Cargo airlines enjoyed a period of high revenue—driven by scarce capacity—during the pandemic. But after the boom of the past three years, yields are gradually falling from the 2021 peak. Belly cargo capacity is recovering, and demand is softening, leading to uncertainty as cargo airlines brace for the risk of a “back to normal” scenario.
This raises the issue of how cargo airlines can make sure that the “back to normal” is not a “hard landing”. In this environment, a new approach to revenue management could be the key that allows airlines to adjust their commercial strategies and continue to benefit from opportunities in the market.
Over the past three years, the cargo market has been capacity-driven and airlines with significant capacity pulled ahead of competitors. Recently, there seems to be a transition back to a demand-driven market: yields have declined, demand has slowed, and belly capacity continues to recover (Exhibit 1). Moving forward, rates are expected to decline further, although will likely remain above 2019 levels.
What this means is that new ways of working may be required for individual cargo airlines to remain competitive in this changing market. As belly capacity returns, the market will likely become increasingly competitive, and airlines that don’t have a robust commercial and revenue-management strategy in place might lose out and see their yields diminish faster than the average.
At the same time, many cargo airlines have invested considerably in their digital strategies since the pandemic began. In particular, online sales have boomed, and consequently, cargo airlines have access to much more data than was possible three years ago. A recent Freightos WebCargo report found that digitized air capacity across the industry reached 57 percent in Q1 2023, compared to 38 percent in Q1 2022, and only 3 percent in Q1 2019.
Taken together, the turning point in the market and the rise of digitization in the industry point to today being a crucial time to formulate next-generation revenue management for air cargo.
This article details three areas where cargo airlines can focus their efforts to re-think revenue management, specifically by relying on accurate forecasting to form actionable insights; using real-time monitoring for fast decision-making; and taking a customer-centric approach.
Using new tech to improve forecasts
Forecasting demand and supply is the starting point for a cargo pricing and revenue-management strategy. However, cargo demand is extremely challenging to forecast, for several reasons.
First, booking tends to be a last-minute process and late bookings are a consistent feature in this environment. Typically, two weeks before departure, less than 40 percent of an airline’s capacity has been booked. Second, the market is volatile. Air freight is often used by shippers as a last-minute restocking option, which depends on many economic factors, so the need for air freight can change almost overnight. Third, the air freight market is composed of dozens of industries, and thousands of commodities, each with different drivers that make demand difficult to predict.
But, airlines can leverage technological advances to improve demand forecasts and deal with volatility. The availability of more granular data sources, and the advance of Machine Learning (ML) algorithms, make it possible for cargo airlines to pursue better demand forecasting solutions to gain deeper insights—and ultimately make more nimble revenue decisions.
For instance, due to the increase in online sales, cargo airlines have more data available about their customers’ behavior. This is particularly the case for airlines that have their own sales portals. Through digitalization, the air cargo industry has an opportunity to build a 360-degree view of demand across the entire customer journey which includes data that is above the sales funnel, such as which flights customers search for, lead times, how the cargo request was made, how long it took to fulfill, and if there was a cancelation or modification. Airlines can also look at step-based conversion rates showing how the airline performs at each stage of the sales funnel (discovery, flight selection, product selection, price offer, etcetera). Having all of this data in one place means that cargo airlines can improve their customer experience: better understand what customers want, and when they are likely to want it. This is the type of insight that companies in B2C industries, such as passenger airlines or hotels, typically have access to and cargo airlines could consider using a similar approach and leaning into the e-commerce aspect of sales.
It’s clear that Artificial Intelligence (AI) and ML are transforming sectors and industries across the world—and cargo airlines could harness the power of AI to better predict demand. A McKinsey Global Institute study identified that the travel, transport, and logistics sector has the most potential for incremental value from AI, amounting to $1.8 trillion in value. Within this sector, roughly half of this value is likely to come from commercial applications such as customer service and pricing.
Cargo airlines are well positioned to increase forecasting accuracy through AI. For example, AI could make sense of the thousand or more commodities, as well as their inter-dependencies, within the supply chain. For instance, AI could determine how trends in raw materials and semi-manufactured products in one country could lead to a growth or decline in specific finished products in another—and how this would influence cargo demand.
There are a few pointers airlines could keep in mind when using AI for demand forecasting. It is important to select the right data as input, as it needs to be sufficiently granular. And using a blend of internal and external data can lead to greater forecasting accuracy as early as two weeks out, despite very few bookings being made at that time. Internal historical data is very important for improving forecasting quality, which tends to be overlooked.
Considering that the accuracy of ML algorithms increases with the amount of quality data being used, airlines will probably find that AI-enabled forecasts get more accurate over time. One cargo airline managed to improve its ability to predict demand significantly through the use of AI. Initially, the AI tool reduced the airline’s forecasting error from around 20 percent to 14 percent, and once it went live it continued to improve in accuracy.
The airline found that the AI model was much better at predicting seasonality patterns through multi-layered algorithms than traditional models. This allowed it to predict volume patterns to a high degree of accuracy from one to four weeks before departure. Furthermore, incorporating data on trends such as booking cancellations improved final volume predictions.
There are other untapped opportunities to leverage internal data, such as by predicting no-show rates for bookings by lane and by customer. Another airline followed this approach which led to better capacity management and, ultimately, improved profitability. Predicting cancellations allowed the airline to increase “overbooking” while still controlling for the risk of penalties (Exhibit 2). This, together with other specific use cases, helped to uplift load factors by around 8 percent after a 12-week pilot. Based on this success, the airline was able to identify potential network-wide savings worth tens of millions of dollars.
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