Pace, a London startup that has developed tech that uses machine learning to let hotels set room prices dynamically based on demand, has raised £2.5 million in seed funding. The financing round is led by InterGlobe, with participation from Seedcamp, Speedinvest and Amadeus Capital Partners. The company says it will use the new capital to invest in its tech and marketing teams.
Founded in 2016 by Jens Munch, John-Paul Clarke and Jason Pinto, Pace is developing dynamic pricing technology that is first being applied to hotels to help them tackle under-occupancy (i.e. empty, unsold hotel rooms) and to maximize profits when demand exceeds supply. The startup reckons that the current imbalance in supply and demand costs the hotel industry up to $100 billion every year because inventory is wasted and hotels fail to get the best price for each room.
To achieve this, Pace’s tech plugs into a hotel’s existing property management system and puts its machine learning to work, which starts by trawling through and crunching historical sales and inventory data. This, the company says, typically takes 24 hours to complete before automated pricing can begin.
As it stands, small and boutique hotels — the segment Pace is initially targeting — still mostly does hotel room price calculations manually and without having the full picture or being able to react quickly enough to affect occupancy rates and the bottom line. In contrast, the Pace dynamic pricing software means that hotels can respond immediately to changes in consumer demand and “price elasticity.”
“Hotels regularly get caught with half of their rooms empty or having sold out at too cheap a price,” Pace co-founder and CEO Jens Munch tells me. “Given that hotels lose 20-30 percent of their revenues to suboptimal pricing, it’s alarming that fewer than 10 percent of them use technology to price. We give our customers a simple interface that turns hotel pricing into something like a self-driving car, automatically navigating supply and demand.”
In other words, Pace might be thought of as something along the lines of “Uber surge pricing as a platform.” In this instance, surge pricing for hotel rooms. That makes sense, given the founders’ background in optimizing airlines and financial systems through machine learning.
Elaborating on how hotel managers interface with the platform, Munch says they log in to the Pace dashboard and are shown a clear way of actioning hundreds of price changes in one go. “We also show them analytics where they can for the first time see forecasts of where they will be in the future. And how that forecast will be different if they change their prices,” he says. Once actioned, these price updates are pushed to the places that a hotel sells its inventory, such as Booking.com, Expedia and their own front desk.