In this special guest feature, Dave O’Flanagan, CEO and co-founder of Boxever, outlines how airlines are leveraging big data and predictive capabilities to transform how they engage with customers. Dave is the CEO and co-founder of Boxever, a data science and omni-channel personalization platform for travel companies. At Boxever, Dave’s focus is enabling airlines and travel providers to improve the customer experience, generate revenue and enhance loyalty through one-to-one, personalized marketing. Dave has more than 15 years’ experience developing and managing enterprise software in mobile telecoms, travel and cloud. Earlier he was VP of engineering at CUSP, a cloud-based document management startup and was a lead architect at CAPE Technologies.
Artificial intelligence has seeped into the lives of everyone with an internet connection. Binge-watching television shows and online shopping carts full of suggested add-ons are commonplace, even though most consumers aren’t aware of what’s powering the recommendations. Algorithms are constantly hard at work, learning in real-time while users are sitting on the couch watching their favorite streaming shows or as they are adding items to their wish list at a favorite online retailer. Predictive analytics is permeating all industries as organizations start to harness the power of data collection to enable smart decisioning.
The travel industry is no exception, and for the most part, travel brands are missing an opportunity to become an integral part of their customers’ lifestyle like Amazon or Netflix. Even airlines are hiring data scientists who can employ AI techniques, such as machine-learning algorithms to provide real-time, highly-personalized offers for different customers. And it makes a lot of sense, as airlines run into many situations where one customer falls into a different profile for different purchases.
For example, a traveler booking a business trip probably has vastly different preferences when it comes to price and convenience then that same traveler does when scheduling a family vacation. Instead of guessing which situation a traveler is in during a particular purchase, machine-learning algorithms can identify patterns that know which profile that particular person fits at any given time, and provide an accordingly enhanced and personalized experience. Not only will the customer be surprised that they were able to find the right travel package so easily, they’ll be delighted that the brand has removed the typical hassle from the booking experience. Brands that can create these moments place themselves in a better position to earn repeat customers and bring in additional revenue.
Understanding the context
Artificial intelligence enables systems to consider context, to see what is happening when a customer is booking on a website and connect those actions to prior transactional and behavioral data. This paints a more complete picture of the customer, allowing organizations to identify precisely which offers and messages are most likely to resonate with that particular person at that particular time.
For instance, if an airline sent an email offer for a discounted getaway flight to Jamaica, an algorithm could recognize which customers opened the message and which ignored it. From there, it could evaluate which customers took an additional action and started looking at hotels—these would be individuals deemed likely to book. However, if that target group still hasn’t purchased, the machine could automatically modify the algorithm in real-time and send a more personal message, offering a discounted flight and hotel package. Even though this is big data at work, it feels authentically human to the customer and goes a long way toward establishing a connection between the traveler and that brand. The algorithm could take it even a step further depending on what it knows about traveler intentions—for example, if a customer books an annual family vacation to the Caribbean at a certain time of year, he or she might receive special offers promoting children’s activities at a specific resort in Jamaica. That’s the power of machine learning, being able to consider such contextual information and provide personalized recommendations based on a traveler’s needs at a specific time. If customers knew that some brands weren’t taking advantage of this technology, they’d be running for the ones that are leveraging big data to improve their shopping experience.
First-movers and early adopters aside, the travel industry’s failure to embrace this methodology is illustrated by the fact that most travelers don’t want to receive promotional offers. In fact, nearly 70 percent of consumers surveyed in a recent Boxever report said that they’re likely or extremely likely to use ad-blocking apps on their mobile devices and another 15 percent said they’d consider it. The primary reasons: the mobile ads typically received are completely irrelevant and never add any value. Due to a glaring lack of personalization and relevance, consumers are simply ignoring communications, even from the brands they frequent. Rather than embracing big data to build trust and enhance experience, brands are doing the opposite and causing mounting frustration.
Today’s advances in artificial intelligence give airlines and other travel providers a great advantage and the ability to learn things in real-time. Even a dollar change up or down can be monitored, to determine its impact conversion rate. Although the technology is artificial, it gives brands a uniquely human power to make an emotional connection with their customers; the key to becoming a lifestyle brand. The influence of this kind of technology is finally being realized by marketers who are helping brands follow consumers throughout their entire journey, ultimately driving increased customer satisfaction, loyalty and more sales. Before machine learning opened up new possibilities, the only way organizations could account for context was through incomplete data and unreliable assumptions. Artificial intelligence has eliminated that guess work.
Sign up for the free insideBIGDATA newsletter.