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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

Experimentation

For a randomized experiment, you split the users into two groups (test and control), and then you apply the treatment to the test group, and you measure the conversion rate of both groups. Random selection of who is in each group is essential and ensures that the groups are comparable and that you are controlling for unobserved factors.

To find the credible interval of lift, we can do the following:

Pr(a < L < b) =  a<L<b L(R t, R c)Pr(R t, R c)d R t d R c

If the random experiment is done correctly, then the distribution of R t and R c is independent, and we can write the following:

Pr(R t, R c) = Pr(R t| k t, n t)Pr(R c| k c, n c)

Now, we can simulate. We know that rates R c and R t will follow a beta distribution, so we can simulate a large number of values for R c and R t, and then calculate the lift for each pair of...

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