Prompt Chaining: Comprehensive 90-Day Market Analysis in 30 Minutes

What You'll Build

A reusable prompt chain — a structured sequence of connected AI prompts where each output feeds directly into the next — that produces a complete 90-day market analysis from raw data inputs. The chain runs in five linked steps: demand intelligence → competitive positioning → pricing strategy → restriction framework → stakeholder narrative. The final output is a presentation-ready market analysis that would otherwise take 3-4 hours to assemble manually.

Why This Matters for Revenue Managers

Most revenue managers can pull a good analysis for this weekend or next week. The 60-90 day horizon — where the real revenue decisions live — stays underanalyzed because building it manually takes too long. A prompt chain makes the 90-day horizon routine.

What You'll Need

• Claude Pro subscription ({{tool:Claude.plan}} — {{tool:Claude.price}}/month) or Claude Project already configured
• Your current STR or Lighthouse data for the 90-day window (even a screenshot summary works)
• Your property's current on-books pace report (export from PMS or RMS)
• Known events for your market in the 90-day window
• 30-45 minutes of uninterrupted focus for the first run

Architecture Overview

A prompt chain is a sequence where you paste the output of Step N as input to Step N+1. Unlike a single long prompt asking for everything at once, chaining forces the AI to reason deeply at each stage before moving forward. The result is more accurate, more structured analysis than any single mega-prompt can produce. For hotel revenue management, five chains cover the full 90-day analysis cycle.

Build Guide

Phase 1: Prepare Your Data Inputs

Before running the chain, assemble these inputs (15 minutes):

Input A — Current pace snapshot: A simple table with: date range (weekly buckets for 90 days), rooms on books, rooms forecasted, variance, and current ADR on books. Pull this from your PMS or RMS as a CSV export or type the weekly summaries.

Input B — Comp set rates: Current rates for your 5 comp set hotels for the next 90 days. Pull from Lighthouse rate shopping report or copy from OTA Insight. Format doesn't need to be perfect — even a text summary works.

Input C — Events list: Known events in your market for the 90-day window. List them as: Date, Event Name, Estimated Attendance, Venue (downtown/stadium/convention center). Check your CVB calendar, local events sites, and Amadeus Demand360 if you have it.

Input D — Prior year performance: RevPAR, occupancy, and ADR for the same 90-day window last year. Pull from STR or PMS historical reports.

Phase 2: Run the Five-Step Chain

Save this as a document and run each step in sequence. Copy the AI output from each step before running the next.

STEP 1 — Demand Intelligence

Paste this prompt, then fill in [INPUT A], [INPUT C], and [INPUT D]:

You are a hotel revenue analyst. Analyze the following 90-day demand picture for [Hotel Name] in [City].

Current pace data (rooms on books vs. forecast by week):
[INPUT A]

Known demand events in market:
[INPUT C]

Prior year performance for same period:
[INPUT D]

Produce:
1. A "demand heat map" summary: for each 4-week block, rate the demand outlook as Strong/Moderate/Soft/Unknown and explain why in one sentence
2. The top 3 demand-positive periods with brief rationale
3. The top 3 demand-risk periods with brief rationale
4. Any event overlaps or stacking opportunities I should price aggressively

Format as clearly labeled sections. Be specific — reference actual dates and events.

STEP 2 — Competitive Positioning Analysis

Paste this prompt, then paste [STEP 1 OUTPUT] and [INPUT B]:

Using this demand analysis:
[STEP 1 OUTPUT]

And this comp set rate data for the 90-day window:
[INPUT B]

Produce a competitive positioning analysis:
1. For each demand period identified in Step 1 (strong/moderate/soft), how is the comp set currently positioned?
2. Where is the comp set leading rate vs. following? Who is pricing most aggressively?
3. Where do we have an opportunity to price above comp set average?
4. Where is the comp set likely to adjust rate based on current patterns?

Be specific about which competitors are doing what and on which dates.

