This is a guest post by Patrick Bosworth, CEO of Duetto, a San Francisco-based revenue strategy technology company.
Patrick Bosworth
The hospitality industry has long used typical information sources to create as accurate a demand forecast as possible. However, with more sophisticated technology and software now available hotels would do well to consider that the data points that they have been using may not be telling the whole story.
For years revenue managers and hoteliers have typically forecast demand by looking at historical booking information, and then the pickup or pace of online reservations leading up to an actual stay date. By charting past and present, hoteliers have been able to get an idea of how much future demand there will be for any given date. This is important to not only establish room rates to maximize revenue, but also for operations teams to set staffing levels.
However, while those sources of data are critical they are also limiting. By using only historical and current bookings, hoteliers may get a decent view of future demand for the next month or maybe two, but when very few reservations are on the books, it becomes harder to predict much further out.
What then is missing? For one, unconstrained demand should be studied more closely. Unconstrained demand is often defined as the forecast or number of rooms a hotel could sell if it had an unlimited supply of rooms. And to measure that, most hotels will look only at the world of people actually purchasing rooms at their property.
However, I would suggest that true unconstrained demand should include not just those buying but everyone potentially shopping. This is more measurable and predictable today with the wealth of new consumer-centric data available.
The key to unconstrained demand: web shopping data
Hotels should look at web shopping ‘regrets’ and ‘denials’, not just consumers booking. (Image credit: saquizeta/iStock)
To improve algorithms and forecasts, hotels should look at web-shopping data. Two of the most useful information sets can be found on hotels’ online booking engine to give any property a better view of its lost potential business and unconstrained demand.
What hotels should look at should not be limited to only consumers booking on a hotel’s website. Consider also web shopping “regrets” when potential customers abandon the hotel’s website without booking, and “denials” when those shoppers try to book a room but are turned away because of availability issues.
If hotels are only tracking actual bookings two similar dates may have had three reservations made on the hotel’s website, but the property would not know any of the nuances in demand for those dates. By looking at the universe of those shopping, it becomes obvious how completely different the dates might be. What if one hotel had three conversions out of 100 people shopping, while the other had three out of 10? The revenue manager at the first hotel could argue that the room rate is too high, and that dropping it slightly might improve the property’s conversion rate to that of the second hotel.
Observing actual customer shopping behaviour and comparing “looked” versus “booked” data is the best way to measure true unconstrained demand and price elasticity.
Better data, better pricing
Events are an obvious source of potential demand. (Image credit: OSORIOartist/iStock)
Information about local events, flight arrivals, social reviews and even the weather – all of those data sets together will also give a more accurate picture of unconstrained demand.
Events are an obvious source of potential demand. Any good revenue manager would consider how a new festival that is expected to attract 100,000 visitors would represent a whole new world of potential buyers, and therefore forecast that demand and pricing should increase in tandem, even before noticeable pick up is seen in the systems.
Or consider what useful data points weather reports can provide. In a beach town, good weather could signal an increase in demand in hotel rooms, especially if nearby towns are seeing bad or unfavorable weather forecasts.
Social reviews and ratings can also be an important factor in consumers’ booking behaviour. If one property receives consistently strong reviews in comparison to a struggling neighbour, the better-rated property will get more business if all other factors are equal.
When hotels start to look at unconstrained demand and web-shopping behaviours as well, they are introducing forward-looking data into their strategy, immediately improving forecasts and influencing pricing algorithms, all the while providing key information that drives better operational decisions and. ultimately, more revenue.
• Featured image credit: seewhatmitchsee/iStock
