Customer Analytics (Coursera)
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.
Customer Analytics is course 1 of 5 in the Business Analytics Specialisation.
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.
Week 1: Introduction to Customer Analytics
Week 2: Descriptive Analytics
Week 3: Predictive Analytics
Week 4: Prescriptive Analytics
Week 5: Application/Case Studies
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