Understanding IT Product Purchasing Behavior of MSMEs Using Sequential Pattern Mining Approaches

Sequential pattern mining is a crucial analytical method for understanding purchasing behavior and uncovering hidden patterns in transactional data. Unlike most prior studies that apply sequential pattern mining primarily in consumer-oriented retail settings or evaluate algorithms in isolation, this study investigates IT product purchasing behavior among Small and Medium Enterprises (SMEs) within a B2B digital transformation context through a direct comparative evaluation of three widely used algorithms: Apriori, PrefixSpan, and CloSpan. A series of controlled experiments was conducted on the same transactional datasets to assess algorithm performance in terms of accuracy, computational efficiency, and redundancy reduction. The results show that Apriori discovers exhaustive patterns at the cost of higher computational complexity, PrefixSpan achieves faster sequence extraction with balanced accuracy, and CloSpan effectively reduces redundancy by generating closed sequential patterns. Beyond pattern discovery, this study translates support, confidence, and lift metrics into actionable decision-support insights, highlighting how different algorithmic characteristics can be aligned with retention strategies, service bundling, and targeted interventions. These findings provide distinct methodological and practical contributions by positioning sequential pattern mining as a data-driven decision-support tool to accelerate digital transformation initiatives among SMEs in the IT product ecosystem.