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
Этот курс входит в специализацию ''Специализация Бизнес-аналитика'
Анализ потребительского поведения
от партнера
Об этом курсе
4.5
Оценки: 5,509
Приобретаемые навыки
Predictive AnalyticsCustomer AnalyticsRegression AnalysisMarketing Performance Measurement And Management
Программа курса: что вы изучите
Неделя
1Introduction 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 ((всего 4 мин.))
Неделя
2Descriptive 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 ((всего 73 мин.)), 2 материалов для самостоятельного изучения, 1 тест
7 видео
Descriptive Data Collection: Survey Overview16мин
Descriptive Data Collection: Net Promoter Score and Self-Reports11мин
Descriptive Data Collection: Survey Design16мин
Passive Data Collection4мин
Media Planning8мин
Causal Data Collection and Summary8мин
2 материала для самостоятельного изучения
Descriptive Analytics Slides10мин
Additional Readings: Descriptive Analytics10мин
1 практическое упражнение
Descriptive Analytics Quiz20мин
Неделя
3Predictive 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 ((всего 79 мин.)), 4 материалов для самостоятельного изучения, 1 тест
10 видео
Asking Predictive Questions3мин
Regression Analysis, Part 1: The Demand Curve5мин
Regression Analysis, Part 2: Making Predictions5мин
Beyond Period 210мин
Making Predictions Using a Data Set8мин
Data Set Predictions: Mary, Sharmila, or Chris?10мин
Probability Models10мин
Implementation of the Model16мин
Results and Predictions5мин
4 материала для самостоятельного изучения
Reading: Customer Lifetime Value10мин
Predictive Analytics and Regression Analysis Slides10мин
Implementation of the Model Spreadsheet10мин
Additional Readings: Predictive Analytics10мин
1 практическое упражнение
Predictive Analytics Quiz20мин
Неделя
4Prescriptive 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 ((всего 42 мин.)), 1 материал для самостоятельного изучения, 1 тест
7 видео
What is Prescriptive Analytics?4мин
Using the Data to Maximize Revenue6мин
Parameters of the Model10мин
Market Structure9мин
Competition and Online Advertising Models5мин
Conclusion(s)5мин
1 материал для самостоятельного изучения
Prescriptive Analytics Slides10мин
1 практическое упражнение
Prescriptive Analytics Quiz20мин
33%
начал новую карьеру, пройдя эти курсы
30%
получил значимые преимущества в карьере благодаря этому курсу
Лучшие рецензии
автор: ND•Jan 31st 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!
автор: SS•Nov 15th 2016
Amazing course for even beginners in the field of customer analytics. Highly recommend to do this course for enhancing the analytical skills. Examples and case studies explains the concept very well.
Преподавателя
Eric Bradlow
Professor of Marketing, Statistics, and Education, Chairperson, Wharton Marketing Department, Vice Dean and Director, Wharton Doctoral Program, Co-Director, Wharton Customer Analytics InitiativeThe Wharton School
Peter Fader
Professor of Marketing and Co-Director of the Wharton Customer Analytics InitiativeThe Wharton School
Raghu Iyengar
Associate Professor of MarketingThe Wharton School
Ron Berman
Assistant Professor of MarketingThe Wharton School
О Пенсильванский университет
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.
О специализации ''Бизнес-аналитика'
This Specialization provides an introduction to big data analytics for all business professionals, including those with no prior analytics experience. You’ll learn how data analysts describe, predict, and inform business decisions in the specific areas of marketing, human resources, finance, and operations, and you’ll develop basic data literacy and an analytic mindset that will help you make strategic decisions based on data. In the final Capstone Project, you’ll apply your skills to interpret a real-world data set and make appropriate business strategy recommendations.
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