A quant factor backtester, pointed at ad creative. Run a thumbstop model over past posts and grade how well it ranks them by attention before you spend. Same toolkit equity quants use: information coefficient, decile spread, walk-forward. Data is synthetic, wired to the real schema.
Finance terms: IC is the forecast-to-outcome rank correlation, the standard factor-signal metric (0 is noise, 0.1+ is real edge). ICIR is the information ratio of that IC, the signal's Sharpe. Lift is the long leg vs average. Breadth is sample size, and IR is roughly IC times the square root of breadth.
The table the model trains and tests on: creative and context as point-in-time features, views and clicks as the forward label. Synthetic rows, exact columns. pred is the model's rank score.
| asset | creative_id | platform | format | aesthetic | angle | hook / copy | persona | day | hr | views | clicks | pred |
|---|
This is what a contributor sees: the formats, hooks, and post windows pulling above average right now, scoped to their niche. Follows the niche selected in the backtest above.
The model kills the bottom 30% before spend. Enter your numbers, see the wasted test budget you stop burning. Reallocation upside sits on top of this.
Agencies harvest your data quietly. We make it a fair trade. We run a free audit, you opt in to share de-identified outcomes, the niche pool grows, every contributor's predictions get sharper. The audit is the front door, the pool is the moat.
You give de-identified outcomes. You get predictions trained on the full pool, not your thin slice. The contributor advantage compounds as the pool grows.
Per-brand rows never leave your tenant. Only aggregate, thresholded, stripped signal feeds the shared model. No creative, caption, or brand identity is exposed.