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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
Languages
Tools
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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

Approaches to Segmentation

Every marketing group does, in effect, some amount of customer segmentation. However, the methods they use to do this might not always be clear. These may be based on intuitions and hunches about certain demographic groups, or they might be the output of some marketing software, where the methods used are obscure. There are advantages and disadvantages to every possible method and understanding them allows you to make use of the right tool for the job. In the following sections, we will discuss some of the most commonly used approaches for customer segmentation along with considerations when using such approaches.

Traditional Segmentation Methods

A preferred method for marketing analysts consists of coming up with rough groupings based on intuitions and arbitrary thresholds. For this, they leverage whatever data about customers they have at their disposal – typically demographic or behavioral. An example of this would be deciding to segment customers...

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