Higher prices, the same service, and no drop in the number of stays – which hotelier would not sign up? Surprisingly, this is within reach for many hotels. Research by SmartHOTEL and Amsterdam Data Collective shows that most hotels in the Netherlands do not charge the prices that guests are willing to pay.* Mostly, this is because prices are not dynamic or customised enough. Data science can play a pivotal role in finding out what constitutes a better price and how to implement it successfully.

Key Findings:

  • 80% of hotels can benefit from a more dynamic pricing strategy.
  • For some hotels this can generate up to 45% more revenue.
  • Hotels can strategically benefit from seasonal patterns over weeks, months and seasons, but also the seasonality in booking behaviour.
  • Hotels can benefit from more customized rates, harvesting the benefits of price differentiation.
  • Having a wide variety of booking options leads to more revenue.

Dynamic Pricing

Many hotels price less dynamically than an average hotel in the market. Dynamic pricing is a pricing strategy in which the price adequately responds to all kinds of external relevant factors. Hotels with fluctuating room rates over time may be classified as dynamic pricers, while hotels with flat prices may be seen as static pricers. To illustrate, weekend days have on average a 35% higher rate than week days, indicating that hotels with a difference of less than 35% between weekend and week days, price less dynamically than the market. These hotels may increase this difference while generating more revenue.

Not only are there such seasonal patterns across the week, but also over months, years and booking behaviour. Especially the latter is interesting, as we found that there is considerable variation in the number of days guests book their stay in advance over the season. The number of days that a stay is booked in advance increases from January to August steadily (except for April) to decrease steadily again from September to December. Hotels that take advantage of these patterns and ensure that they have the right price-inventory mix for a stay date when they know that most guests would book for that stay date, tend to have higher revenue.
Additionally, we find that hotels may benefit from customising rates further to hotel characteristics (such as location, number of rooms and average room rate). Though some of these characteristics are difficult or nearly impossible to adjust (e.g. location or number of rooms) others can be well directed to boost revenue.

For example, we find that the number of connected online travel agencies (OTAs) has a positive effect on the revenue. Likely, hotels benefit from the broader customer reach. Another example is variation in room rates, which has a positive effect on revenue. This relates to the concept of price differentiation. Hotels that account room rates for differences in size, view, interior or extras, tend to perform better as their rate matches more adequately with what guests would like to pay for such as room.


The findings of this research are generalised and may differ per hotel. However, it is clear that there is significant untapped potential. The key to identifying this potential for your hotel is to experiment (to test the potential, e.g. A/B testing) and analyzedata (with a rich dataset including external data).

Data science can help identify this potential and quantify what would be a more suitable pricing strategy at the individual hotel level. This can be achieved internally, but this requires extensive statistical knowledge to correctly interpret and analyze results. On the other hand, one can outsource data science to a company such as SmartHOTEL, which can provide recommendations based on the best practices from the market.

*Please note that our findings may not apply generally to the entire hotel market. Our results were based on pre-Covid data. The indicated potential may therefore be difficult to realize due to the current uncertain economic situation.

Curious to know how to get started?

Get in touch with Rik van der Woerdt at rik@amsterdamdatacollective.com, or check our contactpage.