APEX Insight: Personalization is what converts mobile traffic into sales, according to a recent report from Qubit titled “The Growing Influence of Mobile Discovery on Ecommerce Revenue.” Daniel Bensley, Qubit’s industry lead for Travel, explains how airlines can use machine learning to improve personalization during the mobile booking process.
As part of a Qubit survey, over 4,000 consumers were asked which factors were most likely to encourage them to make a purchase on their phone. “Finding exactly what I want more easily” and “Easier to discover new products that I like” were the second and third most popular responses after “A faster or easier browsing experience.”
Daniel Bensley, Qubit’s industry lead for Travel, attributes this to the fact that platforms like Netflix, Instagram, Spotify and Pinterest are re-wiring consumer expectations by using machine learning to guide visitors to content that is individually curated. He believes this kind of personalization is now essential to a brand’s long-term success.
Much like the aforementioned media platforms, airlines offer a seemingly endless number of choices and products, so they’re well-placed to cater to a broad range of customer needs. However, Qubit’s March 2018 report highlighted that “in 65% of cases, larger catalogs don’t translate into explorable catalogs, meaning that often, the larger the range of products, the harder it is for users to find what they’re looking for.”
Talking specifically about airlines, Bensley says that the booking process must take into account what ‘state’ the customer is in: “Their state is important because the same person booking for business during the day could be booking a family holiday for leisure in the evening.
“Successful brands are able to collect intent and preference-based information from customers as they engage and interact.” – Daniel Bensley, Qubit’s industry lead for Travel
“Successful brands are able to collect intent and preference-based information from customers as they engage and interact. Short interactive surveys and quick questions can provide data for airlines to then quickly act upon and be able to serve a relevant offer, promotion or user journey.”
He goes on to give some examples of personalization where intent data is successfully acted upon, listing, “Recommended flexi-fares for business travelers; promoting trending ancillaries for each booker segment (e.g. family/couple/solo traveler); advising customers when remaining seats available are becoming scarce. The key is to focus on relevance to the customer at all times.”
The same techniques apply for web traffic. In a case study on Thomas Cook Airlines, which uses the Qubit Pro personalization platform, Qubit said it used ‘social proof messaging’ tailored to specific routes. When the carrier started highlighting what other customers on the same route are buying – “it could be that customers booking flights for ski destinations are buying insurance, whereas customers on longer-haul flights buy meals for the plane,” the study clarifies – it saw a 1.6 percent increase in purchases.
For Bensley, the good news is that machine learning will allow personalization to keep improving. “We’re already seeing successful examples of airlines tying historical booking data with onsite behavioral data in order to provide more relevance to the customer,” he says. “We believe that personalization will become more intelligent as time goes on and will be based on more data sources from across the business which add further context or value.”