Like participants in virtually every industry, airports are embracing digitization to optimize operations, increase asset lifeline and generate more non-aero revenue. Here are some examples:
Toronto Pearson Airport (GTAA) used a Near-field Communication (NFC) scanning system to enable passengers to quickly report problems using their mobile phones. The system reduced the time to report incidents by 97%, and the airport estimated that the system saved 180,000 minutes (3,000 hours) for customer experience representatives. The Toronto airport also transformed its contact center using artificial intelligence-powered automation, resulting in a 40% reduction in calls and an average wait time of less than 30 seconds, a dramatic improvement.
Similarly, in 2017, the Athens International Airport used big data analytics to study the behavior of 21.7 million passengers (annually) and found ways to reduce the number of delayed flights and improved the transfer process, so that fewer passengers missed their connecting flights.
These initiatives worked in different ways but have two things in common. First, they used digital technologies – particularly data analytics – to find and implement improvements. Second, they resulted in improved passenger experiences and satisfaction, which lead to growth in non-aero revenue. In fact, according to the Airports Council International 2016 Airport Service Quality survey, an increase of 1% in the global passenger satisfaction mean generates 1.5% growth, on average, in non-aeronautical revenue. This is increasingly important given the reduction, during and after the lockdown, in the number of people flying.
To improve the passenger experience and non-aero revenue growth, airports must capture more passenger data and use data to deliver a hyper-personalised experience for passengers. The way to start is by building a 360-degree profile – a unique, individualized perspective – for every passenger. Airports can do this by taking a few critical first steps.
Start with a centralized operational data layer: Airports should create a centralized data layer (or data lake) using cloud or on-premises storage infrastructure. All captured data will be synched to this data layer using various integration technologies such as APIs, data connection bridges, SQL queries, and batch uploads. Once this centralized layer is ready. airports can also capture data from niche airport systems and operations such as flight schedules, the Airport Collaborative Decision Making system (A-CDM), terminal ops, safety and security, and preventive maintenance. These kinds of operational data will help in mapping the passenger journey from entry to exit.
Capture passenger data across multiple touchpoints: While airlines have plenty of passenger data, airports generally do not. To build a better airport customer experience, it’s vital to capture data from multiple passenger touchpoints such an airport’s mobile app, free Wi-Fi registration, and duty-free shopping in international terminals. Of course, any and all data capturing plans must comply with each and every relevant data privacy and compliance law and regulation (such as GDPR).
In domestic terminals, where retail as well as food and beverage purchases don’t require a boarding pass, capturing customer information requires smartly designed promotional offers to encourage passengers to share correct mobile and email details, which can be verified via one-time passwords.
Build a business intelligence analytics layer: Once operational data is synched and the pool of passenger data deepens, it’s time to put it to work through a robust analytics layer. This business intelligence layer is important to understanding data and applying it to generate insights and predictive intelligence. Here are some instructive possibilities:
Non-aeronautical revenue growth can be an important component of an airport’s bottom line, and digital technology, harnessed well, can generate real growth. It requires the ability to unlock synergies between airport operations and airport facilities and, most important, to be able to hyper-personalize the passenger experience by using data analytics.
A good first step for an airport is to start with a short data strategy engagement designed to provide a “current state” assessment of their existing data landscape. That discovery initiative will inform the creation of a data and technology roadmap to help improve airport KPIs such as operational excellence, customer satisfaction, and non-aero revenue.
Madhu is the Global Practice Head for Airports in Wipro Ltd. She is a business technology professional and strategic planner with more than 21 years of experience across diverse industries and offering expertise in digital transformation for the global aviation industry (including airports and airlines). Her focus is on developing an integrated digital platform to build future-ready airports and organizations. She has extensive exposure to devising long-term business and technology strategies in addition to leading large-scale innovation, transformation, and migration programs.