Show Notes
Why do churn models produce accurate predictions that never move retention numbers? The position here is that most telcos predict churn the week it happens, when the customer has already price-shopped and gone. Telefonica's work came from years of accumulating unglamorous signals such as degraded data speed at a single cell tower, payment timing drift and the gap between plan and actual usage, combined with contract stage and competitor pricing by postcode.
You walk away able to separate accuracy from uplift, size the movable middle, defend a holdout control group to a revenue VP, and wire a score into a pre-approved retention offer the agent can act on. References include MIT Sloan Management Review on unactionable analytics and dbt Labs on data cleaning time.
0:00 Catching churn twelve hours too late 1:00 Boring signals that predict churn early 1:54 Why years went into data plumbing 2:31 Accurate scores nobody can act on 3:10 Uplift and the movable middle 4:04 Monday check: did you run a control group
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