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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

Azra, a large, high-end, fast-fashion retailer that has operations all over the world, has approved its marketing budget for the latest campaign in a particular country. The marketing team is now looking to allocate the budget to each marketing channel, but they have many questions:

  • How much should they spend on email? Read rates are low, but the quality of conversions is high.
  • How about social media? It seems to be an effective channel in general.
  • Should they do any offline promotions? If so, to what extent?
  • How about paid search as a channel?

The company understands that each channel provides a different return on investment (ROI) – that is, some channels are more effective than others. However, all channels should be considered, nonetheless. Naturally, different distributions of allocation to these channels would provide different results. Incredibly low spending on a channel with great ROI is missed potential and high spending on an extremely...

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