Identifying user groups: a machine learning framework for classifying Job Roles based on clickstream data

Han, QiweiSilva, BrunoAlmas, MiguelEsary, William2025-01-022025-01-022024-01-262023-12-15http://hdl.handle.net/10362/176919Clickstream data, the digital footprint of a user’s online browsing activity, offers a unique window into an individual’s interests and intentions. This work showcases a machine learning framework designed to classify website visitors by job function using features crafted from 6 months’ worth of server-side clickstream data. These predicted job functions can be used to send targeted communications to end users of a low-code B2B service in order to boost engagement. The study finds success at differentiating developers, the key users, from other job roles based on their use of the company website.engMachine learningUser segmentationTargeted advertisingOutsystemsClickstream dataIdentifying user groups: a machine learning framework for classifying Job Roles based on clickstream datamaster thesis203681819