Conversion is one of the most important drivers of hotel performance, yet also one of the least understood. In the hotel context, conversion rate reflects the share of potential guests who move from browsing to actually completing a reservation.
NB: This is an article from EHL
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Although conversion matters across all channels, it is particularly critical for direct bookings on a hotel’s own website, where the property has the greatest control over the guest journey and the greatest ability to influence outcomes through pricing, messaging and booking conditions.
Why One Conversion Number Doesn’t Tell the Whole Story
Hoteliers often quote a single booking conversion rate as if it were a stable, fixed metric, but it isn’t. Behind that single percentage lies a complex interaction of guest behavior, pricing decisions, demand patterns, booking conditions and channel context. Conversion shifts by season, segment, booking window, device type and the competitive environment.
A hotel may optimize rates based on demand forecasts, yet still lose bookings if guests perceive weak value, encounter restrictive conditions, or experience friction in the booking journey that pushes them toward intermediaries. Without understanding these underlying dynamics, even the most sophisticated pricing strategy can miss its mark.
A recent study published in the International Journal of Hospitality Management offers one of the most rigorous examinations of how conversion behaves in real hotel operations. Using more than 34,000 booking requests from a leisure hotel, the study treats conversion not as a single average KPI but as a dynamic outcome that changes across different demand environments throughout the year. Rather than searching for one universal explanation of conversion, it demonstrates that booking decisions depend heavily on the conditions under which guests search, evaluate offers and decide whether to commit.
Using Clustering to Capture Real Booking Contexts
To capture this complexity, the study applies a two-step analytical framework. First, machine learning is used to segment stay dates into distinct clusters that represent different demand situations. Second, logistic regression modeling is applied within each cluster to identify which factors most strongly explain whether a booking request becomes a confirmed reservation. This approach reflects a crucial insight for hotel commercial teams: conversion is shaped by shifting consumer decision contexts, not by pricing or website factors in isolation.
