  Customer purchasing behavior analysis plays a key role in developing
insightful communication strategies between online vendors and their customers.
To support the recent increase in online shopping trends, in this work, we
present a customer purchasing behavior analysis system using supervised,
unsupervised and semi-supervised learning methods. The proposed system analyzes
session and user-journey level purchasing behaviors to identify customer
categories/clusters that can be useful for targeted consumer insights at scale.
We observe higher sensitivity to the design of online shopping portals for
session-level purchasing prediction with accuracy/recall in range
91-98%/73-99%, respectively. The user-journey level analysis demonstrates five
unique user clusters, wherein 'New Shoppers' are most predictable and
'Impulsive Shoppers' are most unique with low viewing and high carting
behaviors for purchases. Further, cluster transformation metrics and partial
label learning demonstrates the robustness of each user cluster to
new/unlabelled events. Thus, customer clusters can aid strategic targeted nudge
models.
