Churn propensity models are sophisticated. What's frequently missing is the organizational ability to act on those models with the speed and specificity that early churn prediction and intervention requires. A VP of Customer Retention needs to know which high-value customers are showing early churn signals, what the likely trigger was, and which intervention has the highest historical success rate for that customer profile — now, not at the next weekly retention review meeting.

Databricks Genie enables retention leaders to interrogate their full customer behavioral and commercial data environment in natural language. A VP of Customer Retention can ask: 'Which premium postpaid customers in the 30-59 age segment have shown usage decline greater than 20% over the past 45 days, have had at least one support contact in that period, and are within 90 days of contract end?' That's a real-time intervention target list — surfaced conversationally from your actual data.

The economics of customer retention in telecom are unambiguous: saving a customer costs a fraction of acquiring a new one, and loyal long-tenure customers generate significantly higher lifetime value. The retention programs that win are the ones that intervene early enough for the intervention to actually change the outcome. Genie gives retention leaders the data access to intervene at the right moment, before the decision is already made.

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