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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

What is an anomaly?

An anomaly is an observation that deviates so much from the expected behavior that it raises suspicions that it was generated by a different data-generating process or mechanism. In other words, an anomaly is an observation that is not expected to happen and that is not expected to happen again.

Anomalies can be due to noise or erroneous data, or they can be due to a change in the data-generating process. A good example of an anomaly due to noise or erroneous data is a sudden drop in conversions from a marketing campaign due to a loss in web tracking. You, as an analyst, want to be able to detect this anomaly and understand that it is due to a loss in tracking, and not due to a change in the data-generating process. Another type of anomaly could be a steep drop in the conversion rate due to a change in the rules that allow your ads to be shown. You still want to detect this anomaly, but here you also want to understand that the data-generating process has changed...

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