Behavioural User Identification from Clickstream Data for Business Improvement

One of the key elements for businesses to succeed is to get to know their customers. Traditionally this task has been performed through user studies, however, over the last few years clickstream analysis has been proposed as a potential way of conducting automated behavioural studies at scale. In this paper, we explore the use of a recently-proposed unsupervised data-mining technique to identify common behavioural patterns from a clickstream and use them to automatically group users into clusters. In particular, our goal is to validate the potential of behavioural user identification with respect to a key business-level objective. We consider to which extent it is possible to link overall user in-application behaviour to the completion of a particular business-relevant action. Identifying behavior patterns resulting in such business-relevant actions can enable businesses to make changes to their interface, target relevant user groups or trigger actionable insights, all with the objective of maximizing the likelihood of preferable user actions. We analyzed a realworld dataset from a mobile application deployed on both the iOS and Android platforms for this experiment.