Floor Optimization

A floor price protects a publisher from selling attention below its worth — set too low, second-price clearing grinds toward zero in thin competition; set too high, fill rate — the share of slots that actually serve an ad — collapses. Publishers shouldn’t have to guess this number, so Promovolve measures it.

Sweep, don’t learn

The FloorSweepOptimizer is deliberately not reinforcement learning — an earlier RL approach (value estimates over discretized floor levels) was built, evaluated, and dropped: with pacing and Thompson Sampling already adapting around every floor change, credit assignment was hopeless, and the agent mostly learned noise. What replaced it is controlled measurement:

1. Sweep. Generate candidate floors across the plausible range (bounded below by observed rejected bids, above by the best observed bid). Hold each candidate for a fixed number of auction ticks, measuring served revenue — actual post-pacing, post-selection earnings, not theoretical clearing prices. Revenue is the only honest objective; anything upstream of it can be gamed by the very systems the floor interacts with.
2. Exploit. Take the argmax — the floor that earned the most — and hold it for a longer exploitation period. Ties within tolerance resolve to the higher floor — the same revenue on fewer impressions, and more robust if the dominant bidder’s value drifts up. The optimizer only fails open to the lowest floor when evidence is missing, and a minimum-impressions guard keeps a lucky low-traffic candidate from winning on a handful of data points.
3. Repeat. Markets drift; the cycle re-runs continuously.

Per-category floors

A single site-wide floor has a monopoly pathology: one rich category (say, Finance demand at $12) sets a floor that locks out every other category’s demand entirely. Floors therefore run per content category, each category sweeping independently; categories without enough data fall back to the site-level floor. The pathological case that motivated this — a lone high bidder pushing the site floor above everyone else — now prices one category at $12 while Travel still clears at $3.

Two guardrails matter more than the sweep itself:

• Only approved demand teaches floors. Pending creatives bid (to reach the approval queue) but are invisible to the optimizer — otherwise an unapproved, possibly rejected campaign could inflate a floor that outlives it.
• A lone bidder pegs the floor to its bid. With one approved bidder there is no second price and nothing to sweep; the floor snaps to the bid (and instantly back down when the bidder leaves — validated live in both directions). And with zero approved demand the floor collapses to the minimum immediately: a floor with nobody to price against is pure fill-rate damage.

One honest caveat: in a perfectly homogeneous market — every bidder at the same CPM — every floor below the common bid earns identical revenue, and the optimizer settles anywhere on that plateau. That is correct behavior, and a reminder of what this component is: not intelligence, just a well-designed experiment that never stops running.