Customer Analytics

About this 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

Created by:  University of Pennsylvania

• Taught by:  Eric Bradlow, Professor of Marketing, Statistics, and Education, Chairperson, Wharton Marketing Department, Vice Dean and Director, Wharton Doctoral Program, Co-Director, Wharton Customer Analytics Initiative

  The Wharton School

• Taught by:  Peter Fader, Professor of Marketing and Co-Director of the Wharton Customer Analytics Initiative

  The Wharton School

• Taught by:  Raghu Iyengar, Associate Professor of Marketing

  The Wharton School

• Taught by:  Ron Berman, Assistant Professor of Marketing

  The Wharton School

Basic Info Course 1 of 5 in the Business Analytics Specialization Commitment 4 weeks of study, 5-6 hours/week Language English, Subtitles: Spanish, Chinese (Simplified) How To Pass Pass all graded assignments to complete the course. Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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University of Pennsylvania The University of Pennsylvania (commonly referred to as Penn) is a private university, located in Philadelphia, Pennsylvania, United States. A member of the Ivy League, Penn is the fourth-oldest institution of higher education in the United States, and considers itself to be the first university in the United States with both undergraduate and graduate studies.

Syllabus


WEEK 1


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.


2 videos expand

1. Video: Course Introduction and Overview
2. Video: Overview of the Business Analytics Specialization

WEEK 2


Descriptive Analytics



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.


7 videos, 2 readings expand

1. Video: What is Descriptive Analytics?
2. Video: Descriptive Data Collection: Survey Overview
3. Video: Descriptive Data Collection: Net Promoter Score and Self-Reports
4. Video: Descriptive Data Collection: Survey Design
5. Video: Passive Data Collection
6. Video: Media Planning
7. Video: Causal Data Collection and Summary
8. Reading: Descriptive Analytics Slides
9. Reading: Additional Readings: Descriptive Analytics

Graded: Descriptive Analytics Quiz

WEEK 3


Predictive Analytics



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.


10 videos, 4 readings expand

1. Video: Introduction to Predictive Analytics
2. Video: Asking Predictive Questions
3. Video: Regression Analysis, Part 1: The Demand Curve
4. Video: Regression Analysis, Part 2: Making Predictions
5. Video: Beyond Period 2
6. Video: Making Predictions Using a Data Set
7. Video: Data Set Predictions: Mary, Sharmila, or Chris?
8. Video: Probability Models
9. Video: Implementation of the Model
10. Video: Results and Predictions
11. Reading: Reading: Customer Lifetime Value
12. Reading: Predictive Analytics and Regression Analysis Slides
13. Reading: Implementation of the Model Spreadsheet
14. Reading: Additional Readings: Predictive Analytics

Graded: Predictive Analytics Quiz

WEEK 4


Prescriptive Analytics



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.


7 videos, 1 reading expand

1. Video: Introduction
2. Video: What is Prescriptive Analytics?
3. Video: Using the Data to Maximize Revenue
4. Video: Parameters of the Model
5. Video: Market Structure
6. Video: Competition and Online Advertising Models
7. Video: Conclusion(s)
8. Reading: Prescriptive Analytics Slides

Graded: Prescriptive Analytics Quiz

WEEK 5


Application/Case Studies



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.


8 videos, 2 readings expand

1. Video: Introduction to Application to Analytics
2. Video: The Future of Marketing is Business Analytics
3. Video: The Golden Age of Marketing
4. Video: Applications: ROI
5. Video: Radically New Data Sets in Marketing
6. Video: The Perils of Efficiency
7. Video: Analytics Applied: Kohl's, NetFlix, AmEx and more
8. Video: Conclusion
9. Reading: Application/Case Study Slides
10. Reading: Additional Reading: the Power of Data

Graded: Applications