The problem is that "most of the time" is not when it matters most.
Forecasts are built on the past
A traditional RMS looks at historical booking patterns and uses them to predict how a future departure will fill. If last year's Friday evening sailing to Tallinn sold out three weeks in advance, the system assumes this year's will too, and prices accordingly.
This works until it doesn't. A competitor drops their prices for the same route. A major event pulls unexpected demand into a normally quiet Tuesday. A corporate client books 40 cabins with two weeks' notice. None of these scenarios were in last year's data, and none of them will be handled correctly by a system that is looking backward.
By the time the forecast catches up, the departure has already been mispriced for days.
The staircase has the same problem
Many operators who don't use a full RMS rely on a staircase model instead: prices rise as the departure fills. It's simple, predictable, and easy to explain to customers.
But a staircase only moves in one direction. If a departure is filling slower than expected, the price stays where it is. The system has no mechanism to lower prices and stimulate demand. Seats go unsold, and the departure departs half-empty at a price nobody wanted to pay.
The same logic applies in reverse. If a sailing is filling faster than the staircase anticipated, early bookers got a price that was too low. Revenue that could have been captured wasn't, because the staircase didn't know demand was going to be that strong.
Adapting is faster than predicting
The alternative is not a better forecast. It is a system that stops trying to predict what demand will be and starts responding to what demand actually is.
When a departure starts filling faster than usual, prices adjust upward automatically. When bookings slow down unexpectedly, prices move to stimulate demand before it is too late to fill the sailing. The algorithm does not need to know why demand is behaving the way it is. It only needs to see that it is, and respond.
This is what Priceff does. Every departure is treated as its own pricing opportunity, updated continuously based on real-time booking behaviour. Passenger tickets and vehicle spaces are priced independently, because their demand rarely moves in sync. And when something unexpected happens, the price moves with it rather than waiting for the next forecast cycle to catch up.
The cost of being one step behind
On a busy route with multiple daily departures, the cumulative effect of mispriced sailings adds up quickly. A few percentage points of revenue left behind on each departure becomes a significant number across a full season.
The question is not whether your current system is working. It probably is, most of the time. The question is what it is costing you on the departures where it isn't.
