  A success factor for modern companies in the age of Digital Marketing is to
understand how customers think and behave based on their online shopping
patterns. While the conventional method of gathering consumer insights through
questionnaires and surveys still form the bases of descriptive analytics for
market intelligence units, we propose a machine learning framework to automate
this process. In this paper we present a modular consumer data analysis
platform that processes session level interaction records between users and
products to predict session level, user journey level and customer behavior
specific patterns leading towards purchase events. We explore the computational
framework and provide test results on two Big data sets-cosmetics and consumer
electronics of size 2GB and 15GB, respectively. The proposed system achieves
97-99% classification accuracy and recall for user-journey level purchase
predictions and categorizes buying behavior into 5 clusters with increasing
purchase ratios for both data sets. Thus, the proposed framework is extendable
to other large e-commerce data sets to obtain automated purchase predictions
and descriptive consumer insights.
