Conduct data-driven marketing research and analysis with hands-on examples using Python by leveraging open-source tools and libraries
Key Features

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

Book 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.
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