ForwardKeys for Hotel is designed to help Revenue Managers and hotel marketers better understand and anticipate market demand and trends, allowing hotels to adjust commercial and pricing strategy ahead of competition to improve business performance.
ForwardKeys collects daily all reservations made the day before across all GDS, and aggregates this information to the main database. By definition, this reservation information is mostly about travels scheduled in the future. That is how ForwardKeys offers tangible data about the future - we know how many people are going to travel to a destination any date in the future, and we can compare this to any date, such as same time previous years, at comparable day.
ForwardKeys arrival pickup menu allows to monitor pickup trends for any day, week or month in the past as well as in the future. You can compare this pickup to any alternative date or filter per travellers' origin.
Since ForwarKeys data includes booking made for future travel dates, revenue managers have access to trends for future arrival dates. But ForwardKeys also includes an algorithms that forecasts future arrivals, that once compared to the number of hotel rooms available at destination, allows to define a future occupancy.
To forecast future arrivals, ForwardKeys algorithm uses a lot of information available in the database, such as number of travellers same day last year and previous year, pickup trends between current day and forecasted period for last and previous year, current pickup trends, and reservations completed. As a result, we can anticipate the number of travellers arriving to destination (within our sample of data).
Once we understand how many travellers are going to arrive at destination at any given date in the future, we can cross reference this information with traveller's respective length of stay (when available). That allows us to calculate how many travellers are staying at destination at any given date. That information can then be compared to the or beds/rooms available at destination, to suggest an average occupancy across the market.
This information allows Revenue Managers to adjust their occupancy forecasts at budget level, as well as adjusting rates and sales rules to reflect the reality of the market.