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Forecasting & unit economics

Predictive Marketing

Stop splitting budget evenly across guesses. Lead scoring, forecasting and a hard look at your unit economics — ARPU, LTV, CAC, ROI — show who is most likely to buy and pay back. That's where the money goes.

ARPU · LTVCAC · IAC · ROI modeled
Predictive lead scoring and forecasting analytics
Overview

The problem I solve

Predictive marketing sounds complicated; at its core it's simple. I score every lead and segment by how likely they are to buy and stick around, then line that up against the unit economics — ARPU, LTV, CAC, ROI — to see where money actually comes back and where it leaks. Budget goes to the people most likely to turn a profit. And instead of guessing about next quarter, you get a revenue forecast you can plan around.

What's included

Scope of work

Unit-economics modeling

I break down ARPU, ARPPU, LTV, CAC and ROI by segment, so you see which channels and customers turn a profit and which quietly drain the budget. Every decision comes straight from the numbers.

Lead scoring & segmentation

Scoring ranks leads by how likely they are to buy and stay, so sales and ads work the warm ones instead of burning hours on random contacts.

Forecasting & budget allocation

Forecasts show where the next dollar actually pays back, so spend follows real return. Click and lead counts that only look good on a chart stop driving the budget.

Smart bidding integration

I feed the platforms' autobidding your real payback data, so Google and Meta optimize toward your profit instead of raw impressions.

How I work

Three steps, all in one pair of hands

Model the economics

I build the unit-economics model from your data and put the real ARPU, LTV and CAC on the table for each segment, with no blended averages hiding the truth.

Score & forecast

Scoring and forecasting show which segments pay back best — and exactly where the next slice of budget should go.

Reallocate & compound

Each cycle budget shifts toward the predicted winners, and the model gets sharper as data builds up — the same spend works harder every month.

Result

A real example

$0.55
Amadeus Protocol · Web3

Cut cost per registration from $42 to $0.55 — a 78× drop — across 7 traffic sources on a $30K/mo budget. The method was simple: constantly weigh what each source costs against what it actually brings in, and shut off whatever doesn't pay back.

Predictive work lives inside the Growth and Fractional CMO packages. It runs on the tracking that Foundation sets up first — without clean data there's nothing to forecast from.
FAQ

Common questions

No. We start with clean unit economics on the data you already have. The more history builds up, the sharper the forecast gets, but the payoff shows from the very first model — you immediately see where the money is leaking.

At its core it's rigorous math and modeling. I bring in AI and automation only where they genuinely speed up scoring and forecasting.

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Let's talk

Book a 30-minute strategy call

We look at your funnel and the numbers, and you leave with a clear next step — whether or not we work together.

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