As advancements in technology, particularly big data analytics and artificial intelligence (AI), have transformed every industry, the airline sector and its revenue management teams have also been significantly impacted.
These technologies enable airlines to analyze vast datasets, allowing them to forecast demand, set optimal prices, and personalize offerings with unprecedented speed and accuracy.
Today, airlines collect and analyze vast datasets that include historical pricing, booking patterns, flight searches, and external factors like economic indicators and competitive pricing.
Big data analytics allows airlines to identify trends, understand customer behavior, and predict future demand with higher accuracy
AI, particularly machine learning and deep learning, has been used to process and learn from the data, enabling the automation of decision-making processes in pricing and inventory management. This includes dynamic pricing algorithms that adjust fares in real-time based on changing market conditions and customer demand.
Some airlines, driven by their Revenue Management Systems [RMS] use an advanced application of AI: reinforcement learning. A type of machine learning where algorithms learn to make decisions by performing actions and observing the results. They are used to dynamically adjust prices based on the predicted impact on customer behavior and overall revenue.
Airlines use AI models to forecast demand for flights, considering various factors such as seasonality, events, and competitor actions. Incorporating external data, such as economic indicators and market trends, further refines these forecasts, enabling more precise pricing strategies.
Integrating AI into airline pricing strategies offered numerous benefits. However, this integration also presents challenges.
The evolution of AI and big data processing capabilities has made dynamic and personalized pricing not just a possibility but a necessity for airlines aiming to optimize revenue. However, despite these advancements, the traditional Global Distribution Systems (GDS) used for flight booking and distribution pose a bottleneck. You cannot apply Open Pricing, reinforcement learning practices, when GDS systems take a day to update price change.
But that has changed with the New Distribution Capability (NDC) standard, enabling the direct distribution of airline content to travel agents, bypassing traditional GDS limitations.
Therefore the real value of NDC relies not in richer content, but in more dynamic pricing information, enabling airlines to offer Open Pricing on their websites and through travel agents.
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