What the Early-booking versus last-minute balance model prompt does

Operate as a hotel revenue strategist who balances early commitment, cancellation risk and last-minute pricing power using net contribution and booking-window evidence.

The prompt will, at minimum:

  • Build comparable booking-window cohorts by stay date, segment, channel, room type and season; correct for cancellations, no-shows, taxes, commissions and changed inventory
  • Measure net ADR and contribution by lead-time band rather than assuming early bookings are cheaper or last-minute bookings are more profitable
  • Model base, early-demand-heavy and late-demand-heavy scenarios with explicit assumptions, uncertainty and capacity protection
  • Design differentiated advance-purchase, flexible, fenced and last-room controls by segment; avoid blanket discounts that cannibalise willing-to-pay demand
  • Define release dates, stop-sell rules, reforecast triggers, test cells and review cadence so the policy can adapt without uncontrolled rate changes

Who it is for

Gökhan Güzel's hospitality prompt for Gemini users: marketers, founders, agencies and consultants who need an auditable, evidence-based deliverable instead of generic advice.

What you get

  • Booking-window evidence and assumptions register
  • Net contribution curves by lead-time band
  • Early-versus-late scenario scorecard
  • Recommended rate-fence and inventory policy
  • 30/60/90-day test and governance roadmap

Variables

PlaceholderPurpose
{{adr_by_lead_time}}Numeric value or table; state formula, numerator, denominator, unit, currency, tax treatment, period and source
{{advance_purchase_terms}}Approved rule, policy or constraint; state owner, version, scope, jurisdiction and effective date
{{booking_window_data}}Structured dataset or source file; state fields, data types, period, units, currency, time zone and provenance
{{cancellation_data}}Structured dataset or source file; state fields, data types, period, units, currency, time zone and provenance
{{channel_costs}}Numeric value or table; state formula, numerator, denominator, unit, currency, tax treatment, period and source
{{competitor_rates}}Numeric value or table; state formula, numerator, denominator, unit, currency, tax treatment, period and source
{{demand_segments}}Target audience, segment, persona, customer/player or industry group
{{discount_rules}}Approved rule, policy or constraint; state owner, version, scope, jurisdiction and effective date
{{forecast_horizon}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{hotel_name}}Verified identifier or text value; state exact spelling, source, status and validity scope
{{no_show_data}}Structured dataset or source file; state fields, data types, period, units, currency, time zone and provenance
{{occupancy_by_lead_time}}Numeric value or table; state formula, numerator, denominator, unit, currency, tax treatment, period and source
{{property_location}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{revenue_goal}}Numeric value or table; state formula, numerator, denominator, unit, currency, tax treatment, period and source
{{risk_tolerance}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{season_calendar}}Date, time or period value; state ISO format, time zone, start/end boundary and comparison period
{{target_market}}Target market

How to use

Copy the prompt with the button above, replace every {{placeholder}} with your verified data, and paste it as the first message in a new Gemini conversation. The prompt runs a short question gate first; answer it, then the deliverable is produced.

Run Early-booking versus last-minute balance model in Gemini

Open a new Gemini chat, paste the filled-in Early-booking versus last-minute balance model prompt and answer the short question gate. Gemini then returns the executive decision, the evidence ledger and the task-specific tables in one reply.

Source

Original file in the multilingual prompt library on GitHub: