The Booked vs. Stayed Upgrade Analysis in Insights identifies when a guest’s booked inventory differs from their most recent stayed inventory, and classifies whether that change was paid (with additional overnight accommodation revenue tied to the action) or complimentary (no additional revenue).
This FAQ addresses common questions about how the analysis works and how to interpret the results.
FAQs
What does “Booked vs. Stayed” mean?
It’s a comparison between the inventory originally booked and the most recent stayed inventory for the same booking. If they differ, we record a change and classify it as an upgrade (to higher-ranked inventory) or a downgrade (to lower-ranked inventory).
How do you decide whether it’s an upgrade or a downgrade?
We rank inventories using historical ADR at the property. Moving to higher-ranked inventory is an upgrade; moving to lower-ranked inventory is a downgrade. This avoids relying only on inventory names, which vary across properties.
How do you decide whether an upgrade is paid or complimentary?
For units, we compare the first confirmed booking to the most recent version. This shows whether a guest ended up in higher- or lower-ranked inventory.
For revenue, the logic is different. Simply comparing the total revenue of the first and most recent versions would include all kinds of unrelated adjustments (like discounts, taxes, or other changes). Instead, we isolate the revenue changes tied specifically to the room change action by looking across the booking versions.
This means:
If a room change has additional overnight accommodation revenue directly associated with it, we classify it as a paid upgrade.
If the room change does not have additional overnight accommodation revenue tied to it, we classify it as a complimentary upgrade.
By focusing only on the revenue linked to the change itself, we avoid the “noise” of other adjustments and give you a clearer picture of when upgrades are being monetized versus given away.
Do you look at every intermediate change?
Not for everything. For units (the movement between booked and stayed inventories), we compare the first confirmed booking to the most recent version. This keeps the analysis clear by ignoring the noise of minor edits or adjustments in between. For revenue, we do look across booking versions, but only to isolate the changes specifically tied to the inventory movement. This way, unrelated adjustments (like discounts or tax changes) aren’t mistakenly counted as upgrade or downgrade revenue.
Why are some upgrade paths blank or “undefined”?
In large or complex bookings (such as hostels or groups with many beds tied to one booking ID), inventories may shift in multiple directions over time. In those cases, it is not always possible to identify a single clear path. The analysis will still show that a change occurred and whether it was paid or complimentary, but the “from → to” path may be undefined.
Does this include non-overnight upsell transaction items (like upgrade, upsell, Nor1)?
Not yet. The first version of this analysis focuses on overnight accommodation revenue only. Other upsell postings are not reflected today, but will be included in future development.
What about downgrades?
Downgrades are recorded when the most recent stayed inventory is lower-ranked than the originally booked inventory. They follow the same classification process as upgrades: if there is revenue specifically tied to the downgrade action, it will be shown, though in most cases no additional revenue is collected and sometimes the recorded revenue is lower than the originally booked level.
Is this the same as “Booked vs. Paid”?
No. This is a Booked vs. Stayed analysis with an additional classification of paid vs. complimentary. That distinction means you can see both what inventory the guest ultimately stayed in and whether the change generated overnight accommodation revenue.
How are multi-night stays handled?
The analysis reflects changes over the stay period. If a booking moves between inventories for some nights only, those nights are counted in the underlying data so that upgrade frequency, mix, and value reflect the real booking pattern.
Why might totals differ from what I see in my PMS screens?
Differences can occur for several reasons: optional or tentative bookings are excluded; only overnight accommodation revenue is included (not all posting types); and Insights always compares the first confirmed booking to the most recent version, which may differ from PMS views that display in-progress or intermediate changes.
Does this depend on PMS-specific features like RTC?
No. The analysis is PMS-agnostic, so it works consistently across all systems. Rather than relying on PMS-specific flags like RTC, we use a standardized logic: comparing the originally booked inventory to the most recent stayed inventory, and applying ADR-based ranking to determine whether the change was an upgrade or downgrade. This approach avoids inconsistencies between PMSs and ensures results are comparable and reliable across your entire portfolio.
Do you track room number changes within the same inventory?
No. The analysis focuses on inventory changes (e.g., Standard → Deluxe). Room-to-room moves within the same inventory are not included.
If a rate changes but the inventory doesn’t, is that included here?
Yes, the booking will still appear in the dataset, but because no inventory change occurred, it won’t show any upgrade or downgrade activity. In these cases, no upgrade/downgrade units and no upgrade/downgrade revenue are recorded, since we only measure revenue linked to inventory movements. So while the booking itself is present, the rate change alone does not appear in the analysis.
What if ADRs change over time — does the ranking stay accurate?
Yes. Inventory ranking is based on historical ADR from a rolling 365 days. This ensures the upgrade and downgrade hierarchy reflects real pricing behavior at the property rather than static assumptions.
Can I see who upgraded a guest or where the upgrade decision came from?
No. The PMS data does not tell us who processed the upgrade or the exact trigger (for example, whether it was front desk or an automated process). What the analysis shows is the outcome: whether a guest stayed in different inventory than originally booked, and whether that change was paid or complimentary. Context such as channel, segment, or company reflects the most recent booking information, so while it helps frame where upgrade activity sits, it does not always directly align with the point in time when the inventory change happened.
If you have any questions about how to interpret this analysis or how it applies to your property, please reach out to our Advisory team in the in-app chat — we’ll be happy to help.