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Data Analytics for Marketing

Data Analytics for Marketing

By : Guilherme Diaz-Bérrio
4.5 (2)
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Data Analytics for Marketing

Data Analytics for Marketing

4.5 (2)
By: Guilherme Diaz-Bérrio

Overview of this book

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.
Table of Contents (20 chapters)
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1
Part 1: Fundamentals of Analytics
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6
Part 2: Planning Ahead
9
Part 3: Who and What to Target
14
Part 4: Measuring Effectiveness

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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Data Analytics for Marketing
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