🔥 New · RM Copilot 2.0 - Voice mode is live

RevEvolve
Module 7 of 8 · Forecasting & Pricing

Pricing scenarios on the live data spine - in under 2 seconds.

Adjust. Recompute. Save. Share. Promote. The simulator reads from the same data layer as forecasting and pricing - every scenario runs against the current state, constraint-checked, and ties to the action audit log.

Most what-if exercises happen in spreadsheets - stale data, manual constraint checks, no link to the live forecast or the action engine. The scenario decks the team builds for owner reviews don't reconcile to the live dashboard, and the threshold updates that should follow the strongest scenarios never make it back into production. RevEvolve fixes that: scenarios run against the live spine, constraints are enforced automatically, and scenarios that consistently outperform the current rule surface as threshold update candidates for the action engine. The path to the next 1% of yield.

Run a Scenario Live - 30-Min DemoSee All 8 Platform Modules
  • <2 secscenario recompute on live data
  • Live spinesame data as forecasting + pricing
  • Save · Share · Promotescenarios become threshold candidates
  • +13.7%RevPAR lift in 10 days

Definition

What is a what-if pricing simulator for hotels?

Featured definition

A what-if pricing simulator for hotels lets operators propose hypothetical lever changes - rate, restrictions, comp set, demand, inventory - and project the outcome against the live forecast and constraint engine in real time. Modern simulators read from the same data spine as the production pricing engine, enforce brand parity / OTA / guardrail rules automatically, and promote outperforming scenarios as candidate threshold updates for the action engine.

Why spreadsheet what-ifs are broken.

Spreadsheet scenarios pull from a stale CSV, model demand on yesterday's forecast, skip the constraint engine, and produce numbers that don't reconcile to the live dashboard. The owner asks a follow-up question Tuesday and the math has shifted - because the underlying data shifted and the spreadsheet didn't.

The simulator runs against the live spine. Same forecast, same constraint engine, same comp set state as the production pricing engine. The math you see is the math the action engine would use.

Why scenarios should feed back into the rules.

Most what-if exercises end with a scenario PDF and a meeting decision. The threshold rule that should have changed in the action engine never gets touched, because the link between scenario and rule is manual.

The simulator closes that loop: scenarios that consistently outperform the current rule surface as candidate threshold updates with the audit linkage built in. The simulator is how operators find the next 1% of yield - not a one-off curiosity.

How the simulator works

Inputs in, scenarios out - live spine, no spreadsheets.

Seven inputs, multi-metric outputs, four-cadence refresh, three reasons to trust the result.

01

Inputs - what the engine reads

  • Live data spine

    Reads from the same data layer as forecasting, pricing, pace, comp set, and segments. Every scenario runs against the current state - not a stale snapshot.

  • Lever set

    Rate (BAR / member / OTA), restrictions (min-stay, advance-purchase, channel allotment), comp set (price moves, parity), demand (forecast shock up/down), and inventory (group block release, room-type shift).

  • Forward-night range

    Run scenarios across single nights, weekends, week-of, week-after, or 30-day forward range. The simulator handles each forward night with its own forecast tightness and pickup curve.

  • Constraint engine

    Brand parity rules, OTA contract floors, group block holds, and operator-defined guardrails are enforced. The simulator won't return a scenario that violates a hard constraint - it surfaces the constraint instead.

  • Comparison frame

    Each scenario compares against current trajectory, current forecast, prior-year same-day, and budgeted target. Scenario delta is shown in occupancy, ADR, RevPAR, and net contribution.

  • Action-audit linkage

    When a scenario gets promoted to a threshold candidate, the audit log records who proposed it, who approved it, the math behind it, and the outcome that followed. Defensible to ownership.

  • Stakeholder profile

    RM scenarios show occupancy + ADR + RevPAR. Owner scenarios show net contribution + budget delta. GM team scenarios show pickup velocity + comp position. Same simulator, different headline metric per audience.

