Two-Stage Clustering Approaches for Customer Profiling: A Practical Framework

Erik Kuiper*, Efthymios Constantinides, Sjoerd A. de Vries

*Corresponding author for this work

Research output: Contribution to conferencePaperAcademicpeer-review


Organizations often have difficulties to extract knowledge from data and selecting appropriate Machine Learning algorithms in order to develop robust User Profiles and segments. Moreover, marketing departments often lack a fundamental understanding on data-driven segmentation methodologies. This paper aims to develop a practical framework of Unsupervised Machine Learning (UML) algorithms for User Profiling with respect to important data properties. We conduct a systematic literature review on the most prominent UML algorithms and their requirements regarding the data properties. The proposed framework provides two-stage clustering approaches for categorical, numerical, and mixed types of data with respect to the data size and data dimensionality. In the first stage, a hierarchical or model-based clustering algorithm is applied to determine the number of clusters. In the second stage, a non-hierarchical algorithm is applied for cluster refinement. The two-stage clustering approach alleviates the drawbacks of solely using a hierarchical or non-hierarchical clustering procedure. The framework can support researchers and practitioners to determine which UML algorithms are appropriate for developing robust User Profiles and data-driven segments. The framework contributes to literature regarding approaches and methodologies for UML and data-driven segmentation in a marketing context. Future research can test the proposed framework on varying types of data, data sizes, and data dimensionality by conducting a case study.
Original languageEnglish
Publication statusPublished - 27 May 2019
Event27th Annual High Technology Small Firms Conference, HTSF 2019 - University of Twente, Enschede, Netherlands
Duration: 27 May 201928 May 2019
Conference number: 27


Conference27th Annual High Technology Small Firms Conference, HTSF 2019
Abbreviated titleHTSF


  • Unsupervised Machine Learning
  • Data-Driven Segmentation
  • Digital Marketing


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