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Data Science for Marketing Analytics

You're reading from   Data Science for Marketing Analytics A practical guide to forming a killer marketing strategy through data analysis with Python

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Product type Paperback
Published in Sep 2021
Publisher Packt
ISBN-13 9781800560475
Length 636 pages
Edition 2nd Edition
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Authors (3):
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Vishwesh Ravi Shrimali Vishwesh Ravi Shrimali
Author Profile Icon Vishwesh Ravi Shrimali
Vishwesh Ravi Shrimali
Mirza Rahim Baig Mirza Rahim Baig
Author Profile Icon Mirza Rahim Baig
Mirza Rahim Baig
Gururajan Govindan Gururajan Govindan
Author Profile Icon Gururajan Govindan
Gururajan Govindan
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Toc

Table of Contents (11) Chapters Close

Preface
1. Data Preparation and Cleaning 2. Data Exploration and Visualization FREE CHAPTER 3. Unsupervised Learning and Customer Segmentation 4. Evaluating and Choosing the Best Segmentation Approach 5. Predicting Customer Revenue Using Linear Regression 6. More Tools and Techniques for Evaluating Regression Models 7. Supervised Learning: Predicting Customer Churn 8. Fine-Tuning Classification Algorithms 9. Multiclass Classification Algorithms Appendix

Introduction

The success of a company is highly dependent on its ability to attract new customers while holding on to the existing ones. Churn refers to the situation where a customer of a company stops using its product and leaves the company. Churn can be anything—employee churn from a company, customer churn from a mobile subscription, and so on. Predicting customer churn is important for an organization because acquiring new customers is easy but retaining them is more difficult. Similarly, high employee churn can also affect a company, since the companies spend a huge sum of money on grooming talent. Also, organizations that have high retention rates benefit from consistent growth, which can also lead to high referrals from existing customers. Churn prediction is one of the most common use cases of machine learning.

You learned about supervised learning in the previous chapters, where you gained hands-on experience in solving regression problems. When it comes to predicting...

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