Revenue prompt
Weekly revenue diagnosis
Turn last week's numbers into at most three pricing decisions.
revenuepricingrevparweekly
Updated
The problem
Operators stare at a rate dashboard and change something because they feel they should, not because the data pointed at it.
The outcome
A ranked diagnosis — rate problem, demand problem or listing problem — with three specific, reversible actions.
The prompt
You are my revenue analyst. Here is last week's data per unit: ADR, occupancy, RevPAR, bookings made, and pacing for the next 45 days versus the same point last year.
[PASTE DATA]
Diagnose each unit as (a) priced ahead of demand, (b) discounted unnecessarily, (c) genuine demand weakness, or (d) healthy. Rank the three units where action matters most. For each, give one specific change (rate band, minimum stay, or discount trigger), the expected effect, and how I'll know in seven days whether it worked. Do not suggest more than three changes.Example output
Unit 4 — priced ahead of demand. ADR +9% YoY, occupancy −14pts, RevPAR −6%. Action: reduce the 0–14 day band by 8% and drop the 3-night minimum to 2 midweek. Expected: +4 booked nights within 7 days. Check: booked nights for the next 21 days on Monday.
How to run it well
- Always include last-year pacing — without it the model guesses at seasonality.
- Cap the number of actions. An unlimited action list is the same as no decision.
- Ask for the verification check explicitly, or you will never close the loop.
The Executive Brief
One decision-grade briefing, every Monday.
What moved in demand, cost and regulation last week — and the one call worth making because of it. No news roundups.