Jaya Singh of Mondelez World Travel Retail asked “Per-passenger spend on airport retail is falling since 2012; how can we turn this around?” Mr. Singh is also President of the Asia-Pacific Travel Retail Association. He asked this question during the April 2013 ACI-Europe Airport Retail Conference held in Helsinki. There are many reasons for the decline – among them [online competition, ‘showrooming’ and traveler frustration]. Of course, we’re all aware of the traveler’s frustration. We forget that some frustration comes from the fact that travel is NOT seamless. It comes with transitions between transportation modes. We should be focusing instead on creating frictionless NOT seamless travel as a solution.
We’ve been tracking customer retail spend at airports since 2004 and our data agrees with Mr. Singh’s analysis. At the core of the problem is that travel is not ‘seamless’. It is far from it, as each step in our travel is a seam. By way of example, on a recent trip to Europe, there were 14 different types of documents, each representing a data silo – data held by one travel service provider. That’s 2 airline tickets, 12 other bookings – 6 hotel or rooms, 3 train, 1 car hire, 1 restaurant booking and 1 special event. Everyone a separate document. Certainly not seamless.These data generated and used by travel service providers does not to help during the traveler through their entire journey.
In response to this problem, we developed a customer knowledge platform. Its purpose – to deliver a frictionless journey, knowing it’s not seamless. The customer knowledge platform contains all relevant traveler data that is normally contained in some document form. What this does is create a central repository of data that can be used by airports, in advance of travel. Data like knowing when a traveler is due to arrive or depart, whether in transit and what they might consider buying – maybe electronics, or a meal or … you know what you’d want.
Of course, what really makes this powerful is the ability to aggregate the traveler travel plans with preference data. We provide very specific analytics relating to preference and context. By context we mean the intent of the customer at a time and location. The term context is always misused, in that many assume that time and location equal context. This is not true. Think ‘transit’ versus ‘O&D’ – two different contexts.
Most importantly, our analytics focus on what we call ‘smart data’. While everyone seems to be into big data, what is really important is the data as it specifically applies to the traveler or customer during their journey. What we’re looking for is the obvious – time and location, interdependencies and associations. Most important is our ability to and match it to preference and context.
Our result case study covers many of the challenges and lessons learned.
Our analysis working with airport data, estimates that there could be up to a 5 to 12% increase over the existing customer retail spend in airports. This goes a way to helping airports increase their non-aeronautical revenues. And most importantly, let’s figure out how we can create a frictionless NOT seamless travel experience for travelers.