HospisHospis
Soforthilfe
Back·AI

Our pricing doesn't exploit the data

The price list is fixed in autumn for the whole winter season, adjusted by gut feeling — and how demand actually ran only becomes visible once the week is over.

Typical for: resort hotels with a seasonal price list · houses that set prices once a year · operations without a revenue owner

Documented by Hospis

Updated: 6 September 2026

How you recognize it

  • The season's price list is made the year before and holds until the end
  • There is a discount for off-peak dates, but no premium for strong ones
  • There is no comparison: bookings on hand today versus the same day last year
  • The booking engine knows no rules, only prices
  • A revenue management system was offered and declined — too expensive, too complex
  • Sales does not know which week still needs rooms and which is full

Matching Hospi

JW

Josef Walch

Digitalisation · AI in operations · Systems — Lech am Arlberg
Works with protel · Mews · Mews POS · Smart Host
AdviseImplement

Puts pricing logic before tools: a rate frame with rules, three numbers a week, questions for your own systems — and only then AI that shows patterns.

Mapped to root cause
Rate frameMeasure demandQuestions for the dataTool with limits
View profilePersonally vetted · independent match

Which path fits your situation?

01

Diagnosis

390 € fixed price

A vetted Hospi analyses your situation in a structured way — in conversation and with a written result. Every statement clearly labelled: FACT, BENCHMARK, HYPOTHESIS or CONCLUSION. You get a concrete path, not a sales meeting.

  • A legitimate outcome is also: no engagement needed.
  • If an engagement follows, the diagnosis fee is fully credited.
02

Urgent support

Acute situation? Your case is reviewed with priority — response within 48 hours.

Cause fields — how to tell them apart

Data is rarely missing. What is missing is the habit of looking at it — and a price that is allowed to move. AI improves a pricing logic. It does not replace one that does not exist.

These fields are complete, and many properties get there on their own — that is what this page is for. The cost simply does not appear on an invoice: internal hours, a few attempts, and a season that keeps running in the meantime.

01

The price is a catalogue value

How you spot it

The price list gets printed, sent to partners and put on the website. After that it does not move — because moving it feels like breaking a promise.

The way out

The price list as a frame, not an endpoint: a base per period, plus rules for when the price may go up or down. Whoever communicates the frame breaks no promise.

02

Demand is not measured

How you spot it

Whether a week runs well, the house knows once it is over. Enquiries, cancellations and booking pace are not counted — they are experienced.

The way out

Three numbers every week: bookings on hand today versus the same day last year, enquiries this week, cancellations. Out of that grows a feeling that stands on numbers. Related problem: forecasting.

03

The systems hold the data but no questions

How you spot it

PMS, booking engine, channel manager — every system stores, none of them answers. Reports are not pulled because nobody knows which ones.

The way out

Set the questions first: which weeks book early, which late? Which channel brings which rate? Then check whether the PMS answers them. An AI tool can surface patterns in exports — but only once the question exists.

04

The system was seen as a replacement for judgement

How you spot it

Either the revenue system is expected to decide everything — then it gets rejected because nobody wants to give up control. Or it gets bought and never maintained.

The way out

The tool makes proposals, the person sets limits: floor rate, ceiling rate, rules for regulars. The first step is not software but a rule in the booking engine. Related problem: RevPAR.

Frequently asked

Our regulars expect the same price as last year
Regulars get rules, not chance: a fixed discount, an early booking window. That beats a price that fits everyone equally badly.
Does a revenue management system even make sense for a resort hotel?
From a size at which someone looks at it weekly. Before that, rules in the booking engine and three numbers a week are enough.
What can AI concretely do here?
Read booking patterns across several years, show outliers, justify rate proposals. What it cannot do: set the frame within which it is allowed to work.

Describe your situation

The problem context is automatically included — you do not need to repeat anything.

Problem
Our pricing doesn't exploit the data

Related situations

Our forecasts miss actual occupancy

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Our numbers are scattered across too many systems

Decisions take too long.

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