First Published on 13th March, 2018
Ai Editorial: Be it for having a bigger say in the inspiration phase or coming up with relevant recommendations on a mobile device in real-time or improving the conversion rate, machine learning is playing a bigger role than ever, writes Ai’s Ritesh Gupta
Airlines are finding ways to have a bigger say in the booking funnel, and one critical way to bolster the same is via machine learning, a technology where computers identify patterns in data.
What it essentially means is airlines are taking a comprehensive look at all user activity on their digital assets and then acting on the resulting data to eradicate hurdles in the shopping journey. For instance, how to single out a real shopper who is about to complete a transaction from a fraudster who is trying to trick the system and commit a fraudulent activity? Another area is how to come up with a recommendation about a trip that in all probability would garner the attention of the traveller and get them close to completing a booking on airline.com. So be it for early part of the booking funnel to closing stages of a transaction, machine learning is playing its part in a deeper way than ever.
Here we look at couple of areas that can result in better control over the passenger experience:
Inspiration phase: It is being highlighted that inspiration leads to conversion. As LikeWhere states, airlines facilitating travellers in the inspiration and planning phase will be best positioned at the booking phase. So rather than offering loads of content, build on a layer of intelligence and display destination images, videos etc. as per the trip motive, lifestyle preferences etc.
Of course, for this airlines need to focus on 1st party data. Carriers, too, realize that they can capitalize on the richness and size of data sets quite unique to their own organization. The ideal situation would be to generate enough data within your own user ecosystem to truly understand where and why people are planning to travel. “Once you have a user-specific data, you can understand the purchase journey and also what to recommend. Once you work on a profile of a user, you can understand travel habits and accordingly recommend something relevant, contextual,” points out Gillian Morris, CEO, Hitlist. When it comes to recommending, a way to build affiliation is by focusing on personalizing destination discovery. Here machine learning contributes by letting airlines to match locations with the lifestyle preferences of their customers. The key here is to deliver a nuanced recommendation, to “humanise” the available data. As for what to recommend or what to consider before offering something to the traveller, Morris says, “People aren’t going to a destination, they’re going on a trip. In addition to destination and price, equally important are timing (say weekend vs. weekdays) and social context (family, individual, colleagues etc.).”
If airlines don’t act fast (on their own or by integrating their interface with a machine learning specialist), then they are bound to lose. Why? Because Google, Facebook etc. are in an advantageous position, just like Alibaba and Tencent in China. And then online travel groups like Ctrip.com are getting sophisticated with every passing day. For instance, the team at Trip.com, the Palo Alto, California-based company acquired by Ctrip late last year, is counting on their predictive artificial intelligence (AI) to understand various traits of a traveller - personality, interests, style and budget. So what attracted Ctrip in Trip.com? Travis Katz, Trip.com’s co-founder and CEO, referred to – predictive AI technology behind recommendations for travel, based around a bunch of contextual signals, and an engaged community, which has contributed content that complements the core technology.
(Read how JetBlue is capitalizing on artificial intelligence for trip planning (via partnership with Utrip, a destination discovery and planning platform that helps in crafting a personalized, hour-by-hour vacation itinerary) and lot more).
Monetization: Companies like LikeWhere assert that by engaging right from the inspiration phase, airlines can go for a fruitful association in the form of monetizing clicks. “Once we establish certain parameters with a customer we use machine learning to add value, through informing more contextual recommendations. Our product (recommendation engine) enables airlines to begin their customer lifecycle earlier in the inspiration phase which positions them for the booking/ancillaries – that’s where the monetization is,” says Matt Walker, Chief Storyteller at LikeWhere.
By preparing to serve content in an earnest manner, airlines can also benefit to have deeper association that goes beyond air and air-ancillaries. For instance, if an airline knows a traveller is in the middle of a trip (better if the passenger booked the flight itinerary with them), then they can use contextual signals provided by a mobile device to come up with recommendations. So for example, at 8AM the app knows you are most likely looking for breakfast or coffee, and can show you things nearby versus 9PM where it understands you are either looking to go out or plan your next adventure, and adapts the content accordingly. Similarly, if it’s raining where you are, the app understands this, and recommends things to do indoors. These are all signals that are taken into the account. And the ideas are offered in real-time.
Improving the conversion rate and managing fraud: If airlines adopt a risk-averse approach to managing card-not-present fraud, then sales can suffer tremendously. Limitations of the traditional rule-based fraud offerings and reliance on manual reviews are coming to the fore. With machine learning, the system understands when to skip rules when positive behaviour is detected. Moving towards machine learning allows airlines to remove all these unnecessary rules that would have otherwise blocked genuine customers. The combination of big data and machine learning allows more effective fraud prevention.
With data, including a set that is garnered from airlines, specialists focus on signals that aren’t just related to transactions, but also related to buying pattern, post booking behavior etc. Specialists churn the data through various permutations and combinations to identify potential fraud patterns that may be left behind by fraudsters, who have made micro-changes between transactions in one coordinated fraud attack to trick the system. Using real time pattern recognition, even micro-changes can be proactively identified and tagged to the same fraud pattern group. The data that Sift Science leverages includes attributes associated with the identity of a user, behavorial (browsing patterns, keyboard preferences etc.), location data, device and network data, transactional data, decisions (business actions taken), 3rd party data (geo data, currency rates, social data etc.) plus custom data that is specific to a particular merchant. So the purpose of maximizing legitimate transactions as well as avoiding fraudulent transactions is being served by machine learning.
Hear from experts about machine learning and e-commerce at the upcoming Ancillary Merchandising Conference, to be held in Edinburgh, Scotland this year (9-11 April, 2018).
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