For Hotel Revenue Managers ·
What you'll accomplish
You'll build a repeatable workflow that takes raw rate shopping data from Lighthouse (or any rate tool) and turns it into a structured weekly competitive intelligence briefing — with pattern analysis, positioning recommendations, and a written rationale you can send straight to your GM. Instead of staring at columns of competitor rates and forming your own interpretation in isolation, you'll have a consistent analytical framework that takes 10 minutes a week to run.
What you'll need
Add competitive context to your ChatGPT Plus custom instructions (see the Revenue Reporting guide for how to access Custom Instructions). Include:
Competitive context for [Hotel Name]:
- Comp set: [Comp 1 — brand, room count, segment], [Comp 2], [Comp 3], [Comp 4], [Comp 5]
- We typically position [at parity / at a premium of $X-$Y / as value leader] vs. the comp set
- Our key differentiators: [e.g., location, renovation, review score, F&B, loyalty program]
- Rate floors: $[X] weekday, $[X] weekend, $[X] peak/event
- We use [Lighthouse / OTA Insight / manual checks] for rate shopping
- When evaluating competitive moves, I want: pattern identification first, then positioning recommendation, then rationale I can share with the GM
Competitive intelligence analysis only works if the data you paste is consistently structured. Pick one format and stick with it.
Option A: Table format (copy-paste from Lighthouse export)
Date | Hotel Name | Standard Room Rate | Notes
[paste Lighthouse table here]
Option B: Manual format (if you're doing spot checks)
Date: [X]
[Comp 1]: $[rate] — [room type, any restrictions noted]
[Comp 2]: $[rate]
[Comp 3]: $[rate]
[Our rate]: $[rate]
Notes: [any cancellation policy changes, package additions, BAR vs. promotional]
Consistency matters more than completeness — 5 competitors tracked consistently every week is more useful than 10 checked sporadically.
In ChatGPT Plus, create a new chat titled "Weekly Rate Intelligence — [Hotel Name]" and keep it ongoing. Each week's analysis builds on prior context — after a few weeks, ChatGPT will begin identifying patterns across sessions.
Paste your rate data with this analysis prompt:
Here's this week's comp set pricing for [Hotel Name] for the next 21 days:
[paste your rate table]
Please analyze:
1. Overall competitive positioning — where is the market right now vs. last week?
2. Which competitors are leading rate vs. discounting vs. holding?
3. Any notable pattern changes (comp that moved early, unusual restrictions, price compression)?
4. What is the implied demand signal in how the comp set is pricing?
5. Recommendation: Should [Hotel Name] match, undercut, hold premium, or adjust by segment for this period?
Give me a 3-paragraph briefing I can share with my GM.
Full weekly competitive briefing:
Here's our comp set rate data for the next 21 days: [paste data]. We're currently priced at [our rates by date]. Give me: (1) market positioning summary — where are we vs. comp set, (2) pattern analysis — what's changed since last week, (3) recommendation for any rate adjustments in the next 7 days. Format as a briefing I can share with my GM.
Specific date deep-dive:
I'm looking at [specific date or weekend]. Here's what the comp set is doing: [paste rates]. Our current rate: $[X]. Demand signal: [what your pickup data shows — ahead/behind pace]. Should we hold, move up, or adjust restrictions? Give me a recommendation with rationale.
Event pricing strategy:
[Event name] is happening in [city] on [dates] — [attendance estimate], [event type]. Here's what the comp set is currently pricing for those dates: [rates]. We're at $[X]. Based on historical event patterns and the current comp set positioning, recommend a rate strategy: where should we open, when should we close out lower categories, and what restrictions (MLOS, advance purchase) make sense?
Comp set movement alert analysis:
[Comp hotel name] just dropped their rate from $[X] to $[X] for [date range]. It's [X] days from arrival. What are the most likely reasons for this move? Should we respond, and if so, how? Context: our current pace for those dates is [X]% of forecast.
Week-over-week trend summary:
Here's this week's comp set data: [paste]. Here's last week's for the same future dates: [paste]. What changed materially? Is the market strengthening, softening, or staying neutral? Is our rate position better or worse vs. the comp set compared to last week?