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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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Toc

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

Beyond the CLV formula

What methods can we employ to refine the calculation of CLV? A logical first step is to revisit the definition of CLV, understood as “the present worth of the anticipated cash flows from a customer relationship,” which is mathematically represented as follows:

E(CLV) =  t expected net cashflow in period t|alive × P(alive in period t) × discount factor for period t

The formula is basically the expected net cashflow in period t, given that the customer is alive times the probability of the customer being alive at time t times a discount factor for period t.

The formula, as is, is of no practical use. We need to operationalize the terms. The best way to start with that is to look into the Buy Till You Die (BTYD) model for a solution.

The BTYD model

Before we delve into the BTYD model, we need to understand customer base classifications. Customer bases can be classified into two axes:

  • Opportunities...
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