Stop spreading budget evenly across guesses. Using deep unit economics — ARPU, LTV, CAC, ROI — plus scoring and forecasting, I point spend at the leads and segments most likely to convert and pay back.
Predictive marketing is not a black box — it is disciplined unit economics applied forward. I model ARPU, ARPPU, LTV, CAC, IAC and ROI, then use lead scoring and forecasting to weight budget toward what will actually pay back. The result is spend that gets sharper every cycle instead of flatter.
Deep analysis of ARPU, ARPPU, LTV, CAC, IAC and ROI to find where money is actually made and lost.
Predictive scoring that ranks leads and segments by likelihood to convert and to stay.
Forecasts that decide where the next dollar goes, weighted to payback rather than volume.
Autobid strategies fed by your data so platforms optimize toward your economics, not theirs.
I build the unit-economics model from your data and expose the real ARPU, LTV and CAC by segment.
Lead scoring and forecasting rank where the next budget should go for the best payback.
Budget shifts toward predicted winners each cycle; the model sharpens as data accumulates.
Optimized cost per registration from $42 to $0.55 — a 78× improvement — across 7 traffic sources on a $30K/mo budget, driven by relentless measurement of cost against value per source.
No. It starts with clean unit economics on the data you have; predictive weighting gets sharper as volume grows, but the discipline pays off from the first model.
Mostly rigorous math and modeling, with AI and automation where they genuinely speed up scoring and forecasting — never as a buzzword.
We look at your funnel and the numbers, and you leave with a clear next step — whether or not we work together.
Book your call Prefer email? hello@one-man.marketing