Recency, Frequency, Monetary Value, Clustering, and Internal and External Indices for Customer Segmentation from Retail Data
نویسندگان
چکیده
While there are several ways to identify customer behaviors, few extract this value from information already in a database, much less relevant characteristics. This paper presents the development of prototype using recency, frequency, and monetary attributes for segmentation retail database. For purpose, standard K-means, K-medoids, MiniBatch K-means were evaluated. The clustering algorithm was more appropriate data than other algorithms as it remained stable until solutions with six clusters. evaluation clusters’ quality obtained through internal validation indexes Silhouette, Calinski Harabasz, Davies Bouldin. When consensus not obtained, three external applied: global stability, stability per cluster, segment-level across solutions. Six segments identified by their unique behavior: lost customers, disinterested recent loyal best customers. Their behavior evidenced analyzed, indicating trends preferences. proposed method combining (RFM), clustering, indices, indices achieved return rates 17.50%, acceptable selectivity
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ژورنال
عنوان ژورنال: Algorithms
سال: 2023
ISSN: ['1999-4893']
DOI: https://doi.org/10.3390/a16090396