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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

Steps toward implementing MMM

There are several steps involved in implementing MMM:

  1. Define business questions and scope: Like any other analysis, defining the scope and what questions you are trying to answer is critical as a first step. This will help you define what data you need to collect and what data you need to have access to, to get the best value out of an MMM.
  2. Data collection: MMM requires historical data on sales and marketing efforts across different channels, such as television, radio, print, digital, social media, and out-of-home advertising. It takes into account not only the direct effects of marketing activities but also indirect factors such as economic conditions, seasonality, competition, and market changes. Inaccurate or incomplete data will lead to misleading results.
  3. Data analysis and review: Before modeling begins, you need to check the data for any issues, such as missing values, outliers, or data quality issues. This is a critical step as it...
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