Pitching one AI solution against next
One of the company’s early customers, LodgIQ plans to revamp today’s hospitality industry with smarter real-time pricing strategies based on big-data analysis. Founded last December, the New-York based startup was quick to convince investors, securing $5 million in funding from investors Highgate Ventures and Trilantic Partner while signing its first customer, Highgate Hotels.
The company has just launched its first products, LodgIQ RM and LodgIQ Mobile RM (for Revenue Management). The LodgIQ RM platform incorporates machine learning and artificial intelligence to adapt to evolving demand patterns in real-time. One application could be to help hotel managers adjust their commercial and pricing strategies continuously, based on multiple factors such as flights patterns, weather forecast, city events (trade-fairs, concerts, sporting events), online reviews, or anything that may affect travellers’ destinations and booking patterns.
Talking to eeNews Europe, LodgIQ ‘s CTO Somnath Banerjee explained his ambition to apply modern scientific mathematical techniques to price hotel rooms.
As a hotel manager, the tool could help you find out dynamically who are your savviest competitors in particular segments, or within a given distance.
“We use historical data, but also large data sets from flight reservations, weather forecast, city-events, or even text-based hotel reviews, and quantify their impact on reservation patterns. We use machine learning to identify patterns in data that we can use for our predictions in order to suggest the best room prices for a given market condition”.
Today, hotel managers typically list out their competitors’ prices manually, but this would no longer be possible with dynamic pricing, and they may not optimise their room prices based on statistically accurate demand.
“There are many things in life that are subjective, for example online hotel reviews are very important, but they are often text-based. We want to take them and run AI across them to mathematically quantify their impact on revenue, putting an objective number on them so hotel managers can find out what their hotel’s real score is”.
The idea is to measure the exact correlation between improved reviews and increased revenues, and how these reviews might be improved (the key actions at staff or service-level). Today, this is very nebulous affair, but machine learning could help hotel managers better understand what impacts their business and how they can optimise their pricing strategies accordingly.
“There was first pen and paper, then electronic spreadsheets and today web-based management, but the future is all about machine learning”, Banerjee says.
But what will happen when all hotel room pricings will be automated, dynamically updated by competing AI solutions scouring not only big data but also closely watching their competitor’s dynamic pricing routines? Wouldn’t that inevitably circle back to equalize room prices at their lowest manually set threshold values?
“This scenario is a utopia where every hotel manager would have the same skills and operate the same hotel”, dismisses Banerjee.
In reality, whilst machines can make recommendations, they will also foster creativity in terms of pricing strategies. Hotel managers will differentiate their pricing schemes based on how they match their offers with the particular data patterns they see which affect their hotel differently. Integrating AI into their revenue-management tools will help them create that differentiating edge.
“For the next 30 years, the future is all Maths and Statistics” concludes the CTO.
Visit LodgIQ at www.LodgIQ.com
Visit Dato at www.dato.com
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