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To analyze a hotel’s booking spreadsheet reliably, first establish what each row represents, check the fields and definitions, and document any corrections. Then calculate metrics with consistent denominators, compare equivalent periods or segments, and treat the results as observations from that dataset—not proof of what caused them or universal hotel benchmarks.
Start by identifying what the spreadsheet measures
Before sorting, deduplicating, or calculating, preserve an unchanged copy of the original file. Record who or what produced the export, when it was created, the reporting period, and the currency. Most importantly, establish the spreadsheet’s grain: what does one row represent?
- A reservation: A row may describe a booking, with fields such as arrival date, booking date, cancellation status, or segment.
- A room-night: A row may represent one occupied or available room for one night.
- A business date: A row may summarize a day’s room inventory, rooms sold, and revenue.
These are not interchangeable. A reservation spanning several nights may appear once in a reservation-level export but contribute several room-nights to an operating report. The right aggregation and denominator depend on the row grain and the fields actually present.
Check the data before calculating
Inspect column names and sample records, then look for issues that could distort a summary:
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- Dates stored in inconsistent formats or interpreted in the wrong order.
- Blank or unexpected values in fields used for grouping or calculation.
- Numbers stored as text, or currencies mixed without a documented conversion.
- Category variations such as different spellings for the same segment or channel.
- Duplicate-looking records, missing keys, or totals that do not reconcile.
Do not delete apparent duplicates automatically. Multiple rows may represent a split stay, a revised booking, or separate records generated by the property-management system. Determine what a key identifies and how booking changes are represented before deciding that two rows are redundant.
Keep raw fields intact and record each transformation in a change log or a reproducible formula or query sequence. State how the analysis treats cancellations, no-shows, complimentary rooms, out-of-order rooms, taxes, fees, and manual adjustments. If the workbook does not establish how a category should be handled, flag the uncertainty rather than silently choosing a rule.
Choose metrics that match the records
For daily room-performance data, three common measures answer different questions. Use the reporting system’s definitions where available; “rooms sold,” “rooms available,” and “room revenue” need explicit rules.
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| Measure | Basic calculation | What to define |
|---|---|---|
| Occupancy | Rooms sold ÷ rooms available | Which rooms count as sold and available, and whether rooms taken out of service reduce available inventory. |
| ADR (average daily rate) | Room revenue ÷ rooms sold | What revenue is included and how sold rooms, cancellations, no-shows, taxes, fees, and adjustments are treated. |
| RevPAR (revenue per available room) | Room revenue ÷ rooms available | The same revenue basis and availability rule used for the period being analyzed. |
Occupancy is a rate, ADR is revenue per sold room, and RevPAR is revenue per available room. RevPAR reflects both rate and occupancy, so a change in it alone does not say which component moved. Cloudbeds documents its own ADR calculation as total room revenue divided by total rooms sold and specifies included revenue components and exclusions; that definition may not match another property’s system (Cloudbeds Data Fields definitions and calculations).
Roll up periods without distorting the result
For a multi-day result, add the relevant revenue and divide by the corresponding total rooms sold or available room-nights, using one consistent definition throughout. Do not simply average daily ADRs or daily RevPAR values when the denominators differ: a low-volume day and a high-volume day should not necessarily carry equal weight.
For example, period ADR should be calculated as total room revenue for the period divided by total rooms sold for that period. Period RevPAR uses the period’s room revenue divided by the period’s available room-nights. The LeadAfrik guesthouse spreadsheet describes monthly rollups from totals and distinguishes ADR per sold room from RevPAR per available room (LeadAfrik guesthouse occupancy and RevPAR tracker).
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Compare periods and segments on equal terms
Once the data is trustworthy enough for the question, summarize only by dimensions that are present and reliable. Depending on the workbook, useful cuts might include date or season, property type, market segment, booking channel, lead time, or cancellation status. A booking-level file may support some of these cuts; a daily operating summary may not.
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Before interpreting a comparison, check that the time windows align and that each measure uses the same denominator, inventory rule, revenue basis, and cancellation treatment. A month with more available room-nights is not directly comparable to a shorter period using raw room totals alone. If a dimension has missing or inconsistent values, disclose that limitation or omit the cut.
A hotel-booking analysis project poses questions about hotel types, seasons, months, segments, lead time, cancellations, and retention. Its reported numerical findings are specific to that project, not general hotel-industry benchmarks (Hotel Booking Analysis project repository).
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Turn a pattern into a cautious business observation
For each finding, describe what changed, the size of the change in the records analyzed, the period and segment involved, and the measure’s definition. Then identify plausible alternative explanations and a useful next check. A spreadsheet can show that two things occurred together; it cannot, by itself, establish that one caused the other.
- If occupancy changes, check both rooms sold and available inventory, including changes in rooms out of service.
- If ADR changes, check the room-revenue components, room mix, and treatment of adjustments or excluded stays.
- If cancellations differ between segments, confirm that booking statuses and date windows are comparable before drawing an operational conclusion.
Use a follow-up operational check or a controlled test when a proposed action depends on causation—for instance, whether a pricing change increased revenue rather than coinciding with a seasonal shift.
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Ontario’s government data catalogue lists a monthly hotel-statistics dataset with occupancy, ADR, and RevPAR for Ontario, including a July 2026 catalogue entry (Ontario Data Catalogue: Hotel statistics). It can provide regional context, but a provincial statistic is not automatically comparable to an individual property’s PMS report. Confirm geography, reporting period, and metric definitions before making a comparison.
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Likewise, an example workbook can help illustrate possible reporting structure, but its columns and rules are not universal requirements. Hospitality Grid describes a daily workbook with occupancy, ADR, RevPAR, target variances, operational checks, a selected-period dashboard, and fields such as total rooms, out-of-order rooms, rooms sold, complimentary rooms, and room revenue (Hospitality Grid hotel ADR and RevPAR calculator spreadsheet). Use only fields and rules that fit the records and reporting purpose at hand.
Present the result so it can be checked
A useful analysis makes its path from source records to conclusion visible. Include the reporting period, row grain, metric definitions, any exclusions or transformations, and the segments or dates included. Keep a clear link between each headline finding and the figures that support it. If a result depends on uncertain treatment of inventory or revenue, say so beside that result.
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