Disrupting the Travel Industry – living in the micro-moments.

Data science and advanced analytics in the travel industry is an essential component for market segmentation and revenue generation. However, a significant opportunity still exists in identifying and leveraging the part of the traveler’s itinerary that service providers have little insight into – the crucial gap where the service of one provider ends and the other begins.

This week at the annual Phocuswright conference in Los Angeles, we’re demonstrating how advancements in data allow service providers to break down their target consumers into small groupings down to individualistic levels through micro-segmentation. This process allows brands to provide consumers with personalized information, recommendations and offers that when applied to “micro-moments” -- periods in time like immediately after landing when consumers turn to their mobile devices to get information, answer a question, or buy a product or service – provides them with a unique opportunity to upsell and cross-sell products and services to a receptive consumer.

In many ways, this evolution is a natural outcome of the digital transformation of the travel industry that’s well underway. According to Phocuswright’s Online Travel Overview Report - the U.S travel market grew 5% to a total of $341 billion last year. Phocuswright predicts that this will continue to reach $381 billion by 2017. Globally, the World Travel and Tourism Council (WTTC), forecasts that the travel industry sector’s total contribution to the world economy will reach $7,860 billion – 10% of global GDP.


This growth is fuelled largely by three factors: ease of access to Big Data, advancements in machine learning, and of course, access to mobile data from travellers at an unprecedented scale. While much progress is being made in service providers efforts to extract insights from traveler data in order to better peddle their wares, these efforts remain largely incomplete because no one singular service provider has access to a customer’s complete travel itinerary. Beyond the provision of their own services, businesses within the travel industry have little to no insight into the decision making processes of travelers in those “in-between moments”, those points at which a customer’s needs shift to be met from one provider to another. There are numerous scenarios which can occur during these transition moments – flight delays, transfers between airports to hotels failing to turn up due to traffic, or unexpected cancellation of tours and trips. Even moments when travellers themselves decide to change plans. The solution to all these problems is conceptually simple. By micro-segmenting travellers and gathering data in real-time, insights into the decision making process can be captured during micro-moments, creating opportunities for brands to promote their services.

The Application of Data Science

In recent years, data science has been key to enhancing customer experience thereby ensuring the success of the travel industry. For airlines to OTAs (online travel agencies), car rental businesses and hotels, making sense of Big Data in all its aspects i.e. through the aggregation of large volumes of historical data, from disparate data sources over a variety of time scales is essential. For the most part, this has become easier, since providers are spoiled for possibilities as travelers ‘relinquish’ vast amounts of data indicating their preferences and behaviour, both voluntarily, and indirectly through the use of third party services. Data tends to come from five main sources:

  • Social Media: Facebook, Instagram, Twitter
  • Review Sites: TripAdviser, Yelp
  • GPS and Map services: Google Maps, Apple Maps
  • Travel Apps which include itinerary management and purchase history from OTAs to Airlines: Expedia, British Airways


However, aggregation of data alone does not equate to insight. It is the utilization of advanced and predictive analytics that gives service providers opportunities to upsell and cross-sell e.g. upgrading seating and discounted offers from hotels which partner with the airline: improve operational efficiency, for example by determine booking trends with regards to seasonality to ensure better yield management: provide safer travel by maintaining the “health” of an airline; through the application of predictive maintenance, ensuring aircraft parts can be replaced before failure: and possibly one of the most important application of predictive analytics, give service providers the power to react better to ad hoc and impromptu disruptions; such as that which occurred to Delta Airlines, when they suffered a catastrophic systems failure in the summer of 2016. As a traveler myself on my way to Seattle that day, I watched events unfold from the front line. The stress placed on travelers was unquantifiable. The financial backlash to the airline, however, ran into the millions and is still being calculated.

Micro-Segmentation of the Market

What would it be worth to service providers to know that Ms Executive of “international conglomerate” is a frequent flyer, with multiple stressful business trips ahead. She will pay for the best services and hotels if they were offered to her at the time. What if “parents of three” needed to ensure that the kids are taken care i.e. everything is safe but fun, but needed a way of getting some alone time in? What could providers offer them? How about “Just trying to get my independence back” is in his or her gap year/sabbatical, or taking a long needed break and wants to get away to experience as many exciting things as possible? How does translate to opportunities for tour operators?

