Dynamic pricing at 456 Embarcadero Inn & Suites reduced the time spent managing rates from five hours a week to one hour a month. Georgia’s Eco House Merisi solved a similar problem differently: according to information shared with HotelCafe.ge by the owners, when Exely’s standard calculation did not suit the property, they developed their own model with ChatGPT.
In practice, hotel dynamic pricing can rely on a ready-made algorithm or property-specific rules. Both cases show that a small hotel does not necessarily need a dedicated revenue manager to respond to demand faster. But a standard algorithm will not fit every property, and automation does not remove the need for owner oversight.
456 Embarcadero: about 20 hours of work per month
456 Embarcadero Inn & Suites is a small 32-room boutique hotel by the ocean in the United States. When general manager Patrick Mulholland took over the property in 2021, rates were still being adjusted manually.
Manual rate management took about five hours every week. Even then, the team could not always react quickly enough when demand increased and sometimes sold rooms for less than the market would bear.
The hotel introduced RoomPriceGenie, a revenue-management system integrated with its Cloudbeds PMS. The algorithm began calculating and updating rates based on demand, while the manager retained the ability to adjust suggested prices, set minimum stays and update several room categories at once.
After implementation, rate management fell to roughly one hour a month instead of five hours a week. Using four weeks per month, that is an approximately twentyfold reduction in time.
The manager also reported higher ADR and revenue. Exact before-and-after financial figures were not published, so the proven quantitative result is the time saved, not a specific percentage increase in income.
Why hotel dynamic pricing is not simply raising rates
The goal is to bring the room price closer to current demand. If rooms for a date are selling faster than usual, the rate can rise. If arrival is near and inventory remains high, a lower price may be needed.
Occupancy, day of week, booking lead time, season, events, holidays and competitor prices may all affect the calculation. Software can check these signals more frequently than the manager of a small hotel who is also responsible for guests, staff and daily operations.
However, software does not know a property as well as its owner. It may miss unusual room features, a distinctive audience or a local demand driver. Minimum rates, exceptions and regular performance reviews should therefore remain under human control.
Three ready-made solutions worth comparing
A hotel does not always need a separate RMS. Dynamic prices may be calculated inside a PMS, through a connected module or by an external system. Georgian hotels can compare at least three approaches:
- Exely Price Assist analyses occupancy, demand, seasonality, holidays and local events. Recommendations may be approved manually or applied automatically;
- RoomPriceGenie uses PMS data, booking pace, competitor prices, seasonality and events, with minimum and maximum rate controls;
- Cloudbeds connects with external RMS products, including RoomPriceGenie and Rate Quantum, which receive PMS data and return recommendations or updated rates.
This is not a ranking or an exhaustive list. A standard function may be entirely sufficient for a typical city hotel. Compare integration with the existing PMS, whether rules can be adjusted, the level of automation and total cost. The presence of a dynamic-pricing feature alone does not guarantee that its model suits a specific property.
Eco House Merisi: calculating rates with ChatGPT
According to the owners of the Georgian mountain hotel Eco House Merisi, speaking with HotelCafe.ge, Exely’s standard calculation did not suit the property. Its seasonality, audience and sales drivers are unusual. The owners therefore described their own pricing conditions and used ChatGPT to create a model that recommends rates from the hotel’s data.
The owners say ADR, turnover and other indicators improved noticeably. They do not disclose exact amounts or percentages because those are internal commercial data. The approach is still useful for other hoteliers: when a universal algorithm does not reflect real demand, it can be replaced or supplemented with logic designed for the property.
Recommended rates are still transferred manually into Exely. This takes about ten minutes once or twice a week—far less than continuously monitoring the market and calculating every rate by hand.
Why this matters particularly for Georgian hotels
Demand in Georgia changes substantially through the year. Galt & Taggart reported that, in its Batumi hotel sample for 2025, occupancy ranged from 39% in February to 91% in August, while ADR excluding VAT ranged from $70 in November to $144 in August. In its Tbilisi sample, occupancy moved from 37% in January to 74% in July.
These are aggregated figures, not results for every property, but they show why one fixed rate for a long period may fail to match actual demand. A city hotel, seaside resort and small mountain property should not be evaluated by the same model.
Even a successful model needs periodic checks. Eco House Merisi’s owners review the conditions roughly twice a year and adjust or test rules when necessary. The aim is not to change them every time, but to confirm that the calculation still reflects the market.
What to measure before and after implementation
Before launch, record a baseline and compare like-for-like periods afterwards:
- ADR;
- occupancy;
- RevPAR;
- room revenue;
- booking lead time;
- the share of direct and OTA sales;
- time spent managing rates;
- system and implementation costs.
Compare the same months or periods with similar demand. Otherwise, ordinary seasonal growth can easily be mistaken for the effect of the algorithm.
Key takeaway
456 Embarcadero shows how strongly a ready-made system can reduce routine work—from around 20 hours to one hour a month. Eco House Merisi demonstrates the other route: when a standard calculation does not fit, owners can define their own factors and build a property-specific model.
Hotel dynamic pricing must be tested against the hotel’s own data. There is no single solution for every property. Check which factors the system uses, whether its rules can be changed and whether ADR, RevPAR and revenue improve. It is not a magic button, but a practical revenue tool that saves time when it is configured well and reviewed regularly.