Customer behaviour analysis based on buying-data sparsity for multi-category products in pork industry: A hybrid approach

Understanding customer behaviour is crucial for business success. For achieving this goal, the Recency–Frequency–Monetary (RFM) model has been commonly recognised as an effective approach to analyse behaviour. However, traditional RFM a coarse method quantifying loyalty and contribution that can only provide single lump-sum value of recency (R), frequency (F), monetary (M); hence, it discards information regarding customers’ product preferences. Typically, different customers make purchases. Subsequently, purchases are likely be across customers. This creates data sparsity, which affects performance conventional clustering methods. In study, we integrated group analysis probabilistic latent semantic models perform segmentation analysis. The results indicated developed takes into account preference provides insight captures wide variety types true ordering company’s allows manager improve relationships build personalised purchasing management system grouping with similar patterns.