02

Outputs - what every scenario delivers

  • Live scenario recompute - every lever change recomputes against the live data spine in under 2 seconds.
  • Multi-metric delta - occupancy, ADR, RevPAR, net contribution, and pickup velocity vs current trajectory.
  • Constraint surfacing - brand parity, OTA floor, group block, or operator guardrail violations flagged before commit.
  • Saved scenarios - stash a scenario for the Monday review, share with the GM, or promote to a threshold candidate.
  • Scenario packets - shareable PDFs with the lever set, the math, the constraint check, and the projected outcome. Investor / owner / brand grade.
  • Threshold promotion - when a scenario consistently outperforms the current rule, the simulator surfaces it as a candidate threshold update for the action engine.
03

Cadence - when the simulator updates

  • Real-time recompute

    Every lever change recomputes the scenario against the live data spine in under 2 seconds. No batch delay - the simulator is interactive.

  • On forecast change

    When the forecast tightens or loosens for a forward night, saved scenarios re-evaluate against the new state. Scenarios that no longer hold flag automatically.

  • Daily promotion review

    Each morning the simulator reviews saved scenarios against the prior 24 hours of outcomes. Scenarios that consistently outperform surface as threshold update candidates.

  • On-demand share

    Share scenarios with the team, GM, owner, or brand. PDF packets include the lever set, the math, the constraint check, and the projected outcome.

04

Why this is defensible

  • Same live data spine - not a sandbox.

    The simulator reads the same data as forecasting, pricing, comp set, and pace. Scenarios run against the current state - not a stale snapshot, not an analyst's spreadsheet model. The math you see in the simulator is the math the action engine uses.

  • Constraints enforced - not ignored.

    Brand parity rules, OTA contract floors, group block holds, and operator-defined guardrails are checked on every scenario. The simulator won't return a result that violates a hard constraint - it surfaces the constraint and the closest viable scenario instead.

  • Scenarios feed back - not throwaway exercises.

    Every saved scenario ties to the action audit log. When a scenario consistently outperforms the current rule, it surfaces as a threshold update candidate. The simulator is how operators find the next 1% of yield - not a one-off curiosity.

Operator use cases

Three scenarios where the simulator changes the call.

  • 01

    The weekend you want to lift but aren't sure by how much.

    Setup

    Wednesday morning. Saturday is forecast-strong. You're tempted to lift +$22 to match the comp set average. The reflexive question - "will pickup hold or stall?" - is answered by gut feel, not math.

    What the simulator does

    The simulator runs the scenario against the live data spine. Lift Saturday BAR +$22, all other channels parity-locked, comp set held constant. Result in 1.3 seconds: pickup velocity holds (0% drop in conversion based on past 12 weekends with similar forecast tightness), RevPAR +$11 vs current trajectory, net contribution +$8.40. Promote to action engine? Yes. Same-cycle pricing recompute fires.

    What this replaces

    The mental coin-flip. Most operators either lift +$22 reflexively (and discover Friday afternoon that pickup stalled) or hold and watch comp set out-yield them. The simulator answers the question with math anchored in the actual recent outcome data.

  • 02

    The group block release that the GM team is afraid to commit to.

    Setup

    Tuesday afternoon. A 90-room group block 35 days out is at 62% pickup vs the 21-day cutoff. Group team wants to release 80 room-nights to transient. Sales team is afraid the group will materialize anyway and the property will end up oversold.

    What the simulator does

    The simulator runs the release scenario against the live data spine. Release 80 room-nights × 14 nights, transient pickup curve at 7 days +$32 ADR vs group rate, group materialization probability 71% (historical for this group type), constraint check confirms no overbooking under 5th percentile materialization. Net contribution delta +$24.4K. Risk-adjusted EV +$18.8K. Group team commits.

    What this replaces

    Spreadsheet what-if math built by hand, prone to assumption errors, with no link to the live forecast or the constraint engine. Most decisions get made on the lower-risk side of "what could go wrong" - and revenue gets left on the table.

  • 03

    The board scenario the owner wants to see by 5 PM Friday.

    Setup

    4:18 PM Friday. Owner emails: "What if comp set drops $30 on the Memorial Day weekend? Build me a scenario for the board call Tuesday." Old workflow: GM tells RM team, RM team works the weekend, deck shows up Tuesday morning.

    What the simulator does

    The simulator runs the scenario in 90 seconds. Comp set -$30, demand forecast held constant, pricing engine response modeled three ways (full match, partial match -$15, hold). PDF packet generated automatically - lever set, math, constraint check, three sub-scenarios with delta in occupancy / ADR / RevPAR / contribution. Owner has it in inbox by 4:23 PM Friday.

