Informações sobre o curso: 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 brand new course, four of Wharton’s top marketing professors will dive deeper into the 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. By the end of this course, you’ll understand how data is used to describe, explain, and predict customer behavior; best practices for using this data to prescribe more effective business strategies; and how successful companies are currently using such data to meet (and shape) customer needs, and to increase their bottom line in the process. This course has been designed to help you make better business decisions about emerging data structures, so that you can apply what you’ve learned to your own company or business today.
Desenvolvido por: University of Pennsylvania
Ministrado por: 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
Ministrado por: Peter Fader, Professor of Marketing and Co-Director of the Wharton Customer Analytics Initiative
The Wharton School
Ministrado por: Raghu Iyengar, Associate Professor of Marketing
The Wharton School
Ministrado por: Ron Berman, Assistant Professor of Marketing
The Wharton School
| Informações básicas | Curso 1 de 5 no Análises Empresariais Specialization |
| Compromisso | 4 weeks of study, 5-6 hours/week |
| Idioma | English, Legendas: Spanish, Chinese (Simplified) |
| Como ser aprovado | Seja aprovado em todas as tarefas para concluir o curso. |
Programa
SEMANA 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
- Vídeo: Overview of the Business Analytics Specialization
SEMANA 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
- Vídeo: Descriptive Data Collection: Survey Overview
- Vídeo: Descriptive Data Collection: Net Promoter Score and Self-Reports
- Vídeo: Descriptive Data Collection: Survey Design
- Vídeo: Passive Data Collection
- Vídeo: Media Planning
- Vídeo: Causal Data Collection and Summary
- Lecture: Descriptive Analytics Slides
- Lecture: Additional Readings: Descriptive Analytics
Nota atribuída: Descriptive Analytics Quiz
SEMANA 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
- Vídeo: Asking Predictive Questions
- Vídeo: Regression Analysis, Part 1: The Demand Curve
- Vídeo: Regression Analysis, Part 2: Making Predictions
- Vídeo: Beyond Period 2
- Vídeo: Making Predictions Using a Data Set
- Vídeo: Data Set Predictions: Mary, Sharmila, or Chris?
- Vídeo: Probability Models
- Vídeo: Implementation of the Model
- Vídeo: Results and Predictions
- Lecture: Reading: Customer Lifetime Value
- Lecture: Predictive Analytics and Regression Analysis Slides
- Lecture: Implementation of the Model Spreadsheet
- Lecture: Additional Readings: Predictive Analytics
Nota atribuída: Predictive Analytics Quiz
SEMANA 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
- Vídeo: What is Prescriptive Analytics?
- Vídeo: Using the Data to Maximize Revenue
- Vídeo: Parameters of the Model
- Vídeo: Market Structure
- Vídeo: Competition and Online Advertising Models
- Vídeo: Conclusion(s)
- Lecture: Prescriptive Analytics Slides
Nota atribuída: Prescriptive Analytics Quiz
SEMANA 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
- Vídeo: The Future of Marketing is Business Analytics
- Vídeo: The Golden Age of Marketing
- Vídeo: Applications: ROI
- Vídeo: Radically New Data Sets in Marketing
- Vídeo: The Perils of Efficiency
- Vídeo: Analytics Applied: Kohl's, NetFlix, AmEx and more
- Vídeo: Conclusion
- Lecture: Application/Case Study Slides
- Lecture: Additional Reading: the Power of Data
Nota atribuída: Applications
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Desenvolvedores
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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