Having only just started to get back on its feet from the pandemic, the airline industry has recently been hit with new challenges to recovery.
Global flight schedules have been ravaged by cancellations, delays and disruption due to numerous factors, including staff strikes. In July, one Monday alone saw airports around the world recording more than 25,000 flight delays and 3,100 cancellations.
Despite the difficulties airlines are experiencing, travelers remain keen to fly. However, the significant disruption to travel behavior caused by COVID-19 remains persistent. Remote work has enabled many people to relocate closer to one’s family and support network, requiring less travel to see loved ones. Moreover, working from home also shifted more educational and business engagements online, further reducing the need for business travel.
All these factors make it tricky for airlines to predict travel patterns, forecast demand for future travel, and optimize revenue. In a tough economic climate, soaring inflation in the U.K. pushed government interest costs to a record high in June. Furthermore, cancellation and refunds continues to threaten margins, and this makes reliable optimization of revenue a stand-out challenge.
As airlines look for every opportunity to drive demand and capture more bookings, there is a lot to be learned from the business-to-business (B2B) industry. B2Bs have traditionally operated successfully amid high demand volatility under highly competitive markets. And airlines should embrace some of B2B’s demand forecasting and pricing best practices to secure the most market share.
Here are three lessons that airlines can take from the B2B industry to survive the turbulent current conditions.
Don’t just abandon history
Due to the high predictability of people’s travel behaviors before the pandemic, airlines have traditionally leveraged long history (i.e., seasonality) over many years to help forecast travel demand far out into the future (up to a year out). These reliable predictions enabled airlines to optimize and price their future fares, which is what enabled consumers to book and pay for their flights up to a year in advance.
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However, the pandemic has significantly disrupted this, and long-historical bookings are no longer a reliable predictor of far-out future travels. So, airlines can’t use their historical seasonality in the same manner as before. This doesn’t, however, means that airlines should abandon historical seasonality all together.
B2B enterprises have always operated under demand uncertainties. The global supply chain has made the B2B market extremely competitive because buyers can find equivalent products at a comparable price and switch vendors easily. Therefore, B2B companies use a shorter relevant history (typically a few months or weeks) and forecast a shorter window (few months) into the future.
Requiring a smaller amount of data, this approach enables B2Bs to do these short forecasts quickly, more reliably, and more frequently. Consequently, B2B businesses can track the dynamic market condition more effectively. As more passengers are booking closer to departure due to travel uncertainties, this practice could help airlines capture more bookings, recoup revenues and recover faster.
Embrace diverse data
Traditionally, airline revenue management (RM) systems have used four major inputs to help them forecast demand and optimize prices. These include:
- Historical bookings (i.e., seasonality)
- Current bookings on hand
- Real-time availability
- Holidays and special events
With ongoing volatility across air travel, airlines have been forced to use a shorter booking history to make near-term forecasts. This reduces the data volume that airlines can use to forecast future travel demand, and in turn, the confidence of these forecasts. To improve the accuracy and confidence of demand forecasting, airlines can learn from B2B and retail businesses by incorporating a greater variety of data from different sources into their forecasting engine.
In both B2B and retail (where demand volatility is the norm), when businesses can’t use more of the same data (i.e., longer history), they usually leverage more different kinds of data. It’s fairly common for B2B marketers to use 10 or 15 different data sources to help them forecast demand. Everything - from historical purchases and web searches to website clickstreams, competitor prices and social media mentions and impressions - is fair game.
Some additional data sources that could potentially improve airline demand forecasting include flight search data, events data, weather, fuel price index and even economic indicators. PROS research found promising results when using both flight search data and events data to augment conventional booking forecasts. The research found flight search data could improve demand forecasting accuracy by up to 40% at an aggregate level. Although this result is highly inflated due to the during-pandemic test period and the small sample of manually selected 35 international markets, it suggests that shopping data may be a strong predictor of future bookings.
Consider willingness-to-pay (WTP)
Most revenue management systems work by generating bid prices that represent the expected marginal opportunity costs of a seat under various availability conditions. Airlines use this as an effective control mechanism to close fare classes that are priced below the bid price. This makes perfect sense economically, since selling below the bid price means the airline would incur a higher opportunity cost than the sales price.
This is a low-end control, and it’s most useful when the demand is high, and the airline is capacity constrained. However, conventional revenue management systems don’t have a high-end control mechanism, and it doesn’t tell the airline how high they can or should price a seat. While domestic flight routes have nearly fully recovered and air travel demand has recently exceeded expectations, many international flight routes have not yet returned to their pre-pandemic levels, and many airlines are not capacity-constrained. This excess in capacity could drive the bid price down to zero. Clearly, telling the airline to not sell a seat below £0 isn’t useful, since the airlines already knew this.
When there is a demand collapse, this is when high-end control is crucial. This is accomplished by estimating the consumers’ willingness-to-pay (WTP) for a seat. Since most B2B transactions are negotiated based on business relationships and context, B2B pricing has always been predominantly determined by the buyers’ WTP. The state-of-the-art B2B pricing engines are combining transaction history with both product and customer attributes to predict the WTP price under different business scenarios.
Airlines could benefit tremendously by considering passengers’ WTP and factoring WTP into their control mechanism, especially during demand disruptions. For example, Malaysia Airlines achieved an 8.7% passenger-revenue increase and a 7% yield increase by incorporating WTP into revenue management systems.
In comparison to other industries, airlines are in many ways ahead of the game, having leveraged predicative and prescriptive analytics for a long time. Whilst airline demand will continue to grow as the pandemic recedes and flight disruptions are resolved, demand will be different from before, with more volatility and turbulence.
Airlines that can learn from B2B businesses that have operated successfully in current market conditions will be most equipped to handle these challenges. Moreover, those that can quickly adopt the AI technologies and pricing practices from B2B will have more tools to inform their revenue management, and they will acquire long-term competitive advantages in the market as they continue to drive profitable growth.
About the author...
Dr. Michael Wu
is chief artificial intelligence strategist at PROS.