Deployed · cadence-pricedCertifiedPredict what's next
Contract Renewals
“Which customers are coming back?”
Which customers are most likely to renew at their date — each one scored against their own calendar.
Survival-informed (tenure-aware)
01
What you receive
Every renewal, ranked by likelihood to return
Visual placeholderRanked list · scoredEvery renewal, ranked by likelihood to return
A ranked list of customers approaching renewal, each scored by how likely they are to renew — against their own calendar. Delivered to your data environment so you can size the returning book, plan revenue against it, and direct spend where it changes the outcome.
02
The question it answers
Bring it in plain language.
- “Which customers are most likely to renew?
- “How big is the returning book likely to be?
- “Where does retention spend change the renewal outcome?
What it does not do
- Explain why an individual scores high
- Claim causal or uplift effects
- Score without a defined renewal calendar
- Show per-record scores in the app
Record-level history with each customer's renewal or contract date. Roughly two years of history. No modelling work on your side.
03
How it earns trust
Graded in the open, before you see it.
Proven on our published instrument. Re-certified on your data before anything releases.
Certified
Scores are certified out-of-time on your own history before anything ships — measured on records the model never saw, and refused below a data floor.
Trained on past, tested on future
The model is scored on rolling windows of your own history — trained on the past, tested on the future it hadn't seen.
Beats a baseline, and tracks reality
Ranking skill must beat a naive baseline and the scores must track observed frequency. The model earns its grade on both, or it doesn't publish.
Refused below the floor
Below a minimum of observed events, the run stops rather than certifies. A weak score is never shipped dressed as a strong one.
Rigour you can inspect is worth more than rigour you're asked to trust.
A deployed model, priced per brand on the page.See pricing →