1) When publishers talk about their “programmatic floor strategy,” what does that typically look like in practice today? How are most teams approaching this?
In practice, most publishers’ “floor strategy” is still largely manual and backward-looking.
Typically, teams set static CPM floors in GAM (via UPRs) or in their wrapper/Prebid configuration, segmented by broad buckets like geo, device, or placement. These floors are usually based on historical averages, past performance, or rough heuristics rather than real-time demand signals.
They revisit and tweak them periodically — weekly, monthly, or around seasonal moments — but the core approach rarely changes: fixed prices applied across highly variable auctions.
So most “floor strategies” today are really just static rule sets designed for a slower market. They are not built for the speed, variability, or complexity of modern programmatic auctions.
2) Modern programmatic auctions happen in milliseconds with DSPs constantly repricing based on real-time signals. How should that reality influence the way publishers think about setting price floors?
It should completely reshape how publishers think about pricing.
If buyers are making decisions in real time, then floors cannot be static. They need to reflect what is actually happening in that specific auction — not what happened last week or last month.
Instead of asking, “What should my average floor be?” publishers should be asking, “What is this impression worth right now, given who is in the auction and how they are behaving?”
That means treating floors as dynamic, responsive signals — not fixed thresholds — that react to live demand, bidder behavior, and competition in real time.
3) Many publishers measure price floor success by looking at CPM changes, often comparing this week to last week. Is this the most effective way to measure this?
No — it is one of the weakest ways to evaluate floors.
CPM alone is an incomplete metric. It only tells you the price of impressions that are sold. It says nothing about suppressed demand, lost bidders, or auctions that never cleared.
On top of that, before-and-after comparisons (this week vs last week) are fundamentally flawed because the market is constantly changing. You end up measuring market volatility more than pricing impact.
This is why floor changes often look “good” or “bad” depending on timing — not because of the floor itself, but because of shifting demand conditions.
4) What other metrics should publishers actually be tracking to understand whether their floor strategy is working?
The single most important metric is holistic RPM — revenue per thousand ad opportunities (requests) across all programmatic channels (Prebid, Amazon, AdX, Open Bidding).
This metric captures:
â—Ź Price impact (CPM)
â—Ź Volume impact (fill)
â—Ź Buyer participation and routing effects
Crucially, this must be measured per ad unit first, then aggregated to site level. Site-wide averages hide too much.
Beyond holistic RPM, publishers should also track:
● Bid density (bids per auction) — a proxy for competition
● Win rates by bidder — to see which partners are reacting to floors
● Timeouts / drop-offs — signs of demand suppression
● Clearing price distributions — where auctions are actually settling
Together, these give a much clearer picture of whether floors are helping or hurting.
5) What happens at the auction level when a publisher sets a floor that doesn’t align with what DSPs are willing to bid in that moment?
Two things typically happen, depending on the direction of misalignment:
If the floor is too high, DSPs don’t negotiate — they exit. Bidders reduce participation, route budgets elsewhere, or stop bidding altogether. Fill drops, and auctions become less competitive.
If the floor is too low, auctions clear too easily. Bidders don’t need to bid aggressively, competition thins out, and you end up leaving meaningful value on the table — selling inventory below what buyers were actually willing to pay and losing yield in the process.
In both cases, revenue is lost — it just appears differently: either as lower fill or lower effective prices.
6) When publishers analyze floors using site-wide or monthly aggregated data, what critical dynamics are hidden from view?
A lot.
Aggregated data hides:
â—Ź How different ad units respond to floors
â—Ź How specific bidders behave in specific geos or devices
â—Ź Time-of-day demand patterns
â—Ź Differences between mobile vs desktop, app vs web, or browser types
You might see “healthy” site-wide CPMs, but underneath that some placements could be massively underpriced while others are choking demand.
Most auction-level behaviors — like bid density shifts or bidder pullback — get completely washed out in monthly rollups.
7) How do price floors influence which demand partners and campaigns even enter an auction? Is it just about setting a minimum price?
It’s more than just having a minimum price.
In modern auctions, price is a signal — but a static price is a weak one. Without a reliable, real-time signal, DSPs have to guess the likely clearing price, which makes pacing and confident bidding harder, even when the audience match is strong.
Dynamic floors strengthen that signal. By adjusting in real time, they give buyers a clearer view of where the market is clearing right now, which makes them more willing to participate and bid at the true value of the impression.
Dynamic floors can also help unlock demand. When a higher, real-time clearing price is visible, it can influence which campaigns DSPs prioritize for that impression.
Finally, dynamic floors improve how inventory moves through SSP and DSP throttling systems. Requests that are priced in line with real demand are more likely to clear — and therefore more likely to pass throttling and access available budgets.
In short, dynamic floors act as a real-time market signal that shapes participation, routing, and budget access — not just a guardrail on price.
8) If a Head of Programmatic wanted to properly test whether a floor change improved performance, what would that test need to look like?
They would need a true real-time A/B test, not a before-and-after comparison.
That means:
â—Ź Splitting traffic into a floored cohort and a control cohort running simultaneously
â—Ź Measuring holistic RPM per ad unit across both groups
â—Ź Ensuring both cohorts see the same demand conditions
â—Ź Tracking bidder behavior (bid density, win rates, drop-offs) alongside revenue
Only with this setup can you isolate the true impact of pricing from normal market volatility.
9) What patterns have you seen when publishers shift from static to more dynamic floor strategies? What typically changes in their auction outcomes?
The most consistent pattern is that auctions become healthier.
Typically we see:
● Higher holistic RPM (often 10–16%)
â—Ź Slightly higher CPMs (around 7-11%)
â—Ź Stable or improved fill
In other words, pricing aligns better with real demand. Publishers capture more value without suppressing liquidity.
Perhaps most importantly, they stop seeing the wild performance swings that plague static floor setups.
10) Where do you see price floor strategies evolving as the programmatic ecosystem becomes increasingly algorithmic on both buy and sell sides?
On the web, pricing is becoming more holistic, unified, and visible inside the auction.
Two recent shifts are accelerating this:
â—Ź Amazon now participates in Prebid as a bidder, meaning Prebid floor decisions can directly influence Amazon demand instead of pricing it in a separate silo.
â—Ź Google now allows separate pricing for AdX demand in GAM, giving publishers far more control over how AdX is treated relative to other demand.
Prebid floors themselves aren’t new — they’ve existed since early header bidding. What’s changing is that more major demand sources now sit inside a common pricing perimeter.
As a result, I expect floors to become more holistic and consistent across channels, delivering clearer RPM impact and giving publishers greater control over how their inventory is positioned and monetized.
Vijay Kumar is the founder and CEO of Mile, an AI-powered programmatic optimization platform. In 2012, he was selected for the Times Accelerator program in India and received bootstrap funding to develop his first product. Kumar founded Automatad (now Mile) to bring ad tech managed services to the Asia Pacific, then relocated to the US in 2018 for market expansion. In 2023, he led the company's pivot from managed services to AI-driven optimization technology, rebranding to Mile. He is a member of the IAB Tech Council and Prebid.
