A popular web-based developer tool running Prebid.js with multiple demand partners connected BidTune to see if automated optimization could outperform their existing manually-tuned configuration. Over 12 days, AutoResearch ran 20+ controlled experiments and found actionable improvements across bidder management, timeout tuning, and price floor optimization.
Metric: net revenue per session (gross revenue minus serving cost). All experiments ran as 50/50 traffic splits with Bayesian evaluation. Auto-terminated at >95% confidence. Lifts shown are marginal per-experiment gains vs. control.
The site was running a standard Prebid.js setup with 8+ demand partners through a managed wrapper. Configuration hadn't been actively optimized in months. Timeouts were set to defaults across all bidders. Floor prices were minimal. Nobody was testing whether the current setup was actually leaving money on the table.
This is typical. Most publishers configure Prebid once and move on. But bidder performance shifts constantly—demand partners adjust algorithms, new inventory demand comes online, traffic patterns change by season and geography. A setup that was optimal months ago is almost certainly suboptimal today.
After connecting BidTune (one script URL change), the system spent 48 hours in observation mode—collecting bid-level data across every auction, bidder, geography, and ad unit to build a baseline. AutoResearch then began proposing and running experiments automatically.
Over the next 12 days, AutoResearch ran 20+ experiments across non-overlapping traffic segments. Multiple experiments ran simultaneously—a floor price test in one geography alongside a bidder timeout test in another—maximizing the rate of learning without statistical interference.
Two demand partners were consistently winning auctions at below-market CPMs in high-value geographies, suppressing competition. Removing them forced remaining bidders to compete harder.
Sessions from certain geographies had extremely low average CPMs. Setting minimum price floors eliminated sub-penny bids and lifted revenue significantly in those regions.
The highest-revenue ad unit had no floor price. Adding one filtered junk bids and increased average winning CPMs without reducing fill rate.
One high-value bidder was timing out frequently at the default timeout. Extending their timeout captured high-CPM bids that were previously missed, without affecting overall page latency.
Applied the same floor strategy that worked on the header to the footer ad unit, which generates the most total revenue.
A high-CPM bidder performed well in premium markets but poorly elsewhere. Restricting it to markets where it wins improved overall auction efficiency.
Raising the sidebar floor to match the header floor was too aggressive—it priced out most bidders on a lower-value unit.
Raising floors in high-traffic emerging markets filtered out too many bids. The traffic volume didn't support the higher price point.
Reducing timeouts on the header unit to improve speed lost high-value late bids. The revenue impact outweighed the latency savings.
Lowering the global timeout from 2500ms to 2000ms seemed safe but missed bids from the highest-CPM bidder, which needed the extra time.