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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

Customer Lifetime Value with PyMC Marketing

Customer Lifetime Value (CLV) is a forward-looking approach that allows analysts to estimate the value and profitability of customers. CLV equals the net present value of the future cash flows from a customer. It is used to estimate the value of a customer over the entire relationship with a company. It is used for customer profitability, segmentation, targeting and retention strategies, customer divestment, promotions, advertising, and pricing campaigns.

In this chapter, we will cover the following topics:

  • The fundamentals of CLV and why analysts use them
  • What’s wrong with the CLV formula
  • Beyond the CLV formula
  • Implementing the BTYD model with PyMC Marketing

By the end of this chapter, you will be able to use PyMC Marketing’s CLV classes to predict the lifetime value and purchase frequency of the users in your business, as well as what the pitfalls of a naïve approach are.

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