Qalendra’s algorithms forecast a number of factors — including snow quality and traffic conditions — so users can always book a good trip.
The databases travel agencies provide for users are often based on reviews and pricing, and we’ve seen apps provide other relevant factors such as weather filters. With the belief that reviews and deals don’t guarantee a good holiday, Qalendra is offering a big data approach to travel booking.
Qalendra have developed predictive algorithms that factor in a range of data for a specific time and location. Say a user wants a ski trip sometime in January. Qalendra will rank a list of destinations for their chosen country based on amenity standards, snow quality at that time (which takes from detailed analyses of air density, temperature and altitude) and what level of skier the slopes are good for throughout that period — it will also predict traffic conditions so users can toss up travel options. Qalendra are offering their software as an API for existing travel sites, and believes that their contextual recommendations for anything from golfing to kayaking will guarantee good trips for users, and result in higher levels of customer satisfaction and returns.
Qalendra’s platform suggests that consumers are expecting more sophisticated search functions and more detailed results when they look for travel recommendations. Could similar contextual forecasting algorithms be developed for specific events, such as wedding planning?