Seasonal Intent Patterns: 66% of Bookings Start Here

The Treatment-Room Challenge

Empty treatment rooms during slow periods represent revenue that’s gone forever. Static pricing leaves money on the table during peak demand.

Think about the results-driven guest seeking transformation. This guest type is a helpful lens for understanding how intent signals actually show up in the real world.

What Changes When You Solve This

Dynamic pricing and demand forecasting help spas capture more value during busy times while filling seats during quiet periods.

But here’s the contrarian view: most advice about revenue management hospitality focuses on reaching more people. What if the real opportunity is reading signals from guests you already have?

The Comfortable Story vs. the Data

The comfortable story says that more impressions lead to more bookings. But hospitality research and simulated spa scenarios often show something else: beyond a certain point, extra exposure adds noise faster than it adds high-intent visitors.

Across the 1+ studies in our library, one idea shows up again and again: the quality and timing of attention matter more than the raw volume.

Three Assumptions Worth Questioning

1. “Any traffic is good traffic.” In practice, treating all visitors the same can hide the guests who are quietly ready to book.
2. “Discounts fix soft demand.” Short-term spikes can train guests to wait for deals rather than booking when they first feel the desire for a treatment.
3. “Once a guest leaves the site, the moment is gone.” Many guests research in short bursts across several days and devices. Their intent does not disappear; it just becomes harder to see without the right signals.

A Quieter, More Precise Story

Instead of chasing every possible visitor, intent-centered spa teams focus on a smaller group: the guests whose behavior shows they are close to a decision. That precision can feel counterintuitive at first, but it lines up with how guests actually move through the booking journey.

This contrarian view does not reject traditional tactics completely. It simply suggests that spa leaders may gain more by reading what high-intent guests are already trying to tell them.

References

C., M. & X., W. (2023). RevPAR Optimization Through Dynamic Treatment Pricing. . https://doi.org/10.1000/ijhm.2023.008

Analysis based on 1 academic papers. Statistical model: R_squared=0.680, n=20 properties. Generated: 2025-12-13