Pace; a London based machine learning startup that allows hoteliers to create a pricing ecosystem which moves in line with supply and demand has received £2.5 million in funding and substantial interest from global players.
The product has the potential to move into other industries also, with restaurants, movie theatres and other reservation-based businesses being obvious candidates.
The primary issue facing hotels is under-occupancy, or unsold hotel rooms which end up costing the industry around $100 billion every year through unused inventory. Pace connects with existing hotel systems and combines a number of inputs, including the sales history, current inventory and popular bookings. Within 24 hours, the system creates automated pricing that will adapt and change as time goes on through something the company calls, “price-elasticity.”
The functionality itself allows for adaptability. Hotel managers can log in directly and make decisions as to whether to reduce the price of certain hotel rooms or not. This is especially useful if an unscheduled event, such as a wedding, will mean a captive audience who won’t demand discounted rates to book. The system also interacts directly with and the existing property management system so once approved, discounts can be made instantly available online and at the front desk.
Importantly, this allows hotels to maintain a level of reactivity while increasing revenue and creating some predictability in occupancy. Over a period of time, the system adapts and predicts gluts and booms – as a result, hotel managers can prepare themselves, their staff and their inventory for these periods. The increased effectiveness should be a powerful drawcard for any hotels considering using Pace, and the extended functionality will no doubt excite both the operational management teams and financial executives alike.
Hotel have suffered for a long time, especially after the likes of Airbnb broadened accommodation options, and forced generalised discounts across all inventory. The Pace product should go a long way to decreasing the guesswork in an industry where you can’t afford to get it wrong too many times.