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.

To capitalize on this billion-dollar opportunity, airlines must first overcome the biggest hurdle: human resistance and institutional inertia around data-driven decision making. Too often, pricing and revenue management teams rely solely on personal experience and gut instinct instead of objectively letting data be their guide. Breaking free from this mentality through strategic change management is crucial.

Also, the Indian aviation industry can draw valuable insights from the successes of e-commerce giants like Flipkart and Amazon. Utilizing data science techniques to analyze customer behavior and adjust pricing strategies accordingly is crucial for every airline's success, mirroring the practices employed by leading online retailers.

An intriguing example is Cleartrip, one of India's prominent online travel agencies, which operates under the umbrella of Flipkart. Leveraging Flipkart's extensive e-commerce expertise and data science capabilities, Cleartrip is tapping into this wealth of knowledge to enhance its own operations. By cross-referencing patterns and insights gleaned from Flipkart's experiences, Cleartrip is integrating advanced analytics into its travel business model.

This symbiotic relationship highlights the potential for airlines to learn from e-commerce practices. With vast amounts of data at their disposal, airlines can emulate the strategies employed by e-commerce platforms, optimizing their services and offerings to meet the evolving needs of travelers. Embracing these insights and implementing them effectively is essential for the aviation industry to thrive in the digital age.Here are three core strategies to get your data monetization engine humming at full throttle:

1. Dynamic, Hyper-Personalized Pricing The future of airline pricing lies in continuous, dynamic pricing models rather than static, fixed fares. Industry leaders like Emirates are already paving the way towards true personalized pricing, where every ticket price is tailored to each individual customer based on their willingness to pay and preferences.

But the opportunities go far beyond just dynamically pricing the base airfare itself. Airlines can significantly boost revenue by applying data-driven pricing strategies to all the ancillary products and services like preferred seats, meal packages, etc. Even charging $2-3 more for preferred meals can generate billions in extra revenue at scale across the millions of passengers.

Crucially, the airline ticket purchase represents the important first step in a customer's travel journey, whether for business or leisure. At this point, the airline has a "hot lead" on the customer's transient preferences like destination, travel dates, and can analyze their purchase data to identify other likely needs like hotel, car rental, activities, etc.

However, most airlines currently let these customers slip away to book those additional travel services elsewhere after purchasing just the plane ticket. This is a massive missed opportunity! The airline is sitting on a trove of customer data and purchasing intent signals. By building the right vendor relationships and packages, they can seamlessly upsell customers on the full suite of travel needs, generating lucrative ancillary revenue.

The key is leveraging advanced data analytics, dynamic pricing capabilities, and artificial intelligence to accurately pinpoint each customer's preferences and willingness to pay for various add-ons and packages. Airlines have barely scratched the surface of capitalizing on the valuable customer data they already possess from that first air ticket purchase.

2. Intelligent Ancillary UpsellingOnce a customer books their ticket, their travel journey is just beginning - creating lucrative ancillary revenue opportunities around preferred seats, meal packages, hotels, car rentals and more for savvy airlines. By deeply analyzing enriched customer data spanning demographics, preferences and historical purchase patterns, you can identify the highest-value, most relevant upsell offers to maximize conversion rates and revenue.A significant revenue opportunity that airlines often overlook is excess baggage fees. Typically, excess baggage takes a back seat because it is not seen as a high-margin ancillary revenue stream. The conventional thinking is that the more excess bags you allow, the higher your fuel and operating costs.

However, based on Dharmesh's experience, the aviation industry needs to put much more effort into properly analyzing excess baggage patterns and customer behaviors. Currently, most airlines employ a very basic flat-rate pricing model - for example, charging a fixed $30 fee per extra piece of luggage over the included allowance.

But there is immense untapped value in taking a data-driven approach. By analyzing customer purchase data, especially for airport excess baggage purchases, airlines can uncover why certain customers buy excess baggage and under what circumstances. With those insights, they can move away from inflexible flat rates.

The real opportunity lies in implementing dynamic, variable pricing for excess based on factors like aircraft load factors on each flight. It's simple - if you know a flight will only be 50% full, you can actually offer discounted excess rates to attract more customers like freight shippers who may prefer flying packages rather than trucking them. For price-insensitive customers, the flat rate fees can remain. But for others, offering more affordable excess options on lighter flights creates new revenue that would have otherwise gone to trucking or shipping alternatives.

The potential financial upside is massive when considering the number of daily flights for major carriers. An airline like IndiGo operating 2,000 flights per day could generate significant revenue even if 25% of flights attract some discounted excess baggage business that wouldn't have occurred otherwise. And that's pure profit with minimal added cost.

Interestingly, no major airline is currently implementing sophisticated excess baggage analytics and dynamic pricing models. This represents a greenfield opportunity for innovative, data-driven carriers to get ahead of the competition.

3. Advanced Revenue Management via AI/ML Of course, artificial intelligence is poised to be the next big frontier that propels the aviation industry to new levels of performance and capabilities. While revenue management systems were a major breakthrough back in the 1970s, AI represents an even bigger revolutionary force.

The world's leading airlines are already implementing AI-driven revenue management models or are on the cusp of doing so. These sophisticated AI systems have the power to analyze and synthesize disparate data from across the entire airline's operations in ways that individual human analysts cannot.

The human mind, whether an analyst, sales executive, or network planner, can only process a limited subset of information based on their specialized role. But a well-designed, comprehensive AI system can ingest, correlate and derive insights from a complete universe of data sources both internal and external to the airline.

For example, when it comes to network planning decisions around where to fly new routes and optimize schedules, legacy approaches rely on input from revenue management and sales teams based on their siloed perspectives. An AI system could go far beyond that. It could ingest purchasing data from the revenue management system, agency distribution patterns from sales, social media buzz around potential new destinations, operational constraints like existing route profitability and capacity, and much more.

By applying advanced machine learning models to this rich, multi-dimensional dataset, the AI can identify the most lucrative new routes and schedules that would never be obvious from looking at any single data source alone. And it can dynamically update and optimize these recommendations as conditions change.

The possible applications are limited only by the data ingested and the specific use cases trained. There are enormous amounts of potential inputs an AI system can process, as long as the data exists and is regularly updated. In my opinion, this will completely transform airline management and operations.

In hindsight, revenue management was a pivotal first step, but AI will take the industry to a whole new level of data-driven decision making, product customization, and process automation in the years ahead. Airlines willing to embrace this powerful technology have the opportunity to pull ahead of the competition.

1. Analyze Your Customer Data for Low-Hanging Fruit. Leverage tools to segment customers based on historical booking characteristics, spend patterns, and any other data you have on hand. Identify the highest-value audiences and customer personas to target with personalized travel offers and upsell opportunities.

2. Review Your Current Pricing Strategy. Are you dynamically adjusting fares multiple times daily based on demand and competition? Identify routes where you have flexibility to test more advanced, data-driven pricing tactics like time-of-day or day-of-week pricing changes.

3. Explore Entry-Level Revenue Management Tools. While building internal data science teams takes time, user-friendly third-party revenue management solutions exist to add basic forecasting, scenario modeling, and pricing optimization capabilities. The investment is often less than expected to get started.