FOCUS: hybrid secretary bird super fairy optimized customer segmentation in omni-channel environment

In this paper, a novel hybrid secretary bird super Fairy Optimized CUstomer Segmentation (FOCUS) approach has been proposed for accurate clustering and meaningful customer groups. Textual data is analyzed to extract lexical and keyword-based features, whereas transactional behaviors are captured through RFM scoring. The features are clustered using the hybrid Secretary Bird Super Fairy (SBSFO) optimization, in which Secretary Bird Optimization (SBO) uses global exploration and Super Fairy Wren Optimization (SFWO) uses local exploitation. This model segments customers into meaningful groups, including top customers, loyal buyers, and occasional or lost customers. This approach provides businesses with deeper behavioral insights and enhances targeted marketing strategies. The proposed approach achieves an SI of 0.77, whereas existing RFM-FCA, RFM-K-Medoids, and SONN achieve 0.61, 0.65, and 0.58, respectively. The proposed FOCUS method achieves a minimum DBI of 0.34, outperforming RFM-FCA at 0.57, RFM-K-Medoids at 0.51, and SONN at 0.60.