Wilbert, H.J.; Hoppe, A.F.; Sartori, A.; Stefenon, S.F.; Silva, L.A. Recency, Frequency, Monetary Value, Clustering, and Internal and External Indices for Customer Segmentation from Retail Data. Algorithms2023, 16, 396.
Wilbert, H.J.; Hoppe, A.F.; Sartori, A.; Stefenon, S.F.; Silva, L.A. Recency, Frequency, Monetary Value, Clustering, and Internal and External Indices for Customer Segmentation from Retail Data. Algorithms 2023, 16, 396.
Wilbert, H.J.; Hoppe, A.F.; Sartori, A.; Stefenon, S.F.; Silva, L.A. Recency, Frequency, Monetary Value, Clustering, and Internal and External Indices for Customer Segmentation from Retail Data. Algorithms2023, 16, 396.
Wilbert, H.J.; Hoppe, A.F.; Sartori, A.; Stefenon, S.F.; Silva, L.A. Recency, Frequency, Monetary Value, Clustering, and Internal and External Indices for Customer Segmentation from Retail Data. Algorithms 2023, 16, 396.
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 6 clusters. The evaluation of the clusters’ quality was obtained through the internal validation indexes: Silhouette, Calinski Harabasz, and Davies Bouldin. Once 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.
Keywords
retailing; customer behavior; clustering; segmentation; external validation indices
Subject
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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