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Hands-On Data Science for Marketing

You're reading from   Hands-On Data Science for Marketing Improve your marketing strategies with machine learning using Python and R

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781789346343
Length 464 pages
Edition 1st Edition
Languages
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Author (1):
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Yoon Hyup Hwang Yoon Hyup Hwang
Author Profile Icon Yoon Hyup Hwang
Yoon Hyup Hwang
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Table of Contents (20) Chapters Close

Preface 1. Section 1: Introduction and Environment Setup
2. Data Science and Marketing FREE CHAPTER 3. Section 2: Descriptive Versus Explanatory Analysis
4. Key Performance Indicators and Visualizations 5. Drivers behind Marketing Engagement 6. From Engagement to Conversion 7. Section 3: Product Visibility and Marketing
8. Product Analytics 9. Recommending the Right Products 10. Section 4: Personalized Marketing
11. Exploratory Analysis for Customer Behavior 12. Predicting the Likelihood of Marketing Engagement 13. Customer Lifetime Value 14. Data-Driven Customer Segmentation 15. Retaining Customers 16. Section 5: Better Decision Making
17. A/B Testing for Better Marketing Strategy 18. What's Next? 19. Other Books You May Enjoy

Summary

In this chapter, we have learned about customer churn and retention. We have discussed the reasons why customer churn hurts businesses. More specifically, we have learned how retaining existing customers is much less expensive than acquiring new customers. We have shown some of the common reasons why customers leave a company, such as poor customer service, not finding enough value in products or services, lack of communications, and lack of customer loyalty. In order to understand why customers leave, we could conduct surveys or analyze customer data to understand their needs and pain points better. We have also discussed how we can train ANN models to identify those customers who are at risk of churning. Through programming exercises, we have learned how to use the keras library to build and train ANN models in Python and R.

In the following chapter, we are going to...

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