Hotel Optimize Prices but Ignore the People Paying Them

Nowadays we face rich data revenue management strategies while psychology-poor approaches.

When Sarah Chen booked her hotel room, she faced two options: $189 for a non-refundable rate or $340 for the same room, fully refundable up to 24 hours before check-in. She had no plans to cancel. Her trip was confirmed: flights booked, meetings set, and a friend's wedding to attend that weekend. She clicked "refundable" anyway. Revenue managers would see this as a win. A guest chose the higher-margin rate, just as the pricing model predicted. Mission accomplished. But if you ask Sarah why she made that choice, her answer will have little to do with pricing strategies or rate class optimization. It relates to anxiety, a need for control, and the simple discomfort of feeling trapped in a decision she couldn't reverse, even when the chances of needing that flexibility were almost zero. This is the blind spot at the heart of modern hotel revenue management that some hoteliers still are ignoring, basing their decisions on pure mathematical algorithms.

The industry has created highly advanced systems to predict what guests will pay, whereas it has invested very little in understanding why they make those choices. Many companies look for numerical analysts to fulfil the revenue management positions, but in many cases, the real trigger for the price selection is ignored because it cannot be shown with numbers. Latest academic research publications show that consumer orientation and behaviour are not yet standard practice (Vrionis & Sotirios, 2026). We could say that around 90% of hotels in Europe have adopted dynamic pricing approaches but the behavioural layer is still quite unknown and unused.

As I explain in my revenue management classes at Les Roches Marbella, we tend to think that our clients are rational actors who are purely price-elasticity based in their buying decisions, but behavioural theories demonstrate they are not. We should support our analytical approach in revenue management by borrowing tactics from a field that tries to explain the buyer's economic decisions. Revenue management has perfected the math of the guest while it has left the mind of the guest unexamined.

What are the theories that can help us to cover this blind spot?

Anchoring & reference pricing

The first price a guest sees becomes a mental benchmark against which every other price is judged, and it has nothing to do with the "true" market value, but whatever number happened to load first. A guest who sees a $400 rack rate before a $250 discounted offer feels like they are getting a good deal, while a guest who sees $250 as their starting point and later gets quoted $300 elsewhere feels nothing at all. RM systems rarely account for which anchor a guest saw first. Indeed, in the majority of cases, the discounted price is shown first.

Loss aversion / price fairness perceptions

Behavioural economics has demonstrated that people feel the pain of a price increase roughly twice as intensely as the pleasure of an equivalent discount. That means algorithmic pricing, even when it's mathematically optimal for revenue, can generate a disproportionate sense of being penalized rather than simply charged more. This is why a guest can understand intellectually that demand is high, yet still feel the hotel "did something to them," damaging trust in ways that no RM dashboard captures.

Choice overload

Classic research on decision fatigue shows that too many options do not empower guests; it has the opposite effect, paralyzing the choice, often pushing them to default to the cheapest option, abandon the booking entirely, or experience regret after choosing. RM's instinct is to add rate fences and package tiers to capture more segments, but past a certain threshold, more choice architecture actively works against conversion rather than for it.

Mental accounting

People don't treat money as fully fungible; they mentally sort it into separate silos. A guest might refuse spending $50 on a room upgrade but happily spend $50 on a spa treatment during the same stay, because one feels like "the room" and the other feels like "the experience." RM systems that only optimise the room-rate silo leave untouched money on the table, and mispricing the room can bleed guest goodwill that would have otherwise funded spend elsewhere.

Social proof / herd behaviour

People look to others' choices as a shortcut for their own decision-making; by nature, we have a lazy approach towards deciding. This is exactly why OTAs plaster booking pages with "12 people are looking at this room" and "booked 8 times today." OTAs have built entire interfaces around this bias, yet hotel RM strategy, which controls the actual pricing decisions behind those interfaces, rarely designs its own rate presentation with social proof in mind, ceding the behavioural lever to the distribution channel instead of owning it.

In the current hotel sales environment, with rising sensitivity to price manipulation, all of the above behavioural theories can be of great help. The use of AI can now assist in the personalisation of commercial outcomes to avoid the feeling of manipulation among potential guests. Our competitive advantage may well lie in integrating psychology into RM to build guest trust and loyalty, enhancing long-term revenue generation.

Without doubt, we should integrate psychology into how our prices are framed, sequenced and presented; they are not just numbers appearing on a screen. That does not mean abandoning sophisticated forecasting and pricing engines, which are indispensable if you want to adjust prices in response to rapid changes in demand. But by reframing anchors, curating choices designed to drive spending on experiences, and bringing social proof of demand in-house as OTAs do, we can optimise revenue generation and profitability, treating the guest not as a price point to be optimised, but as a person making psychologically influenced decisions.

AI is making price precision and forecasting better and quicker than ever, but at the same time it is blind to the psychological layer that represents half of the necessary inputs. Those who pay attention to this layer will be the winners in the next decade. It is not all about logarithmic math; we need to bother to understand the person behind the choice of price.

References

Vrionis, Ivana & Varelas, Sotirios. (2026). Implementing Sustainable Habits in Dynamic Pricing Through Revenue Management Strategies in the Hotel Industry — A Literature Review. 10.1007/978-3-032-12968-0_83.