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Predict the Future, Save Revenue Today

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Reducing passive churn, identifying readers at risk of churning, and modeling propensity to churn are the focuses of this module featuring case studies from Telegraph Media Group, Business Insider, and Deep.BI. The module looks at predicting churn before readers even think of leaving, fixing passive churn so you never lose a reader by accident, and handling breakups gracefully but smartly.


Getting Payments Right and Saving Revenue by Reducing Passive Churn
Elaine Scott, Director, Engagement and Retention, Telegraph Media Group

Identifying Readers At Risk of Churningand Acting On It: From Easy Victories to Long-Term Initiatives
Selma Stern, Senior Vice-President, Consumer Subscriptions, Business Insider

Primer On Modeling Propensity to Churn: Methods and Practical Applications
Michael Ciesielczyk, Head of AI Engineering, Deep.BI 

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