Maximizing yield with SmartFloor
How ML-powered dynamic floor price optimization increased sRPM
Client
The client operates a global supply-side platform that enables publishers to control and optimize their ad inventory across web, mobile, and CTV. It connects directly with multiple demand partners to ensure competitive auctions and fair market pricing.
Value delivered
By migrating to the TeqBlaze white-label SSP with the built-in SmartFloor engine, the client gained a unified infrastructure where machine learning continuously optimized bid floors across all traffic. The platform replaced manual pricing with automated, data-driven adjustments that reacted instantly to market signals. Within weeks, yield stabilized, undervalued inventory was eliminated, and sRPM grew by more than 10% — all without relying on external tools or constant operator input.
Challenge
The client’s previous SSP relied on static bid floors that couldn’t keep up with changing demand patterns. As traffic scaled, manual updates became unsustainable, leaving parts of the inventory undervalued and yield inconsistent. The lack of automation limited responsiveness to market shifts and made pricing control highly dependent on daily operator input.
Results
After adopting the TeqBlaze white-label SSP with the integrated SmartFloor ML-based engine, the client achieved significant improvements in revenue performance, operational efficiency, and pricing precision.
Key outcomes
Overall, the client’s SSP recorded a 10-11% increase in sRPM within the first evaluation cycle. SmartFloor proved its ability to deliver consistent monetization improvements across diverse inventory types while significantly reducing operational overhead. The ML-based pricing engine also enhanced the client’s long-term pricing intelligence—enabling more precise, data-backed decisions for future yield management.