Affiliation:
1. Information Systems and Computing, Regional University of Blumenau, Blumenau 89030-903, Brazil
2. Digital Industry Center, Fondazione Bruno Kessler, 38123 Trento, Italy
3. Department of Mathematics, Computer Science and Physics, University of Udine, 33100 Udine, Italy
4. Expert Systems and Applications Lab, Faculty of Science, University of Salamanca, 37008 Salamanca, Spain
Abstract
While there are several ways to identify customer behaviors, few extract this value from information already in a database, much less extract relevant characteristics. This paper presents the development of a prototype using the recency, frequency, and monetary attributes for customer segmentation of a retail database. For this purpose, the standard K-means, K-medoids, and MiniBatch K-means were evaluated. The standard K-means clustering algorithm was more appropriate for data clustering than other algorithms as it remained stable until solutions with six clusters. The evaluation of the clusters’ quality was obtained through the internal validation indexes Silhouette, Calinski Harabasz, and Davies Bouldin. When consensus was not obtained, three external validation indexes were applied: global stability, stability per cluster, and segment-level stability across solutions. Six customer segments were obtained, identified by their unique behavior: lost customers, disinterested customers, recent customers, less recent customers, loyal customers, and best customers. Their behavior was evidenced and analyzed, indicating trends and preferences. The proposed method combining recency, frequency, monetary value (RFM), K-means clustering, internal indices, and external indices achieved return rates of 17.50%, indicating acceptable selectivity of the customers.
Subject
Computational Mathematics,Computational Theory and Mathematics,Numerical Analysis,Theoretical Computer Science
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