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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

Segmentation

Segmentation, simply put, means grouping similar entities together. The entities of each group are similar to each other, that is, "the groups are homogenous," meaning the entities have similar properties. Before going further, we need to understand two key aspects here – entities and properties.

What entities can be segmented? You can segment customers, products, offers, vehicles, fruits, animals, countries, or even stars. If you can express, through data, the properties of the entity, you can compare that entity with other entities and segment it. In this chapter, we will focus on customer segmentation – that is, grouping and segmenting present/potential customers, an exercise that has tremendous utility in marketing.

Coming to the second key aspect, what properties are we talking about? We are talking about properties relevant to the grouping exercise. Say you are trying to group customers based on their purchase frequency of a product...

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