Traditional market segmentation splits groups of individuals into existing and potential customers, making generalized assumptions based psychographic/lifestyle, behavioural, geographical and demographic groupings. Micro-segmentation breaks these groupings down to very small segments – nearly down to the individual level. This process has only been possible in recent years through the application of data science techniques utilising machine learning algorithms.

The Opportunity: Micro-moments

A micro-moment occurs when the user of a mobile device instinctively turns to said device, usually their smart phone to carry out the action of learning, buying, doing, discovering or watching something. Within these moments, user expectations are high as we have been conditioned through our interactions with online providers at a high bit rate. We expect companies and brands to give as exactly what we need, when we need it. Our decisions can be heavily influenced, at these points, which are dependent on the recommendations, suggestions and ease at which we are able to conclude our objectives. They can be anything from watching a how-to video, finding a holiday destination, or buying tickets for that nearly sold-out concert.

The gaps in a traveler’s itinerary – the points at which service providers have little to no information on their customers are rich in micro-moments. Service providers who tap into these moments have an incredible opportunity to influence the decisions of consumers, by offering the right information and offers, not only at the right time and location, but when the desire to act is high.


While industry service providers are well on their way in leveraging data they’ve acquired through direct interaction with their customer base, they still need to acquire new data they currently don’t have in order to create a 360-degree consumer profile that allows them to take advantage of microsegmentation and micromoments. Since micro-moment data is the cornerstone to delivering essential timely products and services, how does an organization go about doing this?

This week at Phocuswright, we’re demonstrating how micro-segmentation can be applied to provide offers, services and support in the form of recommendations via a mobile app at key “micro-moments” throughout the traveler’s journey. Consider for a moment, what’s the first thing you would do when you find out you’ve missed your flight and need to make alternative arrangements? What happens when you find yourself in unfamiliar surroundings and you realise you’re hungry? What happens when you need help or guidance? We all do it now. We pick up our mobile phone and expect a solution to whatever ails us. We also want solutions relevant to us and we want them exactly when we need them. Our demo shows how data aggregated from multiple channels including social media as well as the other sources I highlighted earlier can be analysed in concert with traveler itinerary data to provide unique, timely, ultra-personalised offers when they are needed most. For service providers, this highly contextualised information is a priceless opportunity to not only up-sell and cross-sell at that given moment in time, but to understand their entire customer base in greater detail. Understanding the motivation behind a customer’s decision rather than simply the outcome of it, is the ultimate insight which can lead to predictions of future behaviour. It is also the means by which brands can establish meaningful relationship with their consumers, and secure future revenue streams by acquiring the holiest of grails in customer relations - loyalty.

The Future

In the highly competitive travel and tourism industry, advancements in data science and machine learning are poised to drive the next wave of growth improving the industry’s ability to serve and monetize individual travelers.

In the next five years, the processing power required to carry out in-depth, advanced analytics on ever increasing amounts of data will need to harness the capabilities of the next generation of computer chips, storage and memory. In fact, the work done by Intel on their new class of non-volatile memory utilising 3D Xpoint technology, is likely to prove ground breaking… and the only hints at the tip of the iceberg. And of course, Deep Neural Networks will be harnessed to manage the increased volume and complexity of queries in real-time. All this to deal with the loop; individuals rather than segments, will be provided with even more nuanced offerings, delivered by a larger range of service providers, who will have been fuelled by the same travelers exploiting the ever increasing range of options.
Maya Dillon
A Data Scientist with a passion for AI and Space. She is focused on developing strategic initiatives of businesses – implementing both new technologies and scientific methodologies. She is an experienced and highly acclaimed public speaker not to mention a science and technology evangelist. She is passionate about creating and developing new initiatives incorporating cutting-edge technologies in AI, Machine Learning and Data Science.