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.
|Publication status||Published - 27 May 2019|
|Event||27th Annual High Technology Small Firms Conference, HTSF 2019 - University of Twente, Enschede, Netherlands|
Duration: 27 May 2019 → 28 May 2019
Conference number: 27
|Conference||27th Annual High Technology Small Firms Conference, HTSF 2019|
|Period||27/05/19 → 28/05/19|
- Unsupervised Machine Learning
- Data-Driven Segmentation
- Digital Marketing
Kuiper, E., Constantinides, E., & de Vries, S. A. (2019). Two-Stage Clustering Approaches for Customer Profiling: A Practical Framework. Paper presented at 27th Annual High Technology Small Firms Conference, HTSF 2019, Enschede, Netherlands.