a .txt file... On the whole, really not at all the caliber I would have wanted to see from Wharton. If I had paid for this course, I would be really frustrated.

创建者 Anas M

Jun 28, 2019

Quiz questions were not clear. Content was subpar for a "top" university. It seems like this was nothing more than a haphazardly put together course to make money and nothing more. This course felt like a complete waste of time and I believe the concepts and material in the modules are outdated.

创建者 Jiang T

Sep 22, 2015

In fact, i think this course is waste my time because the course only have lecture. They don't tell you how to use software to solve the problem even do not give any practical homework. Compared to other course, the quality of this course is extremely low.

创建者 Abdullah A D

Aug 30, 2020

It's hard to follow along, the instructor is mostly reading of slides and the level of teaching is of a low standard. The content is actually very good but its not put together well enough for a beginner in the field to capture the information.

创建者 Jarratt O

May 28, 2020

audio is bad. takes too long to move from the fluff and abstract to anything concrete. week 3 has A HUGE gap between lectures and quizzes. showed some interesting concepts but doesnt tell us how we can get there and thus take data for granted