The customer most able to pay was the first to leave.
- The four real shock events (paper loss, mandate expiry, stipend delay, social trigger)
- The income and savings profile of each buyer segment
- The AutoPay mandate mechanics
- The retention question the product team faced
- Each saver's churn risk under each shock
- The day-level timing of disengagement
- The share of churn that was friction- vs intent-driven
- Which shock produced the widest impact
Everyone assumed the customers most likely to drop out were the ones who could least afford to save.
A savings product wanted to know which of its AutoPay savers were about to lapse, so it could intervene. The going-in belief was the obvious one: lower income means thinner margins means higher churn risk. Eight synthetic savers, spanning ₹9,500 to ₹48,000 a month, were put through four shock events — a small paper loss, a mandate expiry, a stipend delay, a social trigger — to see who broke and when.
Eight savers, each with a different reason the habit mattered.
The personas weren't income brackets. Each carried a distinct saving identity — a survival discipline, a performance metric, a household duty — and that identity, not the balance, decided how each one absorbed a shock.
Income didn't predict churn. Saving identity did — and most churn was accidental.
The mandate expired before the intention to save did.
The product treated churn as a decision — someone choosing to stop. The simulation found the opposite: six of eight lapses were friction, not intent. A mandate quietly expired, or a small loss bruised a saver whose whole relationship with the product was about feeling successful, and the habit broke before anyone decided anything. Meena's identity was survival discipline, so a shock made her save harder; Vikram's was performance, so a trivial paper loss read as failure and he walked. The lever isn't a richer incentive for low-income savers — it's removing the friction that lets willing savers fall out by accident.
Stop targeting retention spend by income tier. Re-sequence the mandate-renewal flow so expiry can't silently drop a willing saver, and treat identity-disruption moments (a visible loss for a performance-motivated saver) as the real intervention trigger — not the low balance the dashboard flags.
These are modelled outcomes, not measured ones. Every score and probability here is a synthetic-population prediction, produced before any real-market test. The panel is eight savers across four shocks — built to isolate the identity mechanism, not to size a book. Treat the numbers as directional — the direction and size of the effect is the finding, not a forecast. A design partner validates the top interventions against real behaviour before a decision commits to them.
“The lever isn't motivation. It's friction — most of the people who left still wanted to save.”SIMULATION SYNTHESIS · SHOCK-RESPONSE CLUSTER
Run your version of this decision.
This is the real engine. Bring the thing you're weighing and see how the population responds — before you commit to it.