Classification trees as machine learning tool to explore consumers’ purchasing decision pathway. A case-study on parent’s perception of baby food jars

Abstract

Classification and regression trees are machine learning methods useful to model non-linear and complex relationships. The objective of this study was to use classification tree method to model purchasing parents’ decision of buying or rejecting baby food jars and to identify the different pathways in the decision of consumers that explain the different purchasing patterns. For that, parents (n = 100) of babies (6 to 36 months old) evaluated 16 baby food jar images. For each one, parents indicated their purchase intention but also evaluated other aspects like expected liking, trust, health, and nutritive perception. Three clusters with different purchasing patterns were obtained and classification trees showed the different hierarchy of factors in the pathway toward the purchasing decision. For cluster 1 decision was based mainly on liking but conditioned to trust perception. For cluster 2, the decision was firstly based on healthiness perception and conditioned to liking. Cluster 3, the decision included a more complex pathway initiated by trust and followed by liking, nutritive and healthiness perceptions. The approach combining clustering and classification tree techniques is useful to explore and understand individual differences in consumers purchasing decision.