At this point, digital transformation is no longer a consideration, it is a reality facing businesses the world over, and the aviation industry is not exempt from the changes posed by disruptive technology.
Take transactions, as one example. Today, more and more travelers complete their airline transactions using cards or payment services, either online or through their mobile device. Apart from the hassle-free nature of digital transactions, this also provides the aviation sector with an enhanced level of security, as digital transactions are more easily connected with passenger data. However, the real change brought on by digital transformation goes much deeper than facilitating easier transactions. It is the burgeoning trend of advanced analytics technologies, in fact, that can present a truly transformative value proposition.
A recent survey conducted by Unisys revealed that 59 percent of airport executives surveyed were looking to invest in advanced analytics solutions in the short-term; however, only 31 percent had already started using them. Most interestingly, those surveyed saw the potential benefits of analytics to positively impact a wide range of areas, including passenger flow; airport operations and asset tracking; geolocation and wayfinding; and passenger shopping and retail habits.
Simply put, the potential benefits that advanced analytics and machine learning can provide extend throughout the entire airport ecosystem. At a high level, analytics can play a significant role in improving the knowledge base, using insight to help simplify and improve the operational difficulties and challenges faced by the sector. However, the predictive prowess of machine learning techniques allows airlines to benefit in a variety of ways.
For example, airlines have long tried to decipher travel patterns and passenger preferences, but recent advances in analytics, using machine learning algorithms, makes it possible to understand the nuances of whether passengers can or are willing to pay for additional ancillary services related to air travel – by making that transactional data visible and most importantly, actionable. Consider your own travel patterns, as an example. You likely have an airport you frequent given your location, and have a particular dining option you prefer to visit. Through the intelligence gained from advanced analytics, airlines can further hone their services based on passenger preferences – offering discounts on the types of food or retail that they know the passenger prefers – which ultimately leads to customer loyalty and retention, in addition to establishing new opportunities to generate revenue. The future of customer service in air travel will involve custom-built itineraries and curated add-on services, based on individual preferences, that provide real-time suggestions based on choices you’ve made before. Those airlines that act now to understand that impending reality will find themselves at a competitive advantage.
As the travel domain continues to evolve, emerging trends like personalized itineraries, short-hop flying and custom-tailored experiences are likely to become mainstream and important elements of new offerings from airlines. Also, in order to keep up with these changes, the airlines must now treat customer behavior insight as a primary resource for gaining a differential edge in the market.
In a recent article on Better Customer Insight, Harvard Business Review charts out the following data points as the most important customer insights irrespective of how many different ways they interact with the company. These involve which brand, what medium was used for the interaction, the user experience, and if it managed to pursue/ convince the end-user. Key business decisions must be precisely honed using advanced predictive analytics to derive maximum value out of differential pricing, time sensitivity, flight route feasibility and optimum usage of all resources (including labor, taxi in-out times, fuel burn etc.).
The challenge for airlines will lie in not only balancing customer satisfaction with revenues, but also to cut out the noise of Big Data to zero on actionable behavior insights from data. Machine learning and predictive analytics is the next big wave in airline digitization that uses data, analytics and predictive algorithms to determine a traveler’s propensity to spend, and presents airlines with a wealth of opportunities.
Dheeraj Kohli is Vice President and Global leader - Travel & Transportation, Unisys