Think of Ringier’s ad inventory like airplane seats: if the price is too low, you leave money on the table; if it’s too high, seats go empty. This AI system constantly studies how buyers behave in the ad auction and automatically adjusts the minimum price (floor price) so that more impressions sell at the best possible price without scaring away demand.
Manual or rule-based floor price settings in programmatic advertising are slow, coarse, and often sub‑optimal, leading to either lost revenue (floors too low) or lost fill and campaign performance (floors too high). Ringier needed a dynamic, data-driven way to optimise floor prices at scale across inventory to maximise yield while protecting demand and user experience.
Proprietary impression-level auction data and buyer behaviour patterns, combined with tuning of models to Ringier’s specific inventory and business rules, create a data and process moat that is hard for generic third-party tools to replicate.
Classical-ML (Scikit/XGBoost)
Structured SQL
High (Custom Models/Infra)
Model retraining and feature computation latency at large impression volumes; integration constraints with SSPs/RTB stack when pushing dynamic floors in real time.
Early Majority
Unlike generic yield-optimisation baked into major SSPs or ad servers, this implementation is tailored to Ringier’s first-party data, portfolio of properties, and specific revenue goals, allowing more granular control over risk–reward trade-offs in floor pricing than off-the-shelf optimisers.