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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

Getting your data into Streamlit and generating a basic dashboard

Before going through the practicalities of creating a Streamlit app, we need to look into why we are discussing it in the first place. Usually, dashboards are built in a specialized tool such as Power BI, Looker, Data Studio, or Tableau. Some even use Excel, which has excellent capabilities – and can also be overused. Technically, all these options are excellent.

Firstly, the underlying core of this book is Python. The aim is to provide you, the analyst, with a core set of tools and techniques in one language to be able to do all the facets of your job. And that needs to include dashboarding.

On the other hand, there are increasing limitations to dashboards. They are, by their very nature, static. Streamlit allows you to move into building dynamic data apps in a way that the user can interact with data in a manner not possible with a dashboard. Three reasons stand out when evaluating Streamlit:

  • First...
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