Are we taking segmentation to the extreme, or is this simply Revenue Management taken to perfection?

The objective of Revenue Management has always been “selling the right room to the right customer at the right price, through the right channel, at the right time, with the right cost efficiency.”

To achieve this, the key has always been to have the greatest possible quantity and quality of data and variables to determine the best strategy.

Until now, pricing strategies, although dynamic, have mainly been built around defined segments: Corporate, Leisure, Groups, Wholesale, etc.

But what happens when technology allows us to go beyond traditional segmentation?

Artificial Intelligence is bringing an unprecedented level of computing power and data processing capabilities to Revenue Management. We are already seeing how algorithms can process an increasing number of variables in real time, adapting to demand, market behaviour, competitor activity and macroeconomic factors.

But what if, in addition to understanding the market and the segment, we could understand the individual customer?

What if an algorithm could combine hundreds of signals, such as booking history, browsing behaviour, loyalty status, device, channel, preferences and other legally available data, to estimate an individual's Willingness to Pay?

And perhaps even more importantly, what if we could understand the value that each customer represents for the company, beyond a single transaction?

Because one-to-one pricing would not necessarily be only about knowing what a customer is willing to pay. It could also mean understanding the customer's long-term value to the company.

A frequent guest who books several times a year, participates in the loyalty programme and regularly purchases additional services may have a very different value to the hotel than a one-time customer, even if both have a similar willingness to pay for the room today.

This could potentially allow Revenue Management to move from simply optimising the price of a booking to optimising the value of the customer relationship.

And what if, based on all this information, the system could determine not only the price, but also the conditions, room attributes or additional services most likely to convert?

Would that be the dream of Yield Management, or a nightmare for the consumer?

Perhaps this is where the next evolution of Revenue Management is heading.

We have already moved from relatively static pricing to dynamic pricing, from broad segmentation to increasingly granular segmentation, and from simple rules to sophisticated forecasting and optimisation models.

The next step could be moving from segment-based pricing towards customer-level optimisation.

Technically, this is becoming increasingly possible. But there is still a major difference between personalising an offer and personalising the price.

Showing a customer a room upgrade, breakfast or late check-out based on their preferences is one thing.

Showing two customers different conditions for exactly the same room, on exactly the same dates, at the same moment of booking, because an algorithm estimates that one of them prefers a specific package, a late check-out or another additional service, is something completely different.

Showing them different prices for exactly the same room because the algorithm estimates that one of them is willing to pay more is something completely different again.

And this is where Revenue Management becomes more than price optimisation: it becomes customer value optimisation, while raising new questions about consumer trust and price discrimination.

There is also a major technological challenge. To make this level of personalisation possible, different systems and databases need to communicate with each other: PMS, CRS, CRM, loyalty programmes, booking engines, distribution systems and potentially external data sources.

In other words, the challenge may no longer be whether we have enough data, but whether we can connect it, interpret it and use it responsibly.

The evolution from Yield Management to Revenue Management has already been accompanied by a growing acceptance of dynamic pricing and changing conditions. What once seemed unusual has become completely normal for consumers.

Perhaps, and only perhaps, individually tailored offers, and eventually individually tailored pricing, could follow the same path as consumer acceptance grows.

Time will tell.

* Source / Further reading:

Talón-Ballestero, P., Nieto-García, M., & González-Serrano, L. (2022). The Wheel of Dynamic Pricing: Towards Open Pricing and One-to-One Pricing in Hotel Revenue Management. International Journal of Hospitality Management, 102, 103184.