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The Art of Data-Driven Business

You're reading from   The Art of Data-Driven Business Transform your organization into a data-driven one with the power of Python machine learning

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Product type Paperback
Published in Dec 2022
Publisher Packt
ISBN-13 9781804611036
Length 314 pages
Edition 1st Edition
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Author (1):
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Alan Bernardo Palacio Alan Bernardo Palacio
Author Profile Icon Alan Bernardo Palacio
Alan Bernardo Palacio
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Table of Contents (17) Chapters Close

Preface 1. Part 1: Data Analytics and Forecasting with Python
2. Chapter 1: Analyzing and Visualizing Data with Python FREE CHAPTER 3. Chapter 2: Using Machine Learning in Business Operations 4. Part 2: Market and Customer Insights
5. Chapter 3: Finding Business Opportunities with Market Insights 6. Chapter 4: Understanding Customer Preferences with Conjoint Analysis 7. Chapter 5: Selecting the Optimal Price with Price Demand Elasticity 8. Chapter 6: Product Recommendation 9. Part 3: Operation and Pricing Optimization
10. Chapter 7: Predicting Customer Churn 11. Chapter 8: Grouping Users with Customer Segmentation 12. Chapter 9: Using Historical Markdown Data to Predict Sales 13. Chapter 10: Web Analytics Optimization 14. Chapter 11: Creating a Data-Driven Culture in Business 15. Index 16. Other Books You May Enjoy

Predicting Customer Churn

The churn rate is a metric used to determine how many clients or staff leave a business in a certain time frame. It might also refer to the sum of money that was lost because of the departures. Changes in a company’s churn rate might offer insightful information about the firm. Understanding the amount or proportion of consumers who don’t buy more goods or services is possible through customer churn analysis.

In this chapter, we will understand the concept of churn and why it is important in the context of business. We will then prepare the data for further analysis and create an analysis to determine the most important factors to take into account to understand the churn patterns. Finally, we will learn how to create machine learning models to predict customers that will churn.

This chapter covers the following topics:

  • Understanding customer churn
  • Exploring customer data
  • Exploring variable relationships
  • Predicting users...
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