Personal recommenders to improve online user experience

Custom Predictive Modelling // Divolte




Rob Dielemans

Our role

Custom Predictive Modelling // Divolte

Background info about Transavia

Personal recommenders on Transavia website

Every year, Transavia, one of the largest European low-cost airlines and a wholy-owned subsidiary of KLM, transports over 12 million passengers. The number of routes that the low-cost carrier offers hit 220 in 2016, and continues to grow. Transparency and service are key value drivers for the airline. In order to improve the user experience of their website, Transavia has introduced personal recommendations based on machine learning algorithms. For the collection of clickstream data, the airline implemented open source clickstream collector Divolte. For the development of the recommenders Transavia engaged in a close collaboration with the data scientists of GoDataDriven.

Key Elements

  1. Distinction by better service

  2. Personal recommenders on the Dutch Transavia website

  3. Better service by collecting customer information

  4. Architecture of the Transavia recommenders

  5. The increased importance of the data team

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Transavia Case Study

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Technology we used

GoDataDriven developed recommenders that provide our visitors with a relevant experience in every step of the customer journey by comparing individual sessions with historical website data in real-time.

Charles Verstegen
Senior Revenue Manager, Transavia