Meet LiSA

Personalization and Insights

Personalized Recommendations: AI analyzes historical PNR data to offer personalized services and products. For example, it can recommend travel upgrades (e.g., better seats, lounge access) or offer targeted promotions based on travel history.

Customer Behavior Analysis: AI can detect travel patterns (e.g., frequent destinations, preferred airlines, or booking classes), helping the travel agency tailor suggestions or loyalty offers that match the customer’s preferences.

Preferences Detection: Based on decoded PNRs, AI can automatically detect passengers’ preferences such as favorite seat types, food preferences, or loyalty programs, and apply these in future bookings.

Predictive Analytics

Flight Price Prediction: AI can analyze historical price data and PNRs to predict when flight prices are likely to rise or fall, providing alerts to travel agents or customers for optimized booking times.

Delay Prediction: AI models can predict potential flight delays by analyzing historical flight data, weather conditions, and airline schedules. This information can help travel agencies proactively manage rebookings or notify customers of potential disruptions.

Baggage and Ancillary Predictions: AI can estimate baggage fees or recommend ancillary services based on the PNR data, like excess baggage or in-flight services. It can also forecast how much ancillary revenue a travel agency might generate based on patterns detected in the PNRs.

Fraud Detection and Security

Anomaly Detection in Bookings: AI can detect unusual booking patterns or suspicious activities, such as last-minute itinerary changes, bookings with high-risk payment methods, or PNRs associated with flagged travel histories. This helps detect potential fraud or abuse of loyalty programs.

Sensitive Data Masking: AI ensures compliance with privacy laws (e.g., GDPR) by masking sensitive personal data from the PNRs (such as passport numbers or payment details) when it’s shared or displayed to unauthorized users.

Behavioral Analysis for Fraud Prevention: AI continuously monitors user behavior across the booking process, identifying deviations from typical patterns—such as multiple cancellations or unusual purchasing habits—which can indicate potential fraud and trigger alerts for further investigation.

Customer Sentiment and Satisfaction Analysis

Sentiment Detection from Notes: If the PNR contains free-text notes (e.g., complaints or service requests), AI can analyze the sentiment behind those notes. This is particularly useful for flagging unhappy customers or those likely to churn so that customer service can intervene proactively.

Service Improvement Insights: By analyzing common issues or complaints found in PNRs (e.g., frequent rebookings or meal issues), AI can provide insights into areas of operational improvement.

Upselling and Cross-Selling Opportunities

Targeted Ancillary Offers: AI can analyze the decoded PNR and suggest upsells, such as baggage allowances, Wi-Fi access, lounge access, or hotel upgrades, based on the traveler’s profile, journey type, or past behavior.

Customized Travel Packages: AI can use PNR data to create personalized travel packages, bundling flights, hotels, and other services based on the passenger’s travel patterns and preferences.

Compliance and Reporting

Regulatory Compliance: AI can ensure that PNR decoding follows regulations for various countries (e.g., APIS for the US, EU PNR Directive). It can automatically flag missing or incorrect data that may cause compliance issues, ensuring smooth immigration checks or cross-border travel.

Real-Time Reporting: AI can generate detailed reports for travel agencies or corporate clients, analyzing booking patterns, revenue trends, and customer insights based on the PNR data. This aids in strategic decision-making.