Why "Business and Leisure" Is No Longer Enough, and How Data Is Changing the Rules of the Game

The traditional division of hotel guests into “corporate” and “leisure” is hopelessly outdated in the age of Big Data. A new study shows that if you truly want to optimize pricing and increase revenue, you need to dive deeper into transactional data. An analysis of the behavior of more than 9,000 guests reveals that the key to success is not intuition, but sophisticated segmentation based on purchasing habits.

The End of the “Common Sense” Era

A large proportion of hotels still rely on so-called “common-sense” segmentation. They divide guests into business travelers and leisure travelers, applying dynamic pricing primarily to the latter group. Moreover, they usually do so only in the online environment. However, this approach is overly simplistic and ignores the vast amount of data available to hoteliers. A study published in the Journal of Vacation Marketing points out that modern revenue management requires a more comprehensive view that takes into account not only the purpose of the trip but also the timing of the purchase, price sensitivity, and other behavioral factors. Even the most affordable PMS systems enable effective data analysis, where imagination knows no bounds and allows for maximum personalization of distribution strategies.

What did the data from Prague reveal?

Chalupa and Petříček (2024) presented a case study on the application of market segmentation and econometrics focused on customer behavior in Prague. The “data-first” approach made it possible to create various customer segments and subsequently describe their behavior based on hard data, not on impressions. In fact, we often come across research and studies that describe the behavior of families with children, seniors, and traveling couples based on questionnaire surveys. But few people ask how valid these findings are. Are the respondents’ answers reliable? Is the sample size representative? Can we apply this knowledge here as well? Do our customers behave differently? Unfortunately, this is usually the case.

So what data do we have available from simple records (transactions) in the PMS?

• Demographic and geographic data about the guest (age, gender, country of origin, address).
• Length of stay.
• Date of reservation and date of arrival.
• Lead time (the time between booking and arrival).
• Accepted price of the stay.
• Number of guests (adults, children).
• Room type and number.
• Average daily room rate.
• Revenue from food and beverage services.
• Revenue from ancillary services.
• Accepted rate type.
• Price discounts (MLOS, Early Bird, Non-refundable).
• Payment terms.
• … (and many more—each field in the PMS may represent a characteristic of customer behavior).

The result was not just two, but six specific customer segments that behave completely differently. The identified groups include, for example, Corporates (business clients), Early Bird Bookers (early bookers), Product Seekers (quality seekers), and Last Minute Bookers.

Surprising Findings About Price

The most important thing the data revealed is the different reactions of these groups to price changes—known as the price elasticity of demand. This is where intuition often fails:

1. A last-minute paradox: The Last-Minute Bookers segment (with an average lead time of 9 days) showed a positive price elasticity coefficient. This is a rare phenomenon in economics (possibly a Giffen paradox), where demand does not fall as prices rise but may even increase, as these customers have no other choice or perceive the higher price as a sign of quality at the last minute.
2. Quality Seekers are price-sensitive: Conversely, the Product Seekers segment, which pays the highest average price per room (approx. 3,573 CZK), has the highest price elasticity (-3.413). This means that even though they spend a lot, they are very sensitive to any price change and demand commensurate value for their money.
3. Who Doesn’t Care About Price: Groups such as Early Bird Bookers, Long-Term Stayers, and corporate clients have proven to be price-inelastic. For these groups, therefore, a price reduction is unlikely to lead to a significant increase in demand, and the hotel would be unnecessarily sacrificing its margin.

Why Data Matters

These findings have a fundamental impact on strategy. If a hotel knows that a certain segment is price-inelastic, it can afford to keep prices higher. Conversely, for elastic segments, a properly set discount can lead to a significant increase in sales volume.

The conclusion is clear: Data-driven segmentation allows hoteliers to move beyond “armchair” categorizations and start targeting actual customer behavior. At a time when most reservations are shifting to the online environment, the ability to analyze this data and respond to it dynamically is what separates average hotels from the most successful ones.