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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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Toc

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

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

A/A testing 380

A/B testing

channel worth 338

versus lift analysis 338, 339

versus MMM 338, 339

versus MTA 338, 339

ACF features 130

diff1_acf1 131

diff1_acf10 131

diff2_acf1 131

diff2_acf10 131

Seasonal ACF 131

x_acf1 131

x_acf10 131

acquisition, expansion, and retention (AER) 94

adstock effect

measuring 345

adstock first, rule of thumb 352

adstock model 345, 346

types 346

advanced forecasting methods 144

ARIMA models 149

ETS models 146

model selection 153-155

Prophet model 152

regression models, extending to time series 144-146

Advocacy Loyalty Index (ALI) 282

Akaike information criterion (AIC) 143

algorithm

versus model 77

algorithmic attribution models 317

Fractribution 326

Shapley value...

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