Relaunching officially last week, and with $45 million in the bank to take the strategy forward, English and his team are promising something “quite different” for business travellers.
So why the shift in strategy since launching in 2015 with the aim of building an app for consumers that will act as a travel concierge.
Quite simply, because the data showed that two-thirds of the users Lola has attracted over the past year were business travellers.
Although by his own admission this “caught him off guard,” English and his 50-strong Boston-based team of travel agents and data scientists have decided to run with it because “nobody is doing business travel well”.
Giving an example of what this could mean for a business traveller, English, a keynote speaker at EyeforTravel North America 2017 next week, says:
“Say we have seen you at the Gansevoort hotel in New York and now you’re going to Miami, where we’ve never seen you before. Then we can do one of two things”.
Make truly personalised recommendations: When first using Lola travellers are asked to name a few of their top hotels. Then the AI hones in on those hotels to extract relevant information – historic price range, affiliated brands, its size and so on.
Then there are the “millions of reviews across dozens of sites” that which Lola has scraped to reveal the most-used adjectives. In the Gansevoort’s case those tend to be along the lines of ‘hip, trendy, clubby scene, near great restaurants, architecturally cool’.
Voila! Using Lola’s ‘similarities algorithm, now in Miami the Delano – an independent, not-too-large hotel with a cool vibe – seems a good bet.
Identify user clusters: Aside from the looking for highly personalised similarities, Lola also considers so-called user clusters to find users “who like the same hotels as you in New York, but who have also been to Miami”
With flights, it’s a similar story. As English explains, the app is just not going to offer a business traveller an unnecessary, unwanted layover. “Business travellers are driven by efficiency. They would never do a layover unless there is a huge money saving,” he says.
What Lola’s artificial intelligence aims to do, is map highly personalised and relevant options, rather than simply driving, first and foremost, a commission-earning agenda – something the OTAs are guilty of.
Loyalty and service
Aside from a sharp focus on personalisation, the team has built a Sabre integration so that users also benefit from direct booking benefits such as receiving 100% of their loyalty points.
Service is also at the heart of the chat app, which is staffed by 15 human agents 24/7, who are hired first and foremost for their “phenomenal customer service skills and second for travel expertise”.
At the same time Lola’s co-located tech team, which includes people cherry-picked from Kayak, as well as from both big companies like Facebook, Amazon and Microsoft and smaller start ups, is committed to perfecting Lola’s artificial intelligence.
Although most users prefer the messaging function, Lola also does email and live voice. Thanks to the hiring of Bryan Healey, a former software development manager at Amazon, work in progress is a demo for Alexa.
Today, the chatbot is not yet at the point of answering customer queries directly; that will only happen when the Lola team is confident in its ability to respond as accurately and faster than a human. In other words, saving the user valuable time.
But while English may be a computer scientist, he believes “absolutely in human power”. A bot is after all just a bot and Lola, he says, will never trick you into believing otherwise.
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