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Data Analytics for Marketing

You're reading from   Data Analytics for Marketing A practical guide to analyzing marketing data using Python

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781803241609
Length 452 pages
Edition 1st Edition
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Author (1):
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Guilherme Diaz-Bérrio Guilherme Diaz-Bérrio
Author Profile Icon Guilherme Diaz-Bérrio
Guilherme Diaz-Bérrio
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Table of Contents (20) Chapters Close

Preface 1. Part 1: Fundamentals of Analytics FREE CHAPTER
2. Chapter 1: What is Marketing Analytics? 3. Chapter 2: Extracting and Exploring Data with Singer and pandas 4. Chapter 3: Design Principles and Presenting Results with Streamlit 5. Chapter 4: Econometrics and Causal Inference with Statsmodels and PyMC 6. Part 2: Planning Ahead
7. Chapter 5: Forecasting with Prophet, ARIMA, and Other Models Using StatsForecast 8. Chapter 6: Anomaly Detection with StatsForecast and PyMC 9. Part 3: Who and What to Target
10. Chapter 7: Customer Insights – Segmentation and RFM 11. Chapter 8: Customer Lifetime Value with PyMC Marketing 12. Chapter 9: Customer Survey Analysis 13. Chapter 10: Conjoint Analysis with pandas and Statsmodels 14. Part 4: Measuring Effectiveness
15. Chapter 11: Multi-Touch Digital Attribution 16. Chapter 12: Media Mix Modeling with PyMC Marketing 17. Chapter 13: Running Experiments with PyMC 18. Index 19. Other Books You May Enjoy

Why Python?

Python offers a marketing analyst many benefits. First, it is an easy but powerful programming language with a great ecosystem of tools and libraries for data analysis and statistics. Second, as a programming language, it is easily testable, and the code can be made in such a way as to be generalizable and reusable. Do not underestimate this second point. Reusability is a great asset to have. You can reuse them for other datasets or testing purposes, which will massively increase your productivity in the medium to long term. Third, it handles massive amounts of data with modern libraries such as pandas and NumPy. The limit is essentially the physical memory in your machine.

Some of you might wonder, “Why not R?”. It is a matter of personal preference. Most marketing analytics was derived from the field of applied econometrics. R is one of the prime tools in econometrics and statistics. As a language, it was built for statisticians who did not want to learn...

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