AviationOutlook The airline industry is known for its volatility when it comes to revenue. Demand fluctuates constantly based on seasonality, competitive actions, economic conditions, and unforeseen events like weather disruptions or health crises. This makes revenue management and pricing extremely challenging for airlines. Teams tasked with maximizing revenue and minimizing risk have to be agile, leverage data, and use sophisticated systems and strategies to try and smooth out the peaks and valleys.
Understanding Revenue Volatility in Airlines
There are several key factors that contribute to revenue volatility for airlines:
- Variable Demand – Demand for air travel is highly variable by route, by day of week, by season, and in response to sales and competitive pricing activity. Holidays and summer vacations create peak travel periods while off-peak periods can see steep demand declines.
- Perishable Inventory – Airline seats are a highly perishable product. Once a flight departs, any unsold seats equate to lost revenue opportunities. This creates pressure to engage in tactical pricing adjustments to sell seats.
- Low Marginal Costs – The incremental cost of filling an unsold seat is very low for the airline. This creates incentives to discount pricing at the last minute to capture additional revenue.
- Route Competition – Most routes feature competition from other airlines which leads to competitive counter-pricing and volatility.
- Macroeconomic Forces – Factors like recession, inflation, consumer confidence, unemployment, oil prices, etc. can all impact travel demand and volatility.
- External Events – Weather, air traffic control issues, health advisories, airport disruptions, geopolitical events, and much more can alter or suspend travel on short notice.
This innate volatility makes the jobs of airline revenue management and pricing teams very challenging. The teams try to optimize pricing and inventory controls to stabilize revenue flows over time.
Revenue Management Systems and Capabilities
Airlines leverage highly sophisticated revenue management systems and analytics capabilities to try and manage revenue volatility. This includes:
- Historical Data Analysis – Studying years of booking data by route, forecasting demand cycles and optimal pricing curves.
- Machine Learning – Applying artificial intelligence to crunch data and identify revenue optimization opportunities.
- Dynamic Pricing Algorithms – Automated, real-time pricing engines that can react to booking trends and competitive data.
- Route Profitability Modeling – Detailed financial models on every route to optimize profitability amidst volatility.
- Competitive Intelligence – Tracking competitor pricing and supply changes across channels in real-time.
- Predictive Analytics – Statistical models to forecast demand and recommend optimal pricing to meet revenue targets.
- Customer Segmentation – Analyzing booking patterns and price sensitivity for leisure, corporate, and frequent flyers.
- Disruption Management – Capabilities to quickly adjust networks and re-accommodate passengers during irregular operations.
- Forecasting engine – Software that recommends pricing at micro-segments across distribution channels
These capabilities allow revenue managers to understand revenue volatility drivers, model different scenarios, and enable the airline to respond quickly to opportunities and competitive threats.
Revenue Management Organizational Structure and Processes
Airlines structure their revenue management teams and processes to collect data, monitor volatility, and enable optimal decision making:
- Centralized Revenue Management Divisions – Groups of analysts, data scientists, and specialized revenue managers organized under one division.
- Revenue Management Analysts – Analysts tasked with collecting data, identifying trends, building forecasting models, monitoring bookings and restrictions.
- Revenue Management Pricing Teams – Teams who analyze data and competitive pricing to adjust pricing up or down to optimize revenue.
- Route Managers – Individual managers who oversee pricing and inventory for specific route networks.
- Daily Monitoring – Review overall booking pace, yields, and competitive intelligence. Adjust demand forecasts and pricing levels.
- Routine Meetings – Cross-functional teams hold meetings to discuss trends, issues, and strategies.
- Event-Driven Changes – Rapid response to sudden external events or competitive moves. Executives authorize tactical pricing changes.
- Testing and Optimization – Use historical data to simulate different pricing scenarios and approaches. Implement optimal approaches during corresponding time periods.
The blend of data, systems, analytics, oversight, monitoring, and collaboration enables airlines to be agile and adapt pricing approaches amidst volatile conditions.
Pricing and Inventory Management Strategies
Some key pricing and inventory management strategies deployed by airline revenue management teams include:
- Segmentation Strategies – Separate price/inventory settings based on known customer behaviors – leisure, business, loyal frequent flyers, etc.
- Channel Analysis – Optimize pricing differently across channels – direct, OTAs, corporate, etc.
