Executive Summary
The airline industry is currently navigating a pivotal transition from legacy, static systems to dynamic, real-time retail ecosystems. The future of optimization lies not only in adjusting fares but in the intelligent orchestration of data acquisition. This article explores a next-generation operational model—exemplified by the cooperation between Revenue Management Systems (RMS) like Yieldin and Competitive Intelligence (CI) providers like Aggregate Intelligence. By leveraging Offline Reinforcement Learning to optimize pricing and On-Demand Intelligence, airlines can achieve a new tier of efficiency and revenue maximization.
1. The Evolution: From Static Rules to Open Pricing
Traditional revenue management (RM) relied on rule-based heuristics, such as Expected Marginal Seat Revenue (EMSRb), which require accurate demand forecasting and often struggle when those forecasts are biased. While the industry is shifting toward AI-driven solutions, airlines have historically been wary of the volatility associated with “black box” algorithms.
This shift enables an “Open Pricing Logic,” where airlines are no longer bound by traditional alphabet-based booking classes. Yieldin addresses this by implementing a specific form of Offline Reinforcement Learning. Rather than learning through live, risky trial-and-error, the system leverages historical data to build robust pricing policies.
Alexandre de Tenorio, General Manager at Yieldin, suggests:“The era of static buckets is ending. True optimization requires “Open Pricing”, decoupling advanced pricing logic from the constraints of legacy delivery systems. However, we reject the volatility of ‘black box’ experimentation. Instead, we utilize Offline Reinforcement Learning. By treating every past flight as a completed learning episode, our system distills complex market dynamics into clear, executable policy rules.
This approach unlocks the precision of continuous pricing but delivers it through valid, explainable strategies. This ensures Revenue Managers can intuitively validate the logic, preventing suboptimal human overrides and securing the granular revenue that traditional heuristics miss.
This approach bridges the gap between the “infinite price points” of New Distribution Capability (NDC) and the operational need for stability. By processing historical state-action pairs, the agent learns to navigate the infinite pricing space effectively, achieving 96%–98% of theoretically optimal revenue without the risks of live experimentation.
2. The Data Paradigm: Intelligent and On-Demand Extractions
To fuel this Offline RL engine with current market context, airlines require precise data. However, acquiring this data—competitor pricing, inventory availability, and market trends—can be computationally expensive if done indiscriminately. Advanced data providers, represented by Aggregate Intelligence, have shifted from bulk scraping to “on-demand” extractions.
Kris Glabinski, VP Strategy and Business Development at Aggregate Intelligence, notes: “In a high-frequency market, bulk data is often obsolete by the time it is analyzed. We focus on ‘Intelligent Extraction’—providing granular, on-demand intelligence via API. This allows airlines to trigger specific data requests, from specific sources, only when market volatility is detected, capturing the nuances of continuous pricing without the overhead of massive, unrefined scrapes.”
This granularity allows airlines to capture dynamic offers that change based on context rather than fixed fare classes, which is essential for monitoring modern competitors .
3. The Future Model: Optimization of Fare and Source Extraction
The cooperation between an RL-based RMS (Yieldin) and an API-first CI provider (Aggregate Intelligence) creates a closed-loop optimization cycle. This goes beyond simple price adjustments; it optimizes the cost of intelligence itself.
- Intelligent Requests: The RMS uses RL to determine when new data is needed. If the market is stable, the agent minimizes extraction requests to save costs. If volatility is detected, the agent triggers “on-demand” extractions via the Aggregate Intelligence API .
- Strategic Efficiency: Just as RL learns to optimize seat inventory , it learns the “value of information,” balancing the cost of an API call against the potential revenue gain from a price adjustment.
4. Expanding the Horizon: Ancillaries and Virtual Interlining
The optimization model extends beyond the ticket price. The future airline offer is “unbundled,” requiring the separate optimization of ancillary services, which now account for up to 15% of global airline revenue .
The combined solution can track and optimize pricing for baggage, seat selection, and upgrades dynamically . Furthermore, as airlines embrace virtual interlining, the CI API becomes crucial for aggregating real-time schedule and price data from disparate carriers to create seamless multi-leg offers .
Conclusion
The cooperation between Yieldin and Aggregate Intelligence exemplifies the “New System” of airline retail—one that is dynamic, cloud-native, and data-centric. By integrating Offline Reinforcement Learning to create explainable, risk-free pricing strategies, and governing the flow of information through On-Demand Intelligence, airlines can achieve a dual optimization: maximizing revenue through precise, continuous pricing while minimizing the operational costs of data acquisition.
