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
Introduction to Customer Analytics
What is Customer Analytics? How is this course structured? What will I learn in this course? What will I learn in the Business Analytics Specialization? These short videos will give you an overview of this course and the specialization; the substantive lectures begin in Week 2.
In this module, you’ll learn what data can and can’t describe about customer behavior as well as the most effective methods for collecting data and deciding what it means. You’ll understand the critical difference between data which describes a causal relationship and data which describes a correlative one as you explore the synergy between data and decisions, including the principles for systematically collecting and interpreting data to make better business decisions. You’ll also learn how data is used to explore a problem or question, and how to use that data to create products, marketing campaigns, and other strategies. By the end of this module, you’ll have a solid understanding of effective data collection and interpretation so that you can use the right data to make the right decision for your company or business.
Once you’ve collected and interpreted data, what do you do with it? In this module, you’ll learn how to take the next step: how to use data about actions in the past to make to make predictions about actions in the future. You’ll examine the main tools used to predict behavior, and learn how to determine which tool is right for which decision purposes. Additionally, you’ll learn the language and the frameworks for making predictions of future behavior. At the end of this module, you’ll be able to determine what kinds of predictions you can make to create future strategies, understand the most powerful techniques for predictive models including regression analysis, and be prepared to take full advantage of analytics to create effective data-driven business decisions.
How do you turn data into action? In this module, you’ll learn how prescriptive analytics provide recommendations for actions you can take to achieve your business goals. First, you’ll explore how to ask the right questions, how to define your objectives, and how to optimize for success. You’ll also examine critical examples of prescriptive models, including how quantity is impacted by price, how to maximize revenue, how to maximize profits, and how to best use online advertising. By the end of this module, you’ll be able to define a problem, define a good objective, and explore models for optimization which take competition into account, so that you can write prescriptions for data-driven actions that create success for your company or business.
How do top firms put data to work? In this module, you’ll learn how successful businesses use data to create cutting-edge, customer-focused marketing practices. You’ll explore real-world examples of the five-pronged attack to apply customer analytics to marketing, starting with data collection and data exploration, moving toward building predictive models and optimization, and continuing all the way to data-driven decisions. At the end of this module, you’ll know the best way to put data to work in your own company or business, based on the most innovative and effective data-driven practices of today’s top firms.
Eric Bradlow, Peter Fader, Raghu Iyengar and Ron Berman
I'd like to say a few things about this course. Firstly, I literally withdrew from a $50K Business Analytics program with Harvard, not the extension school, but the actual Harvard Business School. I looked at Wharton, it was very cost effective and the...
I'd like to say a few things about this course. Firstly, I literally withdrew from a $50K Business Analytics program with Harvard, not the extension school, but the actual Harvard Business School. I looked at Wharton, it was very cost effective and the certificate, according to Wharton, will state "Wharton". While it is Wharton Online, its not some fake thing like ECornell, which is just a rebrand of Kaplan University.
So anyway, Wharton's website directed me to Coursera, I did not mind and for $79/mo, I thought it would be just as good as Harvard's program; except that this is a MOOC and MOOCs are becoming more and more popular in the world (we should now call, "globucation" since the internet connects us all).
The first thing I notice is that the presentations are ill prepared. The entire course advertises many companies under the guise of 'education', it is little more than slipping in some names for business professionals to jump on and use for their own businesses.
None of the charts or graphs made any sense, they're haphazardly presented to you. None of the topics are covered in a way that conveys knowledge. Everything is breezed over very quickly. You will find that more time was spent explaining what company X and Z does and not actual, "Customer Analytics".
Moreover, the quizzes are the worst. The materials covered do no match the quizzes at all. There are questions in the quiz from the "additional" reading section, which is not something to be included in a quiz! Half of the answers are WRONG! I literally pulled out professor notes from the transcripts, VERBATIM, and proved my answers were right, did Coursera do anything about it? Nope. I'll tell you why though.
Think about this for a good minute.
If 50,000 students sign up for a MOOC and pay $79 per month and it takes them about a month to complete. What kind of money was generated? $3.95M almost $4 million dollars, if 100% of the students paid for and enrolled. The Wharton School would never see that kind of money from ONE COURSE offering even after Coursera takes their cut. So now, you can see how money ruined what previously was, free online education.
Dmitrijs Kass completed this course, spending 2 hours a week on it and found the course difficulty to be easy.
Do not expect any quantitative material in the lecture videos despite the "analytics" in its title. If you don't, then this course will provide you with a good overview of customer analytics, including terminology and intuition. Despite the fact that I was looking for a quantitative course, I really enjoyed it.
For those interested in the probabilistic models behind the material covered, you will be provided with a link to a scientific paper (Customer-Base Analysis in a Discrete-Time) which describes the model in details. Above that it provides a detailed instruction for implementing it in both Excel and Matlab. This is a valuable paper.
Llama completed this course and found the course difficulty to be easy.
This course is probably not what you think it covers. It's for managers who need to know what is customer analytics at a high-level. If you expect to learn about how to 'do' customer analytics, look elsewhere.
Nan Halberg completed this course, spending 4 hours a week on it and found the course difficulty to be easy.
I like to take the quizzes the first time before I go through the lectures and class materials to establish a benchmark for myself. On most of the quizzes for this course I passed them on that first try using common sense. The lectures reminded me of high-priced business consultants who have a slick style and good spiel but really not very much to say.
Anonymous completed this course.
Very basic course feels like high school level course, Total waste of time. Don't even think about throwing away money on certificate.
Jason Michael Cherry completed this course, spending 2 hours a week on it and found the course difficulty to be easy.
This course is specifically dedicated to conceptual and theoretical understanding of customer analytics. Though there's enough here that you could find application of the theory on your own, no practical analytical examples are given, nor any technical understanding of analysis given. This would be a useful course for an individual on a pure management track, in understanding the results of an analyst, but not for technical analysts.
Anonymous is taking this course right now.
I unenrolled from the course after finishing the week 3 lectures. I thought it was very badly taught. Concepts were breezed over very quickly without sufficient explanation, a whole stretch of lectures seemed like spontaneous off-the-cuff talk without real planning, and there was insufficient supportive material like diagrams, charts, to make visualizing certain concepts more easy. I felt that I would have gotten much more out of a textbook than this course.
Anonymous completed this course.
Too little content and engagement. Lack of practical cases and challenging assignements. And you are expected to pay for that.
Marc completed this course.
Completed the course in 2015. Provides an overall solid understanding of the use of analytics in business, marketing, HR and operations