From a commercial and cost-effectiveness perspective, direct website conversion is one of the most valuable levers in the hotel industry. While the room rate a guest pays may be similar across channels, direct bookings typically generate a higher net ADR yield and contribution margin because they reduce distribution costs such as OTA commissions and intermediary fees.
NB: This is an article from EHL
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Direct bookings also strengthen the hotel’s relationship with the guest by increasing opportunities for upselling and personalization and supporting long-term customer lifetime value. Improving direct conversion is therefore not simply about generating more bookings; it is about capturing more demand through the channel that often delivers the strongest profitability. Even small improvements in direct conversion can translate into meaningful financial gains, because the hotel converts a larger share of existing demand without necessarily increasing marketing spend.
Why One Conversion Number Doesn’t Tell the Whole Story
Yet 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.
The results highlight that conversion drivers are not stable over time. Each demand cluster exhibits its own unique set of determinants and, furthermore, guest-related characteristics play a major role in explaining booking outcomes. In other words, a factor that strongly influences conversion in one period may have a limited impact in another. This challenges the industry’s tendency to apply one-size-fits-all assumptions about what drives conversion. Instead, conversion management must be adaptive, requiring hotels to understand which determinants matter most in each demand environment and adjust their decisions accordingly.
