Ad Inventory Floor Price Stress Test
Estimate how an aggressive floor price will influence programmatic fill, net revenue, and the upside of easing the floor before peak seasonality. Provide impression volume, fill targets, bid CPM, and the test floor to benchmark risk, eCPM drag, and recapture scenarios.
Cross-check with bidder logs and historical fill data before changing floors in production.
Examples
- 45,000,000 impressions, 85.00% fill target, $3.60 bid CPM, $2.80 floor → projected net revenue $129,744.00, effective eCPM $3.40, fill shortfall risk 5.78%, $0.20 floor cut could recapture $7,650.00.
- 28,000,000 impressions, 70.00% fill target, $2.40 bid CPM, $2.70 floor → floor exceeds bid forecast, fill shortfall risk 40.50%, projected net revenue $28,010.88, eCPM $1.43, lowering floor by $0.20 could recapture $3,920.23.
FAQ
How do I tailor the risk threshold?
Adjust the 0.72 multiplier inside the model to reflect your demand curve. Higher multipliers delay risk warnings until floors are closer to bid CPMs.
Can I model multiple markets?
Run separate scenarios per geo or device split, then combine the resulting revenue projections into your unified pacing plan.
Does this include fees?
No. Apply your supply-side platform revenue share after this calculation if you need net-to-publisher figures.
What about direct-sold campaigns?
If a guaranteed deal overrides the floor, subtract those impressions from the monthly volume before running the stress test so results focus on remnant programmatic demand.
Additional Information
- Fill shortfall risk estimates what percentage of your target fill is jeopardized once floors crest roughly 72% of the bid forecast, a common tipping point in header auctions.
- Net revenue subtracts the projected risk drag from base revenue, highlighting how much programmatic revenue sits on the line if demand partners balk at the floor.
- Effective eCPM reflects the revenue per thousand impressions served at the target fill under the stress scenario so you can align with yield dashboards.
- Lowering the floor by $0.20 is a common experimentation increment—adjust that constant if your ad ops team prefers testing in $0.10 steps or larger jumps during peak season.