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Data Analytics for Marketing
Data Analytics for Marketing

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

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Profile Icon Guilherme Diaz-Bérrio
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$24.99 $35.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5 (2 Ratings)
eBook May 2024 452 pages 1st Edition
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$24.99 $35.99
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Arrow left icon
Profile Icon Guilherme Diaz-Bérrio
Arrow right icon
$24.99 $35.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5 (2 Ratings)
eBook May 2024 452 pages 1st Edition
eBook
$24.99 $35.99
Paperback
$44.99
Subscription
Free Trial
Renews at $19.99p/m
eBook
$24.99 $35.99
Paperback
$44.99
Subscription
Free Trial
Renews at $19.99p/m

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Table of content icon View table of contents Preview book icon Preview Book

Data Analytics for Marketing

Part 1: Fundamentals of Analytics

In this part, we will go through the fundamentals of analytics, introducing marketing analytics as a discipline. We will be focusing on data extraction, ingestion, and exploratory data analysis, followed by techniques for effectively presenting results and building dashboards for non-technical audiences. The subsequent discussion shifts toward econometrics and causal inference, providing a foundational understanding of statistics and equipping you with the skills to construct, test, and evaluate statistical models, emphasizing their significance and application in marketing.

This part contains the following chapters:

  • Chapter 1, What is Marketing Analytics?
  • Chapter 2, Extracting and Exploring Data with Singer and pandas
  • Chapter 3, Design Principles and Presenting Results with Streamlit
  • Chapter 4, Econometrics and Causal Inference with Statsmodels and PyMC
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Key benefits

  • Analyze marketing data using proper statistical techniques
  • Use data modeling and analytics to understand customer preferences and enhance strategies without complex math
  • Implement Python libraries like DoWhy, Pandas, and Prophet in a business setting with examples and use cases
  • Purchase of the print or Kindle book includes a free PDF eBook

Description

Most marketing professionals are familiar with various sources of customer data that promise insights for success. There are extensive sources of data, from customer surveys to digital marketing data. Moreover, there is an increasing variety of tools and techniques to shape data, from small to big data. However, having the right knowledge and understanding the context of how to use data and tools is crucial. In this book, you’ll learn how to give context to your data and turn it into useful information. You’ll understand how and where to use a tool or dataset for a specific question, exploring the "what and why questions" to provide real value to your stakeholders. Using Python, this book will delve into the basics of analytics and causal inference. Then, you’ll focus on visualization and presentation, followed by understanding guidelines on how to present and condense large amounts of information into KPIs. After learning how to plan ahead and forecast, you’ll delve into customer analytics and insights. Finally, you’ll measure the effectiveness of your marketing efforts and derive insights for data-driven decision-making. By the end of this book, you’ll understand the tools you need to use on specific datasets to provide context and shape your data, as well as to gain information to boost your marketing efforts.

Who is this book for?

This book is for data analysts and data scientists working in a marketing team supporting analytics and marketing research, who want to provide better insights that lead to data-driven decision-making. Prior knowledge of Python, data analysis, and statistics is required to get the most out of this book.

What you will learn

  • Understand the basic ideas behind the main statistical models used in marketing analytics
  • Apply the right models and tools to a specific analytical question
  • Discover how to conduct causal inference, experimentation, and statistical modeling with Python
  • Implement common open source Python libraries for specific use cases with immediately applicable code
  • Analyze customer lifetime data and generate customer insights
  • Go through the different stages of analytics, from descriptive to prescriptive

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : May 10, 2024
Length: 452 pages
Edition : 1st
Language : English
ISBN-13 : 9781801813839
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Product Details

Publication date : May 10, 2024
Length: 452 pages
Edition : 1st
Language : English
ISBN-13 : 9781801813839
Category :
Languages :
Concepts :

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Table of Contents

19 Chapters
Part 1: Fundamentals of Analytics Chevron down icon Chevron up icon
Chapter 1: What is Marketing Analytics? Chevron down icon Chevron up icon
Chapter 2: Extracting and Exploring Data with Singer and pandas Chevron down icon Chevron up icon
Chapter 3: Design Principles and Presenting Results with Streamlit Chevron down icon Chevron up icon
Chapter 4: Econometrics and Causal Inference with Statsmodels and PyMC Chevron down icon Chevron up icon
Part 2: Planning Ahead Chevron down icon Chevron up icon
Chapter 5: Forecasting with Prophet, ARIMA, and Other Models Using StatsForecast Chevron down icon Chevron up icon
Chapter 6: Anomaly Detection with StatsForecast and PyMC Chevron down icon Chevron up icon
Part 3: Who and What to Target Chevron down icon Chevron up icon
Chapter 7: Customer Insights – Segmentation and RFM Chevron down icon Chevron up icon
Chapter 8: Customer Lifetime Value with PyMC Marketing Chevron down icon Chevron up icon
Chapter 9: Customer Survey Analysis Chevron down icon Chevron up icon
Chapter 10: Conjoint Analysis with pandas and Statsmodels Chevron down icon Chevron up icon
Part 4: Measuring Effectiveness Chevron down icon Chevron up icon
Chapter 11: Multi-Touch Digital Attribution Chevron down icon Chevron up icon
Chapter 12: Media Mix Modeling with PyMC Marketing Chevron down icon Chevron up icon
Chapter 13: Running Experiments with PyMC Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5
(2 Ratings)
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1 star 0%
Praise Ifetogun Jul 12, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book masterfully covers the entire range of marketing analytics, from the basics of extracting and exploring data to advanced topics like econometrics, forecasting, and customer lifetime value analysis. It strikes a perfect balance between theory and practical application, ensuring readers can both understand and implement the techniques discussed.One of the standout features of this book is its clarity and accessibility. Complex concepts are broken down into understandable segments, supported by real-world examples and practical exercises. Whether you're looking to design compelling dashboards with Streamlit or delve into the intricacies of multi-touch digital attribution and media mix modeling, this book provides the tools and knowledge needed to excel. "Data Analytics for Marketing" is not just a read but a comprehensive resource that will stay on your desk as a go-to reference in your analytics journey.
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Seraf Sep 23, 2024
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
A book I have difficulty getting in it. I mean, look at the equation expressions... hard to read. The editor hasn't done its job? But, still readable.
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