Can machine learning and preference-driven shopping deliver true personalization in travel?

Personalization continues to be a mantra for many travel suppliers. As technology matures, traveler expectations evolve to include a greater desire for targeted search. This is especially true when sharing personal data delivers a better experience for the traveler. Yet, even mature technology faces the hurdle of inter-operability: how do you share data across systems so that a traveler’s past behavior and profile can inform future results? Machine learning is the process that applications learn from past behavior. This learning promises a “magical” search experience, automatically taking personal preferences into account. This preference-driven shopping framework is a true personalization in travel, helping suppliers more efficiently match trip itineraries to travelers. One example of this emerging practice in travel is a concept called Preference-Driven Airline Shopping. The concept is driven by a display algorithm for preference-driven…


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