Об этом курсе: 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
Автор: University of Pennsylvania
Преподаватели: 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
Преподаватели: Peter Fader, Professor of Marketing and Co-Director of the Wharton Customer Analytics Initiative
The Wharton School
Преподаватели: Raghu Iyengar, Associate Professor of Marketing
The Wharton School
Преподаватели: Ron Berman, Assistant Professor of Marketing
The Wharton School
| Основные сведения | Course 1 of 5 in the Business Analytics Specialization |
| Выполнение | 4 weeks of study, 5-6 hours/week |
| Язык | English, Субтитры: Spanish, Chinese (Simplified) |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
Программа курса
НЕДЕЛЯ 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 видео
- Видео: Overview of the Business Analytics Specialization
НЕДЕЛЯ 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 видео, 2 материала для самостоятельного изучения
- Видео: Descriptive Data Collection: Survey Overview
- Видео: Descriptive Data Collection: Net Promoter Score and Self-Reports
- Видео: Descriptive Data Collection: Survey Design
- Видео: Passive Data Collection
- Видео: Media Planning
- Видео: Causal Data Collection and Summary
- Reading: Descriptive Analytics Slides
- Reading: Additional Readings: Descriptive Analytics
Оцениваемый: Descriptive Analytics Quiz
НЕДЕЛЯ 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 видео, 4 материала для самостоятельного изучения
- Видео: Asking Predictive Questions
- Видео: Regression Analysis, Part 1: The Demand Curve
- Видео: Regression Analysis, Part 2: Making Predictions
- Видео: Beyond Period 2
- Видео: Making Predictions Using a Data Set
- Видео: Data Set Predictions: Mary, Sharmila, or Chris?
- Видео: Probability Models
- Видео: Implementation of the Model
- Видео: Results and Predictions
- Reading: Reading: Customer Lifetime Value
- Reading: Predictive Analytics and Regression Analysis Slides
- Reading: Implementation of the Model Spreadsheet
- Reading: Additional Readings: Predictive Analytics
Оцениваемый: Predictive Analytics Quiz
НЕДЕЛЯ 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 видео, 1 материал для самостоятельного изучения
- Видео: What is Prescriptive Analytics?
- Видео: Using the Data to Maximize Revenue
- Видео: Parameters of the Model
- Видео: Market Structure
- Видео: Competition and Online Advertising Models
- Видео: Conclusion(s)
- Reading: Prescriptive Analytics Slides
Оцениваемый: Prescriptive Analytics Quiz
НЕДЕЛЯ 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 видео, 2 материала для самостоятельного изучения
- Видео: The Future of Marketing is Business Analytics
- Видео: The Golden Age of Marketing
- Видео: Applications: ROI
- Видео: Radically New Data Sets in Marketing
- Видео: The Perils of Efficiency
- Видео: Analytics Applied: Kohl's, NetFlix, AmEx and more
- Видео: Conclusion
- Reading: Application/Case Study Slides
- Reading: Additional Reading: the Power of Data
Оцениваемый: Applications
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Авторы
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.
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