Case study deck · 9 slides
Subscription
Illustrative scenarioTurning a churn score into a retention system
A mid-market subscription app
Illustrative scenario based on our methodology, not a specific client engagement. Figures are representative targets, not claimed results.
The challenge
What was broken
A churn model scored every account weekly and put the number on a dashboard nobody opened. Retention held in the high 80s throughout, the same range as before the model existed. In one representative week, three accounts crossed the same risk threshold for three different reasons, a paywall hit, a failed payment, a price-increase reaction, and the dashboard showed all three as the same flat number with no owner.
The approach
Scored on the Durable AI Index
We ran the model through the Durable AI Index before touching any code: high impact, high feasibility, stickiness near zero, because the score had no destination. So we built the missing middle layer, a risk-reason classifier tagging why each account is at risk, mapped to a specific pre-approved save play with a named owner and a deadline.
What we built
The system, not just the model
We redesigned the workflow around the model, then built the pieces that make it run every day.
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Risk-reason classifier tagging paywall, billing, price, or support signals
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Four owned save plays, one per reason, each with its own offer
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Routing into the CS queue with an owner and 48-hour SLA
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Feedback loop logging outcomes back into risk-reason accuracy monthly
Inside the build
The save board: risk reason to owned play
Each flagged account matched to the reason it's at risk, the play that reason calls for, and who owns it this week.
Tools & systems
Results
Illustrative scenarioWhat good looks like
From an unused score to a workflow the team runs
Illustrative target: at-risk accounts saved
Clear accountability for the retention metric
Retention gains flow across the whole LTV base
This case is illustrative: a representative composite of the churn-risk-model-plus-unused-dashboard pattern we see repeatedly in subscription businesses, built to show how the Durable AI Index methodology diagnoses and closes a stickiness gap. The figures below model plausible before/after shapes for this scenario, not measured results from a named client.
How it stuck
Adoption is the deliverable
The console stuck because it removed a decision CS was never equipped to make alone: a rep now opens a ticket that already says the reason and the play. Save outcomes roll into the same weekly review as model accuracy, so it isn't a side tool anyone can quietly stop opening.
“A risk score without a reason is a number someone still has to interpret before they can act. We fixed retention by making sure a failed payment and a paywall hit never got the same instructions.”
Ankur Garg, 10dem
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