After peaking at 32% in 2020, cancellation rates have been steadily dropping ever since. Despite cancellation rates reducing year-on-year, 1-in-5 bookings properties are still canceled (SiteMinder data).
What’s more, analyzing Booking.com data reveals that online bookings make up 60% of all hotel bookings, and the median lead time for cancellations is 15-30 days before check-in. Notably, couples cancel 66.7% of their bookings, whereas families only 11%.
Our AI team with Serhii Skoromets built an AI model that goes over generative capabilities and predicts booking cancellations.
Built on Google Cloud Vertex AI and AutoML, the app lets hotels predict cancellations easily by analyzing customer data such as how soon guests book, how long they stay, guest information, previous bookings and cancellations, room preferences and prices, special requests, and seasonal trends.
It cuts through complex data, spotting trends and influences automatically. More importantly, it automatically picks and trains the best models for accurate forecasts, enabling users to make accurate predictions with minimal technical knowledge.
Traditional methods like historical data analysis and strict policies fall short in dynamic markets. Using insights from predictive AI, hotels can offer flexible cancellation policies or off-season promotions to reduce cancellations and attract more guests, resulting in a 15 to 25 percent potential EBIT improvement (McKinsey & Company estimates).
For a deeper dive into how this technology is revolutionizing the booking industry, read our recent article👇
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