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“Dirty Data”- The one data source that might be harming your revenue performance



Historical demand has long been a critical component of dynamic pricing and Revenue Management Systems (RMS). An even more critical requirement for RMS is the unconstrained demand, which is the true demand for a particular product in the absence of any limitations, such as when a room or seat is unavailable to purchase. The success of unconstraining affects the entire pricing and revenue management process.

Regrets and denials data are not directly applicable to calculating an unconstrained demand since there is an important distinction between "denials" that are due to unavailability and "regrets" that are due to price or other factors. Many reservation or booking systems are unable to automatically capture the difference between regrets and denials.

The problem with this methodology is that not only brand.com comprises only 27% of the reservations for transient nights, as TravelClick reports, but also unqualified transient demand data is being widely used without sufficient regard for unconstraining. Unconstraining methods must include demand for each and all of wholesale, group, corporate negotiated, and unqualified transient demands. This is what we call 'holistic unconstraining.

Studies confirm that many travel buyers use a variety of websites to research and compare prices before making booking decisions. So, even if one is confident about the methodology for assessing denials on one site, it is not at all clear that there is not cross-usage of additional websites or multiple visits per buyer that are unknown in the denial logic. That is primarily why leading data scientists refer to regrets and denials as "dirty data."

Each of the scenarios above can result in the RMS over-unconstraining the demand data, which leads to over-protection of inventory and, eventually, reduced occupancy.