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Customer purchasing behavior analysis plays a key role in developinginsightful communication strategies between online vendors and their customers.To support the recent increase in online shopping trends, in this work, wepresent a customer purchasing behavior analysis system using supervised,unsupervised and semi-supervised learning methods. The proposed system analyzessession and user-journey level purchasing behaviors to identify customercategories/clusters that can be useful for targeted consumer insights at scale.We observe higher sensitivity to the design of online shopping portals forsession-level purchasing prediction with accuracy/recall in range91-98%/73-99%, respectively. The user-journey level analysis demonstrates fiveunique user clusters, wherein 'New Shoppers' are most predictable and'Impulsive Shoppers' are most unique with low viewing and high cartingbehaviors for purchases. Further, cluster transformation metrics and partiallabel learning demonstrates the robustness of each user cluster tonew/unlabelled events. Thus, customer clusters can aid strategic targeted nudgemodels.

OPAM: Online Purchasing-behavior Analysis using Machine learning