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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
6
Part 2: Planning Ahead
9
Part 3: Who and What to Target
14
Part 4: Measuring Effectiveness

Why Python?

Python offers a marketing analyst many benefits. First, it is an easy but powerful programming language with a great ecosystem of tools and libraries for data analysis and statistics. Second, as a programming language, it is easily testable, and the code can be made in such a way as to be generalizable and reusable. Do not underestimate this second point. Reusability is a great asset to have. You can reuse them for other datasets or testing purposes, which will massively increase your productivity in the medium to long term. Third, it handles massive amounts of data with modern libraries such as pandas and NumPy. The limit is essentially the physical memory in your machine.

Some of you might wonder, “Why not R?”. It is a matter of personal preference. Most marketing analytics was derived from the field of applied econometrics. R is one of the prime tools in econometrics and statistics. As a language, it was built for statisticians who did not want to learn...

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Data Analytics for Marketing
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