Data Analytics for Marketing: A practical guide to analyzing marketing data using Python
1145438567
Data Analytics for Marketing: A practical guide to analyzing marketing data using Python
35.99 In Stock
Data Analytics for Marketing: A practical guide to analyzing marketing data using Python

Data Analytics for Marketing: A practical guide to analyzing marketing data using Python

by Guilherme Diaz-Bérrio
Data Analytics for Marketing: A practical guide to analyzing marketing data using Python

Data Analytics for Marketing: A practical guide to analyzing marketing data using Python

by Guilherme Diaz-Bérrio

eBook

$35.99 

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Product Details

ISBN-13: 9781801813839
Publisher: Packt Publishing
Publication date: 05/10/2024
Sold by: Barnes & Noble
Format: eBook
Pages: 452
File size: 18 MB
Note: This product may take a few minutes to download.

About the Author

Guilherme Diaz-Bérrio is the Head of Marketing Analytics at Kindred Group, one of the 10 largest gambling operators. He helps improve marketing efforts across various platforms. His career started in finance at a hedge fund and moved through the automotive industry at BMW Group and BMW Financial Services, before coming to Kindred Group. He graduated with a degree in economics from ISEG, University of Lisbon, and has additional training in data science and econometrics. He is also the co-founder of Pinemarsh, a data analytics and digital marketing consulting firm.

Table of Contents

Table of Contents
  1. What is Marketing Analytics?
  2. Extracting and Exploring Data with Singer and pandas
  3. Design Principles and Presenting Results with Streamlit
  4. Econometrics and Causal Inference with Statsmodels and PyMC
  5. Forecasting with Prophet, ARIMA, and Other Models Using StatsForecast
  6. Anomaly Detection with StatsForecast and PyMC
  7. Customer Insights – Segmentation and RFM
  8. Customer Lifetime Value with PyMC Marketing
  9. Customer Survey Analysis
  10. Conjoint Analysis with pandas and Statsmodels
  11. Multi-Touch Digital Attribution
  12. Media Mix Modeling with PyMC Marketing
  13. Running Experiments with PyMC
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