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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

Product type Paperback

Published in Sep 2021

Publisher Packt

ISBN-13 9781800560475

Length 636 pages

Edition 2nd Edition

Languages

Python

Tools

GDI

Concepts

Data Analysis

Authors (3):

Vishwesh Ravi Shrimali

Mirza Rahim Baig

Gururajan Govindan

View More author details

Table of Contents (11) Chapters

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

Regression Problems

The prediction of quantities is a recurring task in marketing. Predicting the units sold for a brand based on the spend on the visibility (impressions) allocated to the brand's products is an example. Another example could be predicting sales based on the advertising spend on television campaigns. Predicting the lifetime value of a customer (the total revenue a customer brings over a defined period) based on a customer's attributes is another common requirement. All these situations can be formulated as regression problems.

Note

A common misconception is that regression is a specific algorithm/technique. Regression is a much broader term that refers to a class of problems. Many equate regression to linear regression, which is only one of the many techniques that can be employed to solve a regression problem.

Regression refers to a class of problems where the value to predict is a quantity. There are various techniques available for regression...

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Authors (3)

Mirza Rahim Baig

Mirza Rahim Baig

Mirza Rahim Baig is a Data Science and Artificial Intelligence leader with over 13 years of experience across e-commerce, healthcare, and marketing. He currently holds the position of leading Product Analytics at Marketing Services for Zalando, Europe's largest online fashion platform. In addition, he serves as a Subject Matter Expert and faculty member for MS level programs at prominent Ed-Tech platforms and institutes in India. He is also the lead author of two books, 'Data Science for Marketing Analytics' and 'The Deep Learning Workshop,' both published by Packt. He is recognized as a thought leader in my field and frequently participates as a guest speaker at various forums.

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Vishwesh Ravi Shrimali

Vishwesh Ravi Shrimali

Vishwesh Ravi Shrimali graduated from BITS Pilani, where he studied mechanical engineering, in 2018. He also completed his Masters in Machine Learning and AI from LJMU in 2021. He has authored - Machine learning for OpenCV (2nd edition), Computer Vision Workshop and Data Science for Marketing Analytics (2nd edition) by Packt. When he is not writing blogs or working on projects, he likes to go on long walks or play his acoustic guitar.

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Gururajan Govindan

Gururajan Govindan

Gururajan Govindan is a data scientist, intrapreneur, and trainer with more than seven years of experience working across domains such as finance and insurance. He is also an author of The Data Analysis Workshop, a book focusing on data analytics. He is well known for his expertise in data-driven decision-making and machine learning with Python.

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