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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

An overview of marketing analytics

The quote at the beginning of this chapter illustrates one of the fundamental questions of the marketing manager in their day-to-day activities. The best way to evaluate where to spend and target their efforts to achieve their ultimate target is to obtain new customers or retain current ones.

Marketing analytics is nothing more than the application of analytical methods to said goal, bringing a quantifiable way of guiding investment or consumer targeting decisions. As with any new and growing domain, it is hard to pin an exact definition of it, but we can define it as a “technology-enabled and model-supported approach to harness customer and market data to enhance marketing decision making”. Being a domain in the larger field of data analytics, it looks to use mathematics and statistics together with computational tools and techniques to find meaningful patterns and knowledge in data. In this book, we will strive to focus on only...

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