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Data Science for Marketing Analytics - Second Edition

You're reading from  Data Science for Marketing Analytics - Second Edition

Product type Book
Published in Sep 2021
Publisher Packt
ISBN-13 9781800560475
Pages 636 pages
Edition 2nd Edition
Languages
Authors (3):
Mirza Rahim Baig Mirza Rahim Baig
Profile icon Mirza Rahim Baig
Gururajan Govindan Gururajan Govindan
Profile icon Gururajan Govindan
Vishwesh Ravi Shrimali Vishwesh Ravi Shrimali
Profile icon Vishwesh Ravi Shrimali
View More author details

Table of Contents (11) Chapters

Preface
1. Data Preparation and Cleaning 2. Data Exploration and Visualization 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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