Solving the Look to Book Challenge in the Airline Industry: A Data-Driven and AI-Powered Approach
Introduction
Airlines process billions of searches daily, yet less than 1% convert to bookings. Major IT providers handle between 3 and 13 billion requests daily, with metasearch engines reaching up to 100 billion. Look to Book ratios can reach million-to-one ratios, and with GenAI on the horizon, we could foresee above millions-to-one without intervention. (IATA Look to Book Whitepaper Oct 2025).
This “Look to Book” challenge isn’t just a metric, it’s a massive drain on resources. The costs for airline and product companies are probably benefiting cloud suppliers ultimately. This gap represents a significant opportunity for airlines to optimize search systems, reduce costs, and unlock new revenue streams. The key lies in leveraging data, advanced AI techniques, and innovative infrastructure to create smarter, more efficient, and commercially viable solutions.
Why It Is Getting Worse
MetaSearch engines dramatically increased search volumes
Airlines process vast numbers of searches daily – from casual browsing to shopping. Yet, most don’t convert due to irrelevant results, poor personalization, and information overload. This creates expensive infrastructure with minimal revenue productivity.
NDC and continuous pricing make traditional caching ineffective
Content is no longer static: NDC generates personalized offers, while continuous pricing enables real-time fare adjustments, meaning cached data loses accuracy within seconds.
GenAI and agentic AI amplify the problem through automated shopping assistants and inspirational searches
Instead of a single user search, an AI travel assistant generates hundreds or thousands of queries to explore all possible combinations and monitor price changes in real-time before presenting a final recommendation.
Bots systematically hunt deals, generating massive queries without booking intent
Bots inflate search numbers while consuming valuable computational resources.
Simply scaling infrastructure is an economically unsustainable response. Smart architecture is the answer.
Travel search presents a dual technical challenge: capturing the complexity and diversity of user behavior – which varies based on price fluctuations, seasonality, personal preferences, and external events – while simultaneously delivering personalized results through sophisticated algorithms that can process vast amounts of data in real-time. Building models that are both accurate enough to improve conversion and scalable enough to maintain system responsiveness remains a core challenge and is essential to enhancing the overall user experience.
This is forcing a reinvention of the techniques to optimize the Look to Book, including working with the airline and the partners triggering those high unproductive volumes.
As the industry evolves beyond Look to Book ratios, leading airlines are adopting a smarter metric: Compute to Order. This new performance lens evaluates the total computing workload involved in generating, searching, and presenting offers, including the personalization and dynamic pricing logic behind them.
Traditional metrics count requests, Compute to Order measures what matters – total computational effort per booking, resource consumption across the entire offer lifecycle, hidden inefficiencies in large payloads and redundant data exchanges, and the true cost of complex fare filing processes. However, it comes with greater calculation complexity: unlike Look to Book, which simply requires counting requests, Compute to Order involves tracking multiple interconnected components across the entire transaction journey.
Building Smarter Search Systems: Core Principles
To address these challenges, airlines must develop systems that are not only efficient but also adaptive and commercially insightful.
- Efficient Data Storage and Computation
Implementing scalable data architectures such as data lakes, distributed processing frameworks like Apache Spark or Flink enable real-time analysis of search behaviors. These systems can process billions of search queries, capturing patterns and anomalies for further modeling. - Enhanced Personalization with AI
Machine learning models can analyze historical search data, user profiles, device types, geographic locations, and browsing sequences to accurately predict user intent. These models can adapt dynamically, refining search rankings and recommendations based on user interactions. - Incorporating Context and External Data
Real-time information such as fare prices, demand trends, weather forecasts, major events, and social media activity can significantly influence user preferences and decision-making paths. - Cost-Aware Search Optimization
Optimizing search operations involves understanding and managing the cost associated with each query. Developing models that weigh the potential revenue gain against search costs enables smarter allocation, reducing unnecessary computations while focusing on high-potential searches.
Unlocking New Revenue Opportunities: Commercializing Search Data
While improving conversion is crucial, search data itself can serve as a valuable commercial asset:
- Inspiration Search & Trend Analysis:
By analyzing popular destinations, seasonal preferences, and emerging travel hotspots, airlines can provide travelers with inspirational content and targeted marketing. Dynamic content based on recent trends can boost engagement and lead to higher conversion rates. - Price Prediction & Market Insights:
AI-powered models can forecast fare trends, providing travelers with insights into the best times to book and informing airlines about demand fluctuations. Aggregated insights can be monetized by selling market intelligence to different travel players. - Personalized Marketing & Upselling:
Using search habits, airlines can serve tailored offers, bundled deals, or upgrades at the optimal moment. Personalized recommendations based on search patterns increase the likelihood of upselling and customer satisfaction.
Our AI-Driven Solution Architecture
At TPConnects, we have already translated those principles into a working architecture ready for implementation.
Building an intelligent search ecosystem involves several interconnected layers:
- Data Collection Layer:
Gather search queries, user interactions, external market data, and contextual information. This data forms the foundation for all subsequent modeling. - Modeling & Prediction Layer:
Train machine learning models to estimate conversion probability, forecast fare trends, and understand user intent. These models enable targeted, relevant search results. - Cost & Revenue Optimization Layer:
Implement reinforcement learning agents that dynamically allocate resources, re-rank search results, and prioritize high-value searches while minimizing costs. They rely on new data to improve efficiency. - API & Interface Layer:
Deliver real-time, personalized search results supplemented with revenue-generating insights, offers, and trend information. - Analytics & Business Insights:
Dashboards and reporting tools monitor key performance indicators, enabling ongoing optimization of both system performance and commercial strategies.
The Benefits
Real-world deployment has already demonstrated significant results with a large carrier:
- 60% Search Reduction: By intelligently routing searches through our AI-optimized cache
- 97% Accuracy: Through continuous adjustment and learning
- Significant cost saving on Searches: Moving a channel from 2,000,000:1 down to 1,000:1 represents a x2,000 saving on the cost of the itinerary search on the Offer Creation system. This starts from the PPS however there is a domino of additional systems which are triggering legitimate costs with very high L2B.
- Revenue Generation: Freed computational resources enable new commercial opportunities:
- Display real-time price trends to create urgency and build traveler confidence
- Show “Best time to fly” recommendations indicating the cheapest travel dates
- Power inspirational search modules like, “Where can you go for $500?
- Send personalised offers based on reliable, pre-computed data.
This transforms the LTB challenge from a defensive cost-cutting exercise into a proactive strategy for revenue generation and improved customer experience.
Infrastructure Impact
Implementing such a system influences the underlying infrastructure:
- Scalability & Cloud Deployment: Utilize cloud platforms like AWS, Azure, or GCP for elastic compute and storage resources to handle fluctuating search volumes.
- Real-Time Processing: Deploy stream processing frameworks for low-latency responses, ensuring users receive timely, relevant results.
- AI & ML Infrastructure: Leverage GPU-accelerated environments for model training and inference.
The Future of Search is Smart, Not Just Scalable
The industry is rightly concerned about the “unstoppable growth of search requests,” especially with the rise of Generative AI. Simply scaling infrastructure is an expensive, unwinnable war. The future belongs to those who can intelligently manage, compute, and repurpose offer data.
The question isn’t whether to optimize. It is how quickly you can implement intelligent compute allocation to stay competitive.
