Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Data Analytics for Marketing

You're reading from   Data Analytics for Marketing A practical guide to analyzing marketing data using Python

Arrow left icon
Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781803241609
Length 452 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Guilherme Diaz-Bérrio Guilherme Diaz-Bérrio
Author Profile Icon Guilherme Diaz-Bérrio
Guilherme Diaz-Bérrio
Arrow right icon
View More author details
Toc

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

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)...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image