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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

Evaluating Clustering

We have seen various ways of performing clustering so far, each approach having its merits. For the same task, we saw that the approaches provided varying results. Which of them is better? Before we answer that, we need to be able to evaluate how good the results from clustering are. Only then can we compare across segmentation approaches. We need to have, therefore, ways to evaluate the quality of clustering.

Another motivation for cluster evaluation methods is the reiteration that clustering is a part of a bigger segmentation exercise, of which clustering is a key part, but far from the whole. Recall from the discussion in the previous chapter that in segmentation exercises, business is often the end consumer of the segments and acts on them. The segments, therefore, need to make sense to the business as well and be actionable. That is why we need to be able to evaluate clusters from a business perspective as well. We have discussed this aspect in the previous...

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