Summary

The beating heart of any product or service business is returning clients. Don't let your hard-won customers vanish, taking their money with them. In Fighting Churn with Data you'll learn powerful data-driven techniques to maximize customer retention and minimize actions that cause them to stop engaging or unsubscribe altogether. This hands-on guide is packed with techniques for converting raw data into measurable metrics, testing hypotheses, and presenting findings that are easily understandable to non-technical decision makers.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology

Keeping customers active and engaged is essential for any business that relies on recurring revenue and repeat sales. Customer turnover—or “churn”—is costly, frustrating, and preventable. By applying the techniques in this book, you can identify the warning signs of churn and learn to catch customers before they leave.

About the book

Fighting Churn with Data teaches developers and data scientists proven techniques for stopping churn before it happens. Packed with real-world use cases and examples, this book teaches you to convert raw data into measurable behavior metrics, calculate customer lifetime value, and improve churn forecasting with demographic data. By following Zuora Chief Data Scientist Carl Gold’s methods, you’ll reap the benefits of high customer retention.

What's inside

    Calculating churn metrics

    Identifying user behavior that predicts churn

    Using churn reduction tactics with customer segmentation

    Applying churn analysis techniques to other business areas

    Using AI for accurate churn forecasting

About the reader

For readers with basic data analysis skills, including Python and SQL.

About the author

Carl Gold (PhD) is the Chief Data Scientist at Zuora, Inc., the industry-leading subscription management platform.

Table of Contents:

    PART 1 - BUILDING YOUR ARSENAL

    1 The world of churn

    2 Measuring churn

    3 Measuring customers

    4 Observing renewal and churn

    PART 2 - WAGING THE WAR

    5 Understanding churn and behavior with metrics

    6 Relationships between customer behaviors

    7 Segmenting customers with advanced metrics

    PART 3 - SPECIAL WEAPONS AND TACTICS

    8 Forecasting churn

    9 Forecast accuracy and machine learning

    10 Churn demographics and firmographics

    11 Leading the fight against churn