The forecast

From Real Reach to profit — before you spend

Acurrate forecasts the profit a creator will make your store before you pay them — every step derived from the one above it, run against your real economics. It is a forecast, not a report.

The chain

Each number comes from the number before it — no black box:

  1. Real Reach — the audience a placement reaches (median views of recent posts), not followers.
  2. → Clicks — the share of that audience that acts, by format and platform.
  3. → Customers — clicks at your store’s conversion rate, adjusted for how well the audience actually fits what you sell.
  4. → Revenue — customers at your AOV, including realistic repeat behaviour over a year.
  5. → Profit — revenue minus your COGS, shipping, discount and the creator’s fee. The only number that decides whether the deal was worth it.

Against your economics — not benchmarks

The same creator is profitable for one store and a loss for another; the difference is the store, not the creator. Connect Shopify and we pull your AOV, conversion and repeat rate automatically; if you’re not on Shopify you enter them and industry benchmarks fill the gaps. Generic benchmarks are the fallback, never the basis.

Before, not after

Attribution tools (UTMs, post-purchase surveys, MMM) tell you what happened after you spent. That’s useful, but it can’t stop you wiring $5,000 to the wrong creator — the money’s already gone. Acurrate runs the same economics before you commit, so the bad deal never gets booked. Then it tracks the real result, so the model stays honest.

Honest by construction

Every forecast carries a confidence tier — we say plainly when the data is thin rather than print false precision. We apply a deliberately conservative attribution-leakage haircut (not every tracked sale is truly creator-driven). And every campaign you run logs forecast versus actual side by side, so the model is accountable to reality, not just to a marketing page.

The logic is open; the calibration is not. The click-through, conversion-fit and longtail values that make each step land are tuned against real outcomes and kept internal — that tuning, plus the creator dataset behind it, is the moat. The reasoning above is the whole argument, and it’s yours to check.