Which statistical method BEST reveals latent consumer preferences from purchase history data?

Latent class analysis (LCA) is the statistical method that BEST reveals latent, or hidden, consumer preferences from purchase history data. LCA is a statistical modeling technique used to identify unobserved subgroups or classes within a population based on observed categorical variables. In the context of fashion, these observed variables are consumers' purchase histories, which include the types of clothing purchased, colors, brands, price points, and purchase frequency. LCA analyzes these purchase patterns to identify distinct groups of consumers who share similar, yet unobserved, preferences. Unlike simple clustering methods that group consumers based on explicit similarities, LCA infers underlying preferences. For example, LCA might identify a 'minimalist' class of consumers who consistently purchase neutral-colored, simple designs, even if they haven't explicitly stated this preference. It could also reveal a 'trendsetter' class that frequently buys new arrivals and designer collaborations. The 'latent' aspect means these classes aren't directly visible in the data; they are inferred by the model. The output of LCA is the probability of each consumer belonging to each latent class, providing a probabilistic segmentation that is more nuanced than traditional demographic segmentation. Fashion brands can use these insights to personalize marketing, tailor product offerings, and improve inventory management by predicting the preferences of different consumer segments.