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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

Understanding MMM

A media mix model is a statistical analysis technique that’s used by marketers to quantify the effectiveness of different marketing strategies on sales and to predict the outcomes of applying future strategies. This approach is frequently employed to fine-tune advertising expenditures to enhance the return on investment (ROI) or to meet other key marketing objectives.

It is a privacy-friendly approach where you neither have nor need access to user-level data. It is a top-down approach where the model is fitted to aggregated data, and the results are then used to inform future marketing decisions.

Let’s see why we should use MMM:

  • Privacy-friendly and signal-resilient: It is privacy-friendly and resilient to cookie deprecation. Compared to multi-touch attribution, it is less reliant on online signals.
  • Holistic: It takes into account all marketing (both online and offline) and non-marketing activities (seasonality, events, promotions)...
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