Hotel owners and lodging REITs

Hotel revenue management AI: keep forecasts current as market signals move.

SquareShift builds hotel revenue management AI for hotel owners and lodging REITs. It keeps the outlook current as booking pace, cost, and market signals move.

Plan a proof of concept

The gap

Keeping forecasts current as signals change

Booking pace, group wash, channel mix, comp set movement, events, airlift, labor, renovations, and macro pressure change every week. Static forecast packets make teams explain variance after the value has already moved.

Five places forecast work breaks at scale

  1. 01

    Pace and forecast drift apart.

    Rooms, ADR, RevPAR, and group pace move before review.

  2. 02

    Assumptions are hard to audit.

    Market, channel, and expense assumptions live in many files.

  3. 03

    Downside cases arrive too late.

    Leaders need impact ranges when demand or cost signals move.

  4. 04

    Portfolio rollups hide the asset story.

    A stable rollup can hide movement in one market or asset.

  5. 05

    Reviews spend time assembling evidence.

    Teams need the variance, driver, source, and next action together.

The five forecast checks

SquareShift builds AI for hotel revenue management that explains outlook movement with evidence.

Each answer compares current forecast, live signals, assumptions, and downside cases, then routes the right exception for review.

How forecast intelligence works

We build AI agents that refresh outlooks as booking, cost, and market signals move.

Forecasts, property data, market feeds, and review rules go in. Variance drivers, downside cases, assumption gaps, alerts, and executive views come out inside your environment.

  • No upfront data overhaulStart with one market, one portfolio slice, or one forecast workflow.
  • Four-week planOne forecast workflow, live on approved data.
  • Yours through the build and beyondYou own it, run it, and extend it.
  • Your data, your environmentNothing leaves. Your access rules apply.

Forecast and signal data

  • PMS/CRS and booking pace
  • Group blocks and wash
  • ADR, RevPAR, and rate-shop feeds
  • Events and seasonality
  • Flight and airlift trends
  • Labor, expense, and macro indicators

Assumption layer

  • Forecast versions and ownership
  • Rooms, ADR, RevPAR, and expense drivers
  • Scenario and downside-case features
  • Data quality and variance checks
  • SquareShiftBusiness context engineeringMakes forecast assumptions make sense to AI

Forecast agents

  • Pace variance
  • Rate and mix change
  • Downside case
  • Assumption gap
  • Portfolio outlook
Runs on approved data

Executive workspace

Forecast review list

Variance, driver, scenario, evidence, and route.

Forecast action

Controls on every layer

  • Runs inside your environment
  • Approved data only
  • Your forecast rules and thresholds
  • Every alert traced to source
  • Finance keeps final judgment

Start with one forecast question. Extend the pattern from there.

See the four-week plan

Forecast reviews in hospitality revenue management

Owners review booking pace, costs and market signals to see where the outlook changed.

Four-week plan

Start with one workflow on your data.

We build the workflow in your environment. Your team tests it against approved forecasts, property data, market signals, and review rules.

Explore SquareShift case studies.

Map your proof of concept

Four-week plan

What you bring

  • Forecast question
  • Assets and versions
  • Data and review rules
  • Environment access
  • Review team time

What SquareShift delivers

  • Forecast workflow
  • Assumptions and rules
  • Variance and cases
  • Environment controls
  • Team read-out
Week 1Scope assets, versions, and signals.
Week 2Build assumptions and checks.
Week 3Explain variance and cases.
Week 4Test, read out, decide next.