The retention AI model whose first big call was sending fewer promos.
Great at tactics, silent to 97% of the base.
The team was genuinely good at finding small pockets of value: a cohort of paused users worth over $200 ARPU, fewer than 2,000 people, converting at 1%. What did not exist was a strategy for everyone else. 97% of monthly active users never received a communication at all, and when promos did go out, they went out wide, training customers to wait for discounts without moving revenue.
One model choosing each customer's next action. Including none.
A model trained to predict which action would generate the most net revenue from each customer over the following weeks: a nudge to reorder a favorite meal, a new chef worth trying, a new cuisine, a small offer, or silence. Trained the honest way: the active base split into six cohorts receiving randomized actions over six-week sprints, so every lift was measured against customers who got something else, not against last month.
Fewer promos, more revenue, and churn caught before it happened.
This ran while the business roughly doubled, from around $200M toward $400M+ ARR. We claim the measured slices below, not the doubling; the levers changed because the model made their cost visible.
| Measure | Before | With the model |
|---|---|---|
| MAUs receiving lifecycle comms | ~3% | Most of the active base |
| Who gets a discount | Everyone, periodically | The 12% where it pays back |
| Net revenue lift, model-chosen offers | Unmeasured | +28% vs control |
| Churn rate | Baseline | Down ~1.2pts (~10%) |
| Incremental ARR, annualized | n/a | ~$15M |
What held, what needed work.
The discipline survived its biggest test: the model kept saying no to most promos, and revenue kept agreeing with it through peak season pushes.
The model was weak on brand-new customers, who have almost no history to learn from. Later phases leaned on demographic and acquisition features to close the gap.
Training expanded beyond promos into product recommendations, message frequency, and channel choice, and the biggest operational ask became protecting the model from ad-hoc campaigns.
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