There’s more data available about your guests than ever before. Today, we’re learning about travelers who visited our websites, began searching dates and locations, but left without booking.
NB: This is an article from Duetto
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This provides a clearer picture of unconstrained demand, a more comprehensive look that goes beyond just guests booking a room, exploring potential shoppers as well.
Instead of looking only at what’s happened in the past, novel data sets are available that can more accurately measure and predict potential buyers.
7 data sets you need to build accurate forecasts
1. Historical data and booking pace
Your hotel’s historical data builds the framework for a solid forecast and can be easily ported into a revenue management system (RMS) from your property management system (PMS). With this, you can look at occupancy, rate, and revenue figures from prior years. The further back your data, the more accurate your forecast will be.
You can begin to recreate past booking curves to get an idea how far in advance rooms for the same date were booked in past years. If the normal booking window for a specific date is two months out, and this year you’re a month out with less than half your business on the books, you can make basic assumptions and tweak your strategy to adjust for the lull in demand. Once you start to understand past booking patterns, you can segment and price more accurately.
From here, you can incorporate an events calendar by importing a list of historical and future events that took place or will take place near your hotel. With these initial data sets, your forecast is on the right track.
2. Competitor pricing data
Another common practice in forecasting is monitoring your competitors’ rates. An RMS, like Duetto, can crawl your competitors’ websites bringing information back to you automatically — how nearby hotels are priced for an upcoming date can help you understand overall market demand.
Although looking at competitor rates is a factor in your forecasting and pricing strategy, we’d recommend placing more weight on your value proposition and other demand indicators.
With historical data, you can begin to recreate past booking curves to get an idea how far in advance rooms for the same date were booked in past years.
3. Events and macroeconomic factors
Historical booking information allows you to establish a baseline for important dates like holidays and annual events that affect demand.
Advanced RMSs can help track annual events and new ones that crop up that may have an effect on your demand. They can scan quickly for anomalies in demand patterns and alert you to sudden changes.
