A leading global online travel agency (OTA) uses Bicycle AI’s agentic analytics platform to uncover gradual, “slow-burn” booking and failure trends across millions of hotel listings—patterns that traditional real-time alerting misses—and turn them into targeted interventions that protect revenue and hotel conversion over weeks, not just minutes.
This global online travel platform offers flights, hotels, and vacation packages through its website and mobile app. Processing millions of flight searches daily, it delivers dynamic pricing to ensure customers receive competitive fares. Operating in over 200 countries with partnerships spanning hundreds of airlines and thousands of hotels, the company invests heavily in mobile experiences to capture spontaneous and last-minute bookings.
Traditional real-time anomaly detection at this OTA was tuned to catch sharp, sudden spikes in failure or booking drops, but it routinely missed gradual “slow burn” patterns—such as a 0.5% daily increase in failure rate or a steady decline in confirmed bookings at specific hotels—that compound into 5–10% impact over 6–12 weeks. These slow-moving issues often went unnoticed until they showed up as revenue leakage, partner escalations, or unexplained conversion drops.
Bicycle AI’s agentic analytics platform implemented a hotel trend analysis solution that looks across weeks—not just minutes—to identify and explain slow-building risks, combining booking trends, failure reasons, and revenue context into a single, action-oriented view.
Instead of discovering hotel performance problems only after they became large and visible, the OTA now identifies and addresses slow-moving issues early, when they are still reversible and less costly.
Revenue operations and hotel teams now start the week with a concise, structured view of where hotel performance is drifting in the wrong direction and why, instead of scanning dozens of dashboards or reacting to partner escalations. They can quickly drill from “this cluster of hotels is trending down” into the exact combination of failure types, platforms, and regions driving the change, and take targeted corrective actions with hotels and suppliers. Compared to generic BI and alerting tools, Bicycle AI is viewed as an intelligent layer that not only detects trends but also connects them to revenue impact, controllable levers, and concrete next steps.
In one line: Bicycle AI turned long-term hotel performance drift from an invisible, retrospective problem into a manageable, proactive workflow for protecting bookings and revenue.
Bicycle is an Agentic Analytics platform engineered for high-transaction businesses to optimize revenue
