Coverage & readiness
Why only some venues are analysed
Checking night history costs data and time per venue, so this view analyses up to 12 at once, chosen by relevance for tonight. Results describe only those venues — nothing is scaled up to the rest of the group.
What it is
Why only some venues are analysed is not a measurement — it is the honest label on a budget. Checking a venue's night history takes extra data and time for each room, so the Compare view analyses up to 12 venues at once, and this explainer says which 12 and why.
The 12 are chosen for tonight, not at random. Venues with a current reading come first — starting with the ones needing attention or going stale, then the freshest — and venues with no current reading fill any remaining places alphabetically. It describes coverage, not a comparison: every result in the Compare card is a statement about these venues only.
How to use it at your venue
A grounded way to read the analysed list is "these are the venues judged most relevant to your question right now, looked at in depth" rather than "this is a census of the group." That distinction is the core discipline of purposive sampling in the research-methods literature: a deliberately selected, criterion-based set is valid for the units you actually examined, but its findings cannot be stretched onto the units you left out.[1] Choosing a limited, information-rich set against explicit criteria under real resource constraints is itself the standard, named framework for this kind of work — not a shortcut around one.[2]
So operators often treat a result as directly actionable for the venues in the set, and treat any pattern that seems to apply to a venue outside it as a hypothesis to check, not a proven fact. Hospitality already runs on a version of this: hotel revenue management uses STR "Competitive Sets" — a small, hand-picked group of rival properties assembled by criteria, explicitly describing only the chosen competitors rather than the whole market — as a standard industry convention.[4] The relevance-tiered selection here is the same idea applied to your own rooms.[3]
How to read it
Read the membership of the 12, not just their numbers. Because live and needs-attention venues are selected first, the analysed set is weighted toward the rooms most worth your time tonight — a venue near the top of the list earned its place by needing a look, not by performing a certain way. Venues with no current reading appear only to fill spare capacity, alphabetically, so their presence or absence carries no signal about the night.
A venue left out of the 12 is not a venue doing well or badly. It simply wasn't read this pass, and the results below say nothing about it either way.
What it doesn't mean
⚠ What it can't tell you
Results describe only the venues analysed. Nothing is scaled up to the rest of the group, no ratio of analysed-to-owned is drawn, and a room outside the set is never inferred to be like the ones inside it. The selection is not a random sample, so it supports no population-level claim about your portfolio.
It is also not a ranking or a health check: the order the 12 are chosen in reflects relevance and staleness, not performance. And a venue being absent is an absence of a reading, not evidence of a quiet or a busy night.
How it's calculated
The choice runs entirely on the readings already loaded for your whole portfolio, so it costs no extra time and reads nothing new. Each owned venue is sorted into one of three relevance tiers from its current live state, ordered within tier, and the first 12 are taken. Venues with a current reading — with the ones needing attention or going stale ahead of the fresh ones — fill the budget first; venues with no current reading fill any remaining places alphabetically.
Given the same venues in the same reading states, the rule always chooses the same 12. But readings change through a night, so the selection updates as venues gain, lose or need attention on a current reading — it is deterministic on the inputs, not fixed for the portfolio.
The exact method
Selection is client-side and pure: gdSelectForCompare(owned) tiers each owned venue as above, breaks ties by name, and fills any leftover budget alphabetically from tier 2. The read budget is the constant VH_CMP_VENUES = 12. The result feeds renderPortfolioCompare and the coverage block, whose trigger is labelled "Why {N}?".
It reads only cached per-venue live state — vpState / vpLive derived from door scans, with approval and staleness governed by GD_LIVE_MINS — and issues no new Firestore reads and no POS calls. The compact coverage line scopes every downstream figure with "Every result below uses only these venues," and no analysed-to-owned proportion is ever computed.
The research behind it
You can't fairly judge an entire market by digging deep on just a handful of businesses picked because they matter most right now — that's fine and useful, but it only tells you the truth about the ones you actually looked at, not about everyone else. Research calls the "picked because they matter most" approach purposive sampling, and the field's hard rule is: never stretch conclusions from that small deliberate group onto businesses you didn't examine. Hotels already do a version of this every week with "competitive sets" — a hand-picked handful of rival properties, not the whole market.
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Comparison of Convenience Sampling and Purposive Sampling ✓ verified
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Purposeful Sampling for Qualitative Data Collection and Analysis in Mixed Method Implementation Research ✓ verified
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Qualitative Research & Evaluation Methods: Integrating Theory and Practice ✓ verified
Read the source ↗ -
Strategic success unveiled: How competitive sets lead hoteliers to informed business decisions ✓ verified
Read the source ↗
Confidence & caveats
The selection rule is exact and repeatable: for a given set of venues in given reading states it returns the same 12 every time, and it introduces no estimate or band of its own. What "sure" means here is narrow — sure about which venues were analysed and why, not sure about anything beyond them.
Because the set is chosen for relevance rather than drawn at random, no statistical confidence attaches to the group as a whole: the findings are trustworthy for the analysed venues and carry no inference about the rest.[1] The 12 can also change between passes as live readings come and go, so the membership is current-state, not a fixed roster.