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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

Beyond simple pivot tables

You might wonder why we need a book on Marketing Analytics Using Python. Surely you can do the same thing using the trustworthy combination of Excel, some VLOOKUPs, and some PivotTables. This is a widespread misunderstanding, and the problem stems from not realizing what the entire analytical process should look like and why. The following diagram shows the process in a simplified way:

Figure 1.3 – Analytical process

Figure 1.3 – Analytical process

As an analyst, you should have the preceding workflow that will generally go through the following tasks:

  1. You should, first and foremost, scope out the question. You need to understand what is being asked of you clearly. Remember that your stakeholders have immense business knowledge and a problem they need to solve, but more often than not, the question might not be clearly defined.
  2. You must extract the correct datasets to explore the problem space. This might be as easy as extracting a CSV from...
You have been reading a chapter from
Data Analytics for Marketing
Published in: May 2024
Publisher: Packt
ISBN-13: 9781803241609
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