Book Image

The Art of Data-Driven Business

By : Alan Bernardo Palacio
Book Image

The Art of Data-Driven Business

By: Alan Bernardo Palacio

Overview of this book

One of the most valuable contributions of data science is toward helping businesses make the right decisions. Understanding this complicated confluence of two disparate worlds, as well as a fiercely competitive market, calls for all the guidance you can get. The Art of Data-Driven Business is your invaluable guide to gaining a business-driven perspective, as well as leveraging the power of machine learning (ML) to guide decision-making in your business. This book provides a common ground of discussion for several profiles within a company. You’ll begin by looking at how to use Python and its many libraries for machine learning. Experienced data scientists may want to skip this short introduction, but you’ll soon get to the meat of the book and explore the many and varied ways ML with Python can be applied to the domain of business decisions through real-world business problems that you can tackle by yourself. As you advance, you’ll gain practical insights into the value that ML can provide to your business, as well as the technical ability to apply a wide variety of tried-and-tested ML methods. By the end of this Python book, you’ll have learned the value of basing your business decisions on data-driven methodologies and have developed the Python skills needed to apply what you’ve learned in the real world.
Table of Contents (17 chapters)
Part 1: Data Analytics and Forecasting with Python
Part 2: Market and Customer Insights
Part 3: Operation and Pricing Optimization

Predicting Customer Churn

The churn rate is a metric used to determine how many clients or staff leave a business in a certain time frame. It might also refer to the sum of money that was lost because of the departures. Changes in a company’s churn rate might offer insightful information about the firm. Understanding the amount or proportion of consumers who don’t buy more goods or services is possible through customer churn analysis.

In this chapter, we will understand the concept of churn and why it is important in the context of business. We will then prepare the data for further analysis and create an analysis to determine the most important factors to take into account to understand the churn patterns. Finally, we will learn how to create machine learning models to predict customers that will churn.

This chapter covers the following topics:

  • Understanding customer churn
  • Exploring customer data
  • Exploring variable relationships
  • Predicting users...