    What this replaces

    Reactive analyst overtime that produces inconsistent scenario decks, often with assumption errors and no link to the live data state. Owner walks into the board call with numbers built by an analyst at 11 PM Sunday - the simulator gives the owner numbers built on the same engine that runs production.

The simulator workspace

Four views operators use weekly.

  • 01

    Lever workspace (default)

    Every lever - rate, restrictions, comp set, demand, inventory - adjustable on a forward-night range. Live constraint check on every change. The simulator recomputes the scenario delta in under 2 seconds.

  • 02

    Delta dashboard

    Multi-metric scenario delta - occupancy, ADR, RevPAR, net contribution, pickup velocity - vs current trajectory, current forecast, prior-year same-day, and budgeted target. Switch the headline metric to match the audience.

  • 03

    Saved scenarios

    Every scenario saved by the team, with creator, audience, headline metric, and last-recompute timestamp. Re-share, re-run against the latest forecast, or promote to a threshold candidate.

  • 04

    Threshold promotion

    Scenarios that consistently outperform the current rule surface as threshold update candidates. Approver review, comparison frame, and audit-log linkage built in. The simulator becomes the path to the next 1% of yield.

Platform integration

The simulator reads from 7 other modules.

Scenarios don't live in spreadsheets. They run against forecasting, pricing, pace, comp set, segments, reporting, and the portfolio view - in under 2 seconds.

Compared

How this compares to how you what-if today.

CapabilitySpreadsheet modelStandalone simulatorSingle Enterprise RMSRevEvolve
Recompute speedHours (rebuild spreadsheet)Seconds - separate modelVariable - batch refresh<2 seconds · live data spine
Data sourceStale CSV exportsImported snapshotSame vendor, separate layerLive spine - same as production
Constraint enforcementManual checks (often missed)LimitedAvailable - config-heavyBuilt-in · brand / OTA / parity / guardrail
Multi-metric deltaRevPAR onlyRevPAR + ADRConfigurableOccupancy · ADR · RevPAR · contribution · pickup
Save + shareEmail the spreadsheetInternal onlyLimitedSaved scenarios + PDF packets
Threshold promotionNot possibleNot providedManual rule editingPromoted scenarios become rule candidates
Defensible audit logEmail chainNoneAvailable - analyst-builtBuilt-in · ownership-ready
The simulator in the field

The threshold update that stuck.

A 22-property independent management company ran 14 weeks of weekend BAR lift scenarios in the simulator. The data showed +$22 lifts on forecast-tight Saturdays held pickup velocity 0% in 11 of 14 weekends - with material RevPAR upside. The simulator promoted the lift threshold to a candidate rule update; the action engine adopted it. The next 8 weeks of forecast-tight Saturdays ran on the new rule with consistent +$11 RevPAR vs the prior baseline.

  • +$11

    RevPAR lift on promoted threshold weekends

  • +13.7%

    Property RevPAR vs prior 30-day baseline

  • 22

    Properties on one simulator engine

We used to argue about lift thresholds in the Monday meeting. Now the simulator runs the scenario on Friday, the data shows it works, and the action engine adopts the rule. The argument moved from opinion to outcome - and the threshold updates that should have happened a year ago finally land.
Director of Revenue22-property independent management company · Midwest US
Read the full case study

FAQ

Simulator questions, answered.

Three structural differences. First, the simulator reads from the same live data spine as forecasting and pricing - not a stale CSV export. Scenarios always run against the current state. Second, every scenario is constraint-checked automatically - brand parity, OTA contract floors, group block holds, operator guardrails. The simulator won't return a result that violates a hard constraint. Third, scenarios that consistently outperform the current rule surface as threshold update candidates for the action engine. The simulator is how operators find the next 1% of yield - not a one-off exercise.

See it on your data

Run a scenario - on your live data.

Bring a recent decision you couldn't commit to - a weekend lift, a group block release, a comp set drop you wanted to test against. We'll run the scenario in the simulator on your live spine and show you the constraint check, the multi-metric delta, and what the threshold candidate would look like. The argument moves from opinion to outcome.

Run a Scenario Live - 30-Min DemoSee All 8 Platform Modules

Comparing simulators? See the side-by-side at /compare/ - or run the numbers at /roi-calculator/.

  • <2 second recompute
  • Live data spine - same as production
  • Constraint engine built in
  • Save · Share · Promote workflow
  • Threshold promotion to action engine