Unlocking Revenue Through Data in the Aviation Industry

For decades, Indian airlines have amassed vast troves of data on customer behavior, market dynamics, operations and more - a potential goldmine that has remained largely untapped. As the travel industry experiences a demand supernova, traditional pricing and revenue management models based on historical averages and analyst gut instincts are no longer enough. The carriers that are able to effectively monetize their data will gain a sustainable competitive advantage.

The value at stake is staggering. Currently, the Indian aviation industry is on a remarkable trajectory, boasting a growth rate of over 15% annually. Despite facing setbacks due to COVID, it has swiftly bounced back, with Indian carriers now actively placing massive orders for new aircraft, poised for explosive expansion. To put things into perspective, consider China, with over 3,000 aircraft currently in operation. In contrast, India, with its vast population surpassing China's, has fewer than 700 aircraft in service. Given the challenges of India's train services and burgeoning population, the potential for growth in aviation is enormous.

This opportunity presents a ripe field for data science to play a pivotal role. In the next decade or so, the aviation industry is primed to become one of India's fastest-growing sectors, akin to the FMCG industry.Case in point, let’s look at an example of how analyzing data can reveal important insights that go against long-held assumptions and drive key business outcomes.**** For many years, it was the norm to expect higher occupancy levels for flights on weekdays (Monday, Thursday, Friday) compared to weekends for all airlines.

However, when the Indian Ministry of Civil Aviation started publishing daily occupancy data segmented by airline, a clear pattern emerged: full-service carriers actually had higher occupancy on weekdays, while low-cost carriers saw higher occupancy on weekends. This was likely due to the different customer segments that tend to fly full-service versus low-cost.

This insight came as a surprise to the full-service airline teams who had been operating based on the weekday/weekend occupancy assumptions. So, by analyzing the new data, they realized there was a fundamental shift in the operating models required for full-service versus low-cost carriers.

This example shows that even basic data analytics, without advanced data science techniques, can uncover valuable insights that allow companies to change their mentalities and operating strategies. By being open to findings from data instead of relying solely on experience and gut instincts, airlines can optimize for actual customer behaviors*.*Separately, another major airline I worked with significantly boosted occupancy and yields simply by tracking real-time competitor pricing and analyzing customer purchase timing and patterns. They discovered that on certain routes, their price differential versus low-cost carriers needed adjustment based on day of week and time of day, without which their sales were being lost to the low-cost carriers. By rapidly implementing these data-driven insights into their dynamic pricing model and adjusting their price differential to around INR 700 instead of the initial INR 1000-1200, they regained optimal occupancy at the new fare levels.

Looking ahead, imagine taking this smart pricing approach to the next level through hyper-personalized fares and travel packages. An airline could leverage enriched data on customer preferences, purchase histories and contexts to dynamically present unique package offers tailored to each individual's willingness to pay. For some customers, that may mean prioritizing amenities while others simply want the cheapest base fare. This level of precision is the holy grail of revenue maximization.