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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
2. Chapter 1: What is Marketing Analytics? FREE CHAPTER 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

Customer Insights – Segmentation and RFM

In God we trust. All others bring data.

– Barry Beracha, CEO of Sara Lee Bakery Group

As we venture deeper into the complexities of marketing analytics, we now focus on a critical yet frequently evolving concept: customer dynamics. In this chapter, we seek to accomplish several learning objectives to solidify your understanding and practical application of this essential facet of marketing analytics.

We will dig deeper into the role of specific analytic tools in managing customer dynamics: segmentation and Recency, Frequency, and Monetary Value (RFM) analysis. These tools provide a mathematical and statistical framework to analyze, predict, and influence customer behavior. Our discussion will cover how these tools work and their implications in the broader context of marketing analytics, using Python to illustrate their application.

We aim to explain the benefits and business potential of RFM and segmentation as instrumental...

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