- Dynamic Pricing – Use algorithms to automatically adjust pricing up or down at the micro-segment level based on demand and competitive data.
- Competitor Based Discounting – Tactical pricing markdowns to counter a competitor’s pricing move or new market entry.
- Time of Day Control – Adjust availability and pricing for peak versus off-peak times to maximize revenue.
- Long-Term Restrictions – Limit booking further in advance for volatile periods, then open up nearer to departure.
- Short-Term High-Value Pricing – Raise “walk-up” fares closer to departure for travelers willing to pay more.
- Overbooking Optimization – Use data to optimize overbooking levels to reduce spoilage from no shows.
- Route Optimization – Adjust flight frequencies or redeploy capacity away from underperforming routes.
The goal is to use a sophisticated blend of automation, oversight, and strategic human decision making to adjust to real-time market conditions.
Impact on Consumers
Airline pricing practices focused on revenue optimization also impact consumers in a variety of ways, including:
- Fare Volatility – Consumers can be frustrated to see fares change constantly for the same flights.
- Lower Prices Early On – Consumers benefit from fare sales and discounts further in advance.
- Higher Prices for Last Minute – walk-up fares near departure dates are typically much higher.
- Booking Prompts – Airlines will highlight “limited time” discounts to induce early booking.
- Channel Differences – Pricing and availability can differ across airline.com, OTAs, corporate booking tools.
- Upselling Incentives – Airlines will offer perks, upgrades or amenities to induce consumers to book higher fare classes.
- Complex Fare Rules – Fare classes come with varying change fee policies, cancellation rules, etc. that impact flexibility.
While revenue management practices aim to stabilize airline revenues, consumers do see side effects in terms of constantly changing and complex fare options. This requires diligence in monitoring pricing across channels for deals.
Future Evolution of Airline Revenue Management
Airlines will continue to evolve their revenue management and pricing capabilities and strategies. Some key areas to watch include:
- More Granular Forecasting – Using artificial intelligence to predict demand and set optimal pricing at extremely narrow sub-segments.
- Distribution Channel Optimization – Customized pricing strategies for each sales channel – direct, OTA, metasearch, etc.
- Ancillaries Revenue Expansion – Unbundling offerings to provide more add-on purchase options across more fare classes.
- Dynamic Overbooking Levels – Utilizing machine learning to dynamically adjust overbooking by route based on updated data.
- Real-time Competitive Pricing – Instant automated pricing changes in response to competitors’ offerings.
- New Data Sources – Tapping new data from mobile apps, social media, web browsing to detect early booking signals.
- Hyper-Personalization – Customizing offers like upgrades, discounts, and amenities individually for members.
While volatility will remain inherent to the industry, airlines will get sharper in leveraging data, technologies, and pricing strategies to maximize revenues through every twist and turn.
FAQs
Here are some common questions and answers about how airline revenue management teams tackle volatility:
How often do airlines change their prices?
Airline pricing can change multiple times per day on a given route depending on booking trends, competitive moves, and revenue targets. Pricing algorithms automate these changes.
What data do revenue management teams monitor daily?
Key daily data feeds include bookings by route, fare class, historical comparisons, cancellation rates, competitive pricing changes, web browsing activity, and macroeconomic or event driven factors.
How far in advance are airfares typically the cheapest?
Airlines typically offer the lowest fares and most availability about 3 months prior to departure. But this can vary based on competitive dynamics and expected future demand.
What is the best day of the week to purchase airfare?
Tuesdays and Wednesdays tend to have lower pricing on average as airlines stimulate demand to smooth bookings across the week.
How do airlines determine overbooking levels?
Revenue analytics teams use historical data on cancellation and no-show rates by route to build predictive models that optimize overbooking while minimizing bumping.
What is the difference between revenue management and revenue integrity?
Revenue management focuses on maximizing revenues through pricing and inventory controls. Revenue integrity emphasizes accurately collecting revenues booked to minimize leaks.
How are airline alliances impacting revenue management?
Alliances allow revenue sharing and coordinated scheduling between partner airlines. This expands possibilities for optimizing routes, pricing, and inventory availability.
How can machine learning improve airline revenue management?
Machine learning algorithms can rapidly analyze billions of data points to detect patterns and make micro-segment pricing recommendations that human analysts could miss.
