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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

Diving deeper into CLV

CLV is 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. One needs to realize that not all customers are equally profitable. The idea behind CLV is fairly intuitive. Once you acquire a customer, you will earn some revenue from services sold to that customer. You will also spend some money on that customer – for example, on marketing, advertising, customer service, and so on. The difference between the revenue and the costs is the profit. The profit is the cash flow from that customer. The CLV is the net present value of all the future cash flows from that customer. Quantifying the CLV can be challenging because you will need to forecast the stream of cash flows generated by the customer over its entire lifetime. This is a difficult task because you will need to make assumptions about the future. You will need to make assumptions about the future revenue...

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