Segmentifier: Interactive Refinement of Clickstream Data

Kimberly Dextras-Romagnino, Tamara Munzner

Abstract

Clickstream data has the potential to provide insights into e-commerce consumer behavior, but previous techniques fall short of handling the scale and complexity of real-world datasets because they require relatively clean and small input. We present Segmentifier, a novel visual analytics interface that supports an iterative process of refining collections of action sequences into meaningful segments. We present task and data abstractions for clickstream data analysis, leading to a high-level model built around an iterative view-refine-record loop with outcomes of conclude with an answer, export segment for further analysis in downstream tools, or abandon the question for a more fruitful analysis path. Segmentifier supports fast and fluid refinement of segments through tightly coupled visual encoding and interaction with a rich set of views that show evocative derived attributes for segments, sequences, and actions in addition to underlying raw sequences. These views support fast and fluid refinement of segments through filtering and partitioning attribute ranges. Interactive visual queries on custom action sequences are aggregated according to a three-level hierarchy. Segmentifier features a detailed glyph-based visual history of the automatically recorded analysis process showing the provenance of each segment as 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 a corporate e-commerce analyst.