STEP 3 — Pricing Strategy Recommendations

Paste this prompt, then paste [STEP 2 OUTPUT] plus your pricing philosophy:

Using this demand and competitive analysis:
[STEP 2 OUTPUT]

Our hotel's pricing rules:
- Rate floor: [your floor weekday / weekend]
- MPI target: [your target, e.g., 105-110]
- Discount policy: [your rule, e.g., value-add before rate cut]
- Restriction triggers: [e.g., MLOS at 90%+ occupancy forecast]

Produce a 90-day pricing strategy:
1. For each demand period, give a specific rate recommendation (range or target ADR) and the rationale
2. Flag dates where I should activate restrictions (MLOS, CTA, advance purchase)
3. Flag dates where I should open value-adds instead of discounting
4. Identify two or three yield opportunities the comp set hasn't captured yet

Format as a table with columns: Date Range | Demand Level | Recommended Rate | Comp Set Positioning | Action Required

STEP 4 — Group and Channel Strategy

Paste this prompt, then paste [STEP 3 OUTPUT]:

Using this 90-day pricing strategy:
[STEP 3 OUTPUT]

Analyze group and channel implications:
1. Which date ranges should be closed or restricted to group business? Why?
2. Which soft demand periods represent group opportunities (where group revenue fills otherwise empty nights)?
3. Should any OTA availability be restricted or opened based on this strategy?
4. What channel mix adjustments would improve net revenue given this demand picture?

Be specific. Reference actual date ranges and demand levels from the prior analysis.

STEP 5 — Stakeholder Narrative

Paste this prompt, then paste [STEP 3 OUTPUT] and [STEP 4 OUTPUT]:

Using this pricing strategy and channel analysis:
[STEP 3 OUTPUT]
[STEP 4 OUTPUT]

Write a 90-day revenue strategy narrative for presentation to:
- Our General Manager (primary audience)
- Our ownership group / asset manager (secondary audience, more financial focus)

The narrative should include:
1. Executive summary: market outlook and our strategic positioning (3-4 sentences)
2. Demand highlights: top opportunities and risks (bullet format)
3. Pricing strategy overview: our approach and the rationale (2-3 paragraphs)
4. Group strategy recommendation (1 paragraph)
5. Forward outlook: what we're watching and when we'll reassess (1 paragraph)

Tone: professional, data-driven, confident. Avoid jargon. Write as if you're the revenue manager presenting to ownership.

Phase 3: Compile and Distribute

Take the outputs from Steps 3, 4, and 5 and assemble your 90-day strategy document:

• Step 5 narrative becomes the executive summary section
• Step 3 table becomes the pricing appendix
• Step 4 recommendations become the channel strategy section
• Add your own commentary where the AI got something wrong or missed local context

The finished document is typically 2-3 pages — suitable for an owner call or a strategy meeting.

Real-World Walkthrough: Q3 Strategy Session

Scenario: It's May. You're preparing the Q3 strategy presentation for an owner call in two weeks.

You spend 20 minutes pulling data (PMS pace, Lighthouse rates, CVB events, STR prior year). You run the five-step chain in a single Claude Pro session over 25 minutes. The outputs:

• A demand heat map showing July 4th weekend and a regional trade show in August as compression opportunities
• A competitive analysis showing Competitor A is holding rate aggressively on the trade show dates while Competitor C has dropped weekday rates through mid-July
• A pricing strategy recommending $20-30 premium on trade show dates, MLOS-2 for the July 4th window, and value-add packages (parking + breakfast) for the soft first-two-weeks-of-July period
• A channel recommendation to close Expedia on trade show peak nights
• A 3-page owner narrative covering all of the above in clear, professional language

Total time: 45 minutes from raw data to owner-ready document. Previously: 3-4 hours.

Customization Options

• Shorten for weekly use: Run only Steps 1, 2, and 3 weekly; save Steps 4 and 5 for monthly strategy sessions
• Extend the horizon: Adapt for 180-day or full-year budget analysis by adjusting the date ranges in your data inputs
• Add F&B layer: If you manage a full-service hotel, add an F&B revenue step between Steps 3 and 4

Maintenance

• Weekly: Run Steps 1-3 as a rolling update; paste new pace data each time
• Monthly: Run the full five-step chain for stakeholder presentations
• Quarterly: Revisit the prompt templates — if outputs are drifting from useful, tighten the instructions

What This Won't Do

• Pull live data automatically — you must paste in current numbers each run
• Replace your RMS for automated rate recommendations — this chain handles the analysis and communication layer
• Predict demand for truly novel events (new major stadium, one-time mega-event) without comparable historical data