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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 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

What is Marketing Analytics?

Half the money I spend on advertising is wasted; the trouble is I don’t know which half.

– John Wanamaker, the forefather of marketing

In this chapter, we will attempt to cover the fundamentals of marketing analytics as a role and discipline. As a marketing analyst, you are faced with common questions during your day-to-day activities. For example, “How did this campaign perform?” or “How can you optimize your budget to achieve a result?”.

In this chapter, we will break down the types of analytics (from descriptive to prescriptive), the value they add to a business, and the questions each of them answers.

You will learn about the following topics:

  • What is analytics?
  • An overview of marketing analytics
  • Exploring different types of analytics
  • Beyond simple pivot tables
  • Why Python?
  • Modern challenges in the world of privacy-centric marketing
  • The importance of data engineering...
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