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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

Econometrics and Causal Inference with Statsmodels and PyMC

All models are wrong, but some are useful.

– George Box

As a marketing analyst, usually you will not have the luxury of big data to feed into machine learning models. The data will be sparce or made up of low-frequency, time series, or panel data, which will prevent you from brute forcing your way through. You need a solid understanding of econometrics and the principles of causality to answer common questions your stakeholders will have.

As you can recall from Chapter 1, What is Marketing Analytics?, some questions you will encounter in your daily work will be “Why did something happen?”. This is diagnostic analytics. As a marketing data analyst, you will be asked, “What is the impact of X on Y?” or “What is the impact of X on Y, while controlling for Z?” In this chapter, we will cover the basics of how to answer these questions using a linear regression model.

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