Analyze hotel performance against my competitive set
Explains occupancy, ADR, and RevPAR results versus the comp set, with index analysis and the likely causes.
The prompt
Analyze my hotel's performance versus my competitive set.
Property: [TYPE, ROOM COUNT, MARKET, SEGMENT MIX - e.g. 60% transient, 25% group, 15% contract]
Period: [e.g. last month, compared to same month last year]
My results: [OCCUPANCY %, ADR, REVPAR - this period and last year]
Comp set results or index report (e.g. STR/CoStar): [COMP OCC, ADR, REVPAR, OR MPI / ARI / RGI]
Segment and channel detail if available: [ROOM NIGHTS AND ADR BY SEGMENT AND CHANNEL]
Known events: [renovation, out-of-order rooms, citywide events, new hotel openings, weather]
Analyze:
1. Index position: MPI (occupancy), ARI (rate), RGI (RevPAR), and whether each is above or below fair share (100). State whether we gained share by buying occupancy with rate, or the reverse.
2. Year-over-year change for us versus the comp set, so market-wide movement is separated from our own performance.
3. Segment and channel contributors: which segments drove the change, and whether lower-yield channels grew.
4. Day-of-week or peak/shoulder patterns if the data allows.
5. Effects of out-of-order rooms or available-room changes on the numbers.
Return:
- A one-paragraph summary for ownership.
- A table of KPIs with variance vs. last year and vs. comp set.
- The 3 most likely causes, each labeled as supported by data or a hypothesis to check.
- 3-5 actions for next month (pricing, segment mix, channel, sales focus).
Calculate only from the figures I provide; mark anything missing as [ADD DATA].
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Data & Analysis prompts
What to fill in
- e.g. last month, compared to same month last year
- OCCUPANCY %, ADR, REVPAR - this period and last year
- COMP OCC, ADR, REVPAR, OR MPI / ARI / RGI
- ROOM NIGHTS AND ADR BY SEGMENT AND CHANNEL
- ADD DATA
How this prompt is built
This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.
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