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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

Introduction

A large e-commerce company is gearing up for its biggest event for the year – its annual sale. The company is ambitious in its goals and aims to achieve the best sales figures so far, hoping for significant growth over last year's event. The marketing budget is the highest it has ever been. Naturally, marketing campaigns will be a critical factor in deciding the success of the event. From what we have learned so far, we know that for those campaigns to be most effective, an understanding of the customers and choosing the right messaging for them is critical.

In such a situation, well-performed customer segmentation can make all the difference and help maximize the ROI (Return on Investment) of marketing spend. By analyzing customer segments, the marketing team can carefully define strategies for each segment. But before investing precious resources into a customer segmentation project, data science teams, as well as business teams, need to answer a few key...

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