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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

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Colour book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

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Unfortunately, due to several restrictions, we are unable to ship to the following countries:

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Praise Ifetogun Jul 12, 2024

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.

Amazon Verified review

Seraf Sep 23, 2024

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.

Subscriber review

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

What do you get with Print?