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UBC Theses and Dissertations

Segmentifier : interactively refining clickstream data into actionable segments Dextras-Romagnino, Kimberly


Clickstream data has the potential to provide actionable insights into e-commerce consumer behavior, but previous techniques fall short of handling the scale and complexity of real-world datasets. We present Segmentifier, a novel visual analytics interface that supports an iterative process of refining collections of action sequences into meaningful segments that are suitable for downstream analysis with techniques that require relatively small and clean input. We also present task and data abstractions for the application domain of clickstream data analysis, leading to a framework that abstracts the segment analysis process in terms of six functions: view, refine, record, export, abandon, and conclude. The Segmentifier interface is simple to use for analysts operating under tight time constraints. It supports filtering and partitioning through visual queries for both quantitative attributes and custom sequences of events, which are aggregated according to a three-level hierarchy. It features a rich set of views that show the underlying raw sequence details and the derived data of segment attributes, and a detailed glyph-based visual history of the automatically recorded analysis process showing the provenance of each segment in terms of an analysis path of attribute constraints. We demonstrate the effectiveness of our approach through a usage scenario with real-world data and a case study documenting the insights gained by an e-commerce analyst.

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