28Oct

Differences in Measuring Transient Demand and Group Demand in Hotels

Author: Erik Browning

In Part 1 of this three-part series on the Changing Landscape of Group Demand Measurement, I discussed the health of the overall US hotel market specifically focusing on the group segment. The looming problem is that group ADR growth is far below what it should be - a challenge that must ultimately be solved by the revenue and sales team responsible for an individual hotel. It's obvious, but many times forgotten, that overall statistics are made up of each individual property and the decisions they make. Now, let's take a look at some possible reasons for lackluster group ADR lift and what hotels can do about it.

Hotel sign

 

Setbacks and Shortages in Group Pricing

Hotels supported by a transient revenue management system (RMS) make most of their transient pricing decisions based on data. A good RMS will project demand, determine how sensitive buyers will be to guest room prices and then provide an optimal rate. The same cannot be said for the group pricing process. Group pricing in most hotels is a very manual process and highly influenced by emotions and anecdotal information gathered by the hotel sales and revenue management teams. In most hotels, group pricing is not supported by proper analytics and software. Hospitality revenue management started out as a process whereby we attempted to remove the gut feel out of forecasting and pricing transient business. Unfortunately, there hasn't been the same effort and focus placed on group pricing.

In this manual pricing environment, it can be a complicated process to determine how to price a group, but, boiling it down, whenever revenue management thinks about a price, it first looks at how much expected demand there is and then determines the value of that demand. As demand goes up and exceeds supply, the price rises and lower value business is turned away. To derive the most appropriate group price, we would still estimate the demand and also incorporate factors such as group history, F&B contribution, reason for the meeting, lead source, etc. For this to work however, we have to accurately measure group demand, because an incorrect assessment of demand will almost certainly lead to an incorrect price. Historically, group demand has been somewhat less complicated to measure than transient demand, but there are some important changes to group distribution which likely impact estimations of group demand.

Group Demand Measurement Challenges Through the Lens of Transient Demand

 \It was relatively straightforward to measure unconstrained transient demand 10-15 years ago. By adding the number of rooms sold to the number of denials and regrets tracked at the hotel, you could get a reasonable approximation of the amount of demand we might expect if we had an infinite supply of rooms. Denials and regrets in most cases were only captured by voice agents and even then, it wasn't a complete picture. However, it was statistically significant and in most hotels, a multiplying factor enabled the property to estimate the unconstrained demand with a fair degree of certainty. Due to hotel rate shopping moving from voice channels to electronic channels, we had to adapt and create methodologies for measuring transient demand such as synthetic unconstraining of demand.

What hasn't been discussed are the online group tools for meeting planners and their impact on the quantification of group demand. Historically, as long as the sales team at a hotel input the majority of their leads into their sales system, group demand could be estimated. By running a report including definite, tentative, prospect, lost, and canceled business, you can add those up to estimate the amount of unconstrained group demand for a given day or set of days. The big question is whether or not this alone is still the best way or if there are other factors that need to be considered.

In the final post of this three-part series, we look at the impact of third-party meeting planners on traditional group demand measurement and how you might be overestimating demand and ultimately pricing groups incorrectly.

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