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Data Science for Marketing Analytics

Data Science for Marketing Analytics - Second Edition

By : Mirza Rahim Baig , Gururajan Govindan , Vishwesh Ravi Shrimali
4.3 (203)
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Data Science for Marketing Analytics

Data Science for Marketing Analytics

4.3 (203)
By: Mirza Rahim Baig , Gururajan Govindan , Vishwesh Ravi Shrimali

Overview of this book

Unleash the power of data to reach your marketing goals with this practical guide to data science for business. This book will help you get started on your journey to becoming a master of marketing analytics with Python. You'll work with relevant datasets and build your practical skills by tackling engaging exercises and activities that simulate real-world market analysis projects. You'll learn to think like a data scientist, build your problem-solving skills, and discover how to look at data in new ways to deliver business insights and make intelligent data-driven decisions. As well as learning how to clean, explore, and visualize data, you'll implement machine learning algorithms and build models to make predictions. As you work through the book, you'll use Python tools to analyze sales, visualize advertising data, predict revenue, address customer churn, and implement customer segmentation to understand behavior. By the end of this book, you'll have the knowledge, skills, and confidence to implement data science and machine learning techniques to better understand your marketing data and improve your decision-making.
Table of Contents (11 chapters)
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Preface

Summary

In this chapter, we explored the idea of segmentation and its utility for business. We discussed the key considerations in segmentation, namely, criteria/features and the interpretation of the segments. We first discussed and implemented a traditional approach to customer segmentation. Noting its drawbacks, we then explored and performed unsupervised machine learning for customer segmentation. We established how to think about the similarity in the customer data feature space, and also learned the importance of standardizing data if it is on very different scales. Finally, we learned about k-means clustering – a commonly used, fast, and easily scalable clustering algorithm. We employed these concepts and techniques to help a mall understand its customers better using segmentation. We also helped a bank identify customer segments and how they have responded to previous marketing campaigns.

In this chapter, we used predefined values for the number of groups we asked...

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