Choose an RMS on the quality of the data it can read, the control it leaves you, and how well it explains a recommendation. Ask to see the reasoning behind a specific price, not just the price. Run it in advisory mode for a full season before letting it publish rates automatically, and measure against your own baseline rather than the vendor's.
An RMS needs clean history from your PMS, live pricing from the market, and your own constraints. If your last three years of data are messy, if room types were renamed, or if the connection only reads bookings and not cancellations, the model learns from a distorted picture. Ask what the system ingests, how far back, and what it does when history is thin.
Ask about market data specifically. Some systems read competitor rates from public sources, others use aggregated booking data, and the two answer different questions. Understanding where the outside signal comes from tells you when to trust the recommendation and when to override it.
Pick a date in the demo and ask why the system suggests that price. A good answer names the drivers: pace against last year, remaining inventory, the competitive set, an event, a length of stay pattern. A vague answer about the model means you will be defending prices to an owner with nothing but faith.
This matters more than accuracy in the first year. A revenue manager who cannot explain a rate will quietly stop using the tool, and an unused licence is the most expensive software there is.
Skip the research, talk to someone who has done it 45 minutes with an independent specialist. Free for hotels, no pitch, no commissions. Book a free session →Every serious system supports advisory mode, where it recommends and a human publishes. Use it for a full season. Log the cases where you disagreed and check afterwards who was right. That log is the only honest evidence about whether the system fits your property.
Then look at the guardrails: minimum and maximum rates, blocked dates, rules for groups and for long stays. You want a system that lets you fence off the decisions you will never delegate, without turning every fence into a support ticket.
RMS pricing is a recurring fee that scales with rooms and modules, and vendors quote it against your inventory rather than from a list. Whatever the number, translate it into the ADR multiplied by occupancy rate. It is the standard headline metric for comparing hotel">RevPAR lift you would need to break even, then check whether that lift is plausible for your occupancy and season shape.
Set the baseline before you start. Record last year's RevPAR, ADR and occupancy by month, and agree with the vendor how you will compare. Without a baseline agreed in advance, the first review meeting becomes an argument about which numbers to use.
There is no universal threshold. What matters is variability: a hotel with strong seasonality, several segments and volatile demand gets value at a smaller size than a property with flat year round occupancy. If your rates barely change across the year, the system has little to optimise.
No. It replaces the repetitive part of the job, which is recalculating prices across dates and room types. Strategy, segment mix, contracting and the judgment calls around events still need a person. In practice the system makes one revenue manager able to cover more properties.
Models trained on history handle the unprecedented badly, which is exactly when you want manual override to be fast and obvious. Ask in the demo how you freeze pricing across a date range in one action, and how quickly the system recovers once normal patterns return.
Eventually, yes, because manual publishing across channels reintroduces the delay you paid to remove. But automatic writeback should be earned. Start advisory, automate the room types and date ranges where the system has proven itself, and expand from there.
Clean room type definitions, consistent segment tagging and at least two years of history with cancellations recorded properly. Fixing that data first is unglamorous and it determines how useful the first six months of recommendations will be.
Whoever chairs the revenue meeting, because that is where recommendations become published rates. Ownership by a general manager works at small properties, provided someone has time to review the recommendations weekly rather than monthly.