Every open lead, scored and ranked on how likely it is to convert within a defined window — the list your next push should start from.
01
What you receive
Every lead, ranked by conversion likelihood
A demo readout with made-up figures. Yours shows your own data, in the same three parts: the answer, the working, and the proof.
Every open lead, scored and ranked on how likely it is to convert within a defined window. The full scored list is delivered to your data environment on release; the page carries the ranked, population-level shape. The top bands typically carry several times their share of expected conversions — the same effort works a shorter, better list.
02
The question it answers
Bring it in plain language.
When it's the right model
You have a specific milestone in mind — a conversion, a signup, an activation — and a window for it, with record-level history to learn from. The output is a ranked list someone will actually work: an SDR call sheet, a nurture queue, a partner handoff.
“Which leads are most likely to convert this quarter?
“Which signups are most likely to activate in the next 30 days?
“Which trials will convert to paid — and which are stalling?
What it does not do
Explain why an individual scores high
Claim causal or uplift effects
Score without a specific outcome, bar, and window
Show per-record scores in the app — the population view is on the page, the full list is in your data environment
Record-level behavioural events with dates, and amounts if value defines the outcome. 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.
How it's built
The rows that already converted train the model on what preceded them. Every score is then produced on data the model never saw while training, so the ranking is tested rather than fitted. Scores are calibrated against observed rates, so 0.30 means three in ten.
The maths
A fitted probability per row, validated out of sample on your data and calibrated against your own observed rates. The certified object is not the score — it is the expected conversion rate at each decile boundary of your own scored book, with an interval around it at a stated confidence level.
Rigour you can inspect is worth more than rigour you're asked to trust.