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Data about our browsing and buying patterns is 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 the future. purchase decisions. In this course, four of Wharton's leading marketing professors will provide an overview of the key areas of customer analytics: descriptive analytics
Data about our browsing and buying patterns is 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 the future.
purchase decisions.
In this course, four of Wharton's leading marketing professors will provide an overview of the key areas of customer analytics: descriptive analytics, predictive analytics, prescriptive analytics, and their application to real-world business practices such as Amazon, Google, and Google. Starbucks, to name a few.
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This course provides an overview of the field of analytics so you can make informed business decisions.
It is an introduction to customer analytics theory, and is not intended to prepare students to conduct customer analytics.
Course Learning Outcomes: After completing the course, learners will be able to…
Describe the main customer data collection methods used by businesses and understand how this data can inform business decisions Describe the main tools used to predict customer behavior and identify appropriate uses for each tool Communicate key insights about customer analytics and how the field informs business decisions Communicate customer analytics history and the latest best practices at top companies
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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; substantive conferences begin in week 3.
In this module, you'll learn what data can and can't describe customer behavior, as well as the most effective methods for collecting data and deciding what it means. You'll understand the fundamental difference between data that describes a causal relationship and data that describes a correlate as you explore the synergy between data and decisions, including 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 will have a solid understanding of effective data collection and interpretation so you can use the right data to make the right decision for your company or business.
Once you have collected and interpreted the data, what do you do with it? In this module, you'll learn how to take the next step: how to use data about past stocks to make predictions about future stocks. You will examine the main tools used to predict behavior and learn how to determine which tool is appropriate for which decision purposes. Additionally, you will learn the language and frameworks for making predictions of future behavior. By the end of this module, you will be able to determine what kinds of predictions you can make to create future strategies, understand the most powerful techniques for predictive modeling, including regression analysis, and be ready to take full advantage of analytics to create effective strategies. data-driven business decisions.
How do you turn data into actions? 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, define your goals, and optimize for success. You'll also examine critical examples of prescriptive models, including how price affects quantity, how to maximize revenue, how to maximize profit, and how to best use online advertising. By the end of this module, you'll be able to define a problem, define a good goal, and explore optimization models that take into account the competition, so you can write recipes for data-driven actions that drive success for your business. or business.
How do major companies make data work? In this module, you'll learn how successful companies use data to create cutting-edge, customer-centric marketing practices. You'll explore real-world examples of the five-pronged attack on applying customer analytics to marketing, beginning with data collection and exploration, moving to predictive modeling and optimization, and continuing to data-driven decisions. By the end of this module, you'll know how best to put data to work in your own company or business, drawing on the most innovative and effective data-driven practices from today's leading companies.
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Professor of Marketing, Statistics, and Education, Chairperson, Wharton Marketing Department, Vice Dean and Director, Wharton Doctoral Program, Co-Director, Wharton Customer Analytics Initiative
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The University of Pennsylvania (commonly known 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 is considered the first university in the United States with undergraduate and graduate studies.
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