What we build
A revenue-management stack that replaces Excel — and earns the owner's trust.
Every capability is production-grade infrastructure — wired to your PMS and channel manager, tracked in MAPE, and owned by a team that ships weekly.
Demand forecasting with pickup-curve modelling
Booking velocity, historical seasonality, pace variance, and search-signal intent combined into a 365-day demand forecast. Per room type, refreshed nightly, accuracy tracked in MAPE.
Per-channel rate layering
One BAR decision flows through layered rate structures — wholesale net, corporate LRA, OTA public, direct member — with per-layer floor and ceiling controls the revenue team actually trusts.
Event-aware pricing calendars
DHL, IATA, local CVB, airline schedules, F1, MICE, and public-holiday feeds wired into demand signal. Pricing calendars update when a new event lands — not a month later.
Length-of-stay + MLOS controls
Minimum-stay, CTA, and CTD restrictions applied to high-demand arrival patterns automatically. Weekend-arrival guards, shoulder-night protection, and group-displacement logic built in.
Competitor-set monitoring
STR, HotelIQ, OTA Insight shops — hourly. Parity drift, compset median shifts, and RGI / MPI / ARI tracked per property, with alerts when your position vs compset materially moves.
Forecast vs actuals variance reports
Weekly owner-ready forecast-vs-actual with the 'why' — booking pace, channel mix, ADR moves, segment shifts — and the model's own learnings. No mystery, no spreadsheets.
Who this is for
One engine, every yield motion.
Same forecasting, open-pricing, and variance-review infrastructure — tuned per property size, demand type, and market context. Every segment below runs on the same core; only the feature weights and compset logic differ.
80-200 room independents
Single-property independents transitioning from manual spreadsheet-era revenue management to an AI-tuned system. Fast-payback use case with high RevPAR sensitivity to pricing improvements.
200-500 room urban hotels
Corporate, MICE, and leisure mixed-demand properties with complex rate layers, group displacement decisions, and corporate LRA contracts. Benefits most from layered open-pricing.
Resort + destination properties
Long booking windows, F&B and spa revenue leverage, shoulder-season sensitivity, cancellation profile management. Pickup-curve modelling delivers the biggest lift here.
Multi-asset hotel groups
Portfolios of 5-30 properties wanting consistent revenue-management discipline with per-property tuning. Shared models where it makes sense; per-market calibration where it doesn't.
Brand-level enterprise deployments
Hotel brands with dedicated revenue-management teams looking to augment (not replace) the team with a transparent AI layer that owns the repetitive work, not the strategy.
Migrating from Excel-era RM
Groups currently running revenue management in spreadsheets, email threads, and weekly meetings. Moving to an AI-native system without losing the revenue-manager's intuition.
Model families we deploy
No single model cracks yield on its own. So we ensemble.
Four specialised models — forecasting, pickup-curve, optimal-price, and event-impact — each tuned to a specific job in the revenue-management loop.
Hybrid Prophet + LSTM ensemble producing 365-day occupancy and ADR forecasts per room type. Handles multi-seasonality (weekly, quarterly, yearly) and holiday effects natively.
Gradient-boosted regressor on booking lead-time vs. realised occupancy. Separates 'pace ahead' from 'pace behind' signals — even when absolute on-the-books looks normal.
Reinforcement-learning agent that sets BAR within human-defined floor/ceiling guardrails — optimising RevPAR across a rolling window while honouring LOS and channel-mix constraints.
Classifier that maps incoming events — conferences, flights, holidays, festivals — to their demand-impact category and timing. Learned from past event-vs-pickup patterns per property.
Data sources wired into every model
Every signal that moves the forecast — integrated.
Pulled in parallel, normalised into a single demand + rate schema, versioned alongside the model that consumes them.
Explainability, not just predictions
A rate move is a decision — and every decision carries its reasoning.
Every BAR change, LOS restriction, and rate-layer push carries a reasoning trail — which signals fired, which guardrails applied, and why the model chose the value it did. Revenue managers audit before it ships and override with a single click.
Not a black box. A control panel.
- Per-rate-move reasoning + signal attribution
- Override log with reason codes and model-vs-actual
- MAPE + variance reporting to owners monthly
- Champion / challenger comparisons on request
Why Axccelerate for revenue management
Not a spreadsheet.
Not a black box.
A legacy RMS gives you one number. Our stack gives you forecasting, open pricing, compset, variance reporting, and override logs — the infrastructure a real revenue team actually needs.
Pricing
Priced to the portfolio, not the room night.
Revenue-management deployments are custom — we scope against your PMS, integrations, and property mix before quoting.
Glossary
The vocabulary behind every rate decision.
A quick reference for the acronyms that show up in revenue-management reviews — the terms your revenue team, owner, and STR report will all use.
- RevPAR
- Revenue Per Available Room
Rooms revenue divided by available room nights. The industry's combined price-and-occupancy yardstick and the single most-watched hotel KPI.
- ADR
- Average Daily Rate
Rooms revenue divided by rooms sold. The pure price metric — separates price discipline from occupancy mix.
- Occupancy
- Rooms sold ratio
Rooms sold divided by rooms available. Combined with ADR to produce RevPAR. Occupancy alone rarely tells the whole story.
- RevPAG
- Revenue Per Available Guest
Total revenue (rooms + F&B + spa + other) divided by available guest nights. Picks up ancillary-revenue leverage that RevPAR misses.
- GOPPAR
- Gross Operating Profit Per Available Room
GOP divided by available rooms. The margin-aware counterpart to RevPAR — what asset managers and owners actually care about.
- Demand forecast
- Forward demand model
Predicted room nights and ADR at a future date. Confidence intervals matter — a 75% confident forecast at +5% pickup is actionable; a 40% one isn't.
- Pickup curve
- Booking velocity pattern
How bookings accumulate vs lead-time to arrival. Compared against historical pickup curves to detect pace ahead/behind signals before occupancy reveals the truth.
- BAR
- Best Available Rate
The lowest publicly-available unrestricted rate at a property for a given date. Most rate-parity clauses compare BAR across OTA and direct channels.
- LOS control
- Length-of-Stay restriction
Minimum-stay, maximum-stay, CTA (closed-to-arrival), and CTD (closed-to-departure) restrictions. Yield-critical on high-demand arrival dates.
- Open pricing
- Layered rate architecture
Rate philosophy where each rate layer (BAR, member, corporate, wholesale) moves independently relative to a BAR signal, rather than via fixed discounts off BAR.
- Compset
- Competitive set
A defined group of hotels you benchmark against — typically 4-8 properties in the same market, price band, and customer segment. Source: STR, HotelIQ, or self-defined.
- STR index
- Smith Travel Research benchmark
STR produces occupancy, ADR, and RevPAR vs-compset indices — the industry's neutral benchmark. Used in most owner reporting and RMS performance review.
- MPI / ARI / RGI
- Market Penetration · Average Rate · Revenue Generation Index
STR fair-share indices. MPI = occupancy vs compset; ARI = ADR vs compset; RGI = RevPAR vs compset. An RGI of 1.10 means you captured 10% more than fair share.
- Yieldable
- Yieldable inventory
The room inventory available for dynamic pricing — excluding out-of-order rooms, comps, house-use, and long-stay-contracted rooms. The base the RMS actually controls.
Your yield decisions, engineered.
30-minute scoping with a senior engineer and a revenue-systems operator. You'll leave with a model plan, integration sketch, and realistic timeline — not a sales pitch.