Before
Reservation history documented who had stayed, when they stayed, and what they spent. Finding the guests who were unusually late to return still required manual judgment.
Case Study · Island House Key West
From reservation history → guest return intelligence
Island House already had years of reservation history. The opportunity was to turn that history into a short list of repeat guests worth contacting now.
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Before / After
After
Island House could identify repeat guests who had moved beyond their own normal return pattern without another reservation on the books.
The Job
Turn years of booking history into a Tuesday-morning call list.
The useful question was not simply who had spent the most. It was which repeat guests had gone beyond the interval in which they normally returned and therefore deserved attention now.
See the pattern
Calculate each repeat guest’s normal interval between stays instead of treating every guest as though they return on the same schedule.
Find the exception
Flag guests once the current gap moves beyond 120% of their normal return interval.
Make it actionable
Remove guests who already have a future reservation so the result is a list someone can actually call.
“We are leaving money on the table.”
James Braun · Island House Key West
The Artifact
The Guest Return Window
The Guest Return Window compares each repeat guest’s current absence with that guest’s own historical return rhythm. It converts booking history into a decision signal.
Holy Gusto Marketing
Guest History → Return Window
Most hotel reports can rank guests by spend or recency. The Guest Return Window asks a different question: who is late relative to their own behavior?
Why the Window Is Useful
A fixed recency rule can miss the difference between a guest who normally returns every three months and one who normally returns every eighteen.
The return window uses the guest’s own history as the baseline. That makes the resulting list smaller, more specific, and easier for a reservations team to act on.
Measure the normal interval between stays for each repeat guest.
Review the guest once the current gap reaches 120% of that normal interval.
If there is no future reservation, put the guest on the outreach list.
Reservation records
18,598
Guest records
9,311
Review threshold
120%
Output
Call list
The Decision Rule
The calculation is simple enough to explain and specific enough to use.
| Step | Rule |
|---|---|
| 01 | Use repeat guests with enough reservation history to establish a normal return interval. |
| 02 | Calculate the guest’s average interval between stays. |
| 03 | Set the review point at 120% of that average interval. |
| 04 | Compare the current time since the last stay with that review point. |
| 05 | Exclude anyone with a future reservation already on the books. |
| 06 | Send the remaining names to the reservations team for human review and outreach. |
Guest names and individual reservation histories are withheld.
From Window to Action
Surface
Find repeat guests whose current absence has moved beyond their own normal return rhythm.
Check
Confirm that the guest does not already have another reservation and review the relationship before outreach.
Call
Give the reservations team a concrete reason to contact a specific guest instead of sending another broad promotion.
The Result
The reservation history became an operational decision tool instead of another report.
Island House could move from “who are our best guests?” to “which repeat guests should we contact now?” using data it already owned.
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