Abstract

The objective of this study was to characterize the profile of the lamb consumer in the Brazilian semi-arid region using a machine learning approach. A total of 347 questionnaires with questions about socioeconomic aspects, consumption habits and preferences, meat preparation, and appreciation due to product quality were applied. A combination of cluster analysis (hierarchical and k-means), canonical discriminant analysis, neural network, and tree analysis were performed. The machine algorithms revealed three clusters: Traditional profile - Mature rural consumers, married or single, low level of education, low salary level, average consumption of meat, who do not pay for quality and do not know the benefits of lamb products; Emerging profile: Young men and women, single, with a high level of education, intermediate salary level, low consumption of meat, pay for quality, and do know the benefits of lamb meat; and Conventional profile: Mature urban consumers who live in the city, married and single, with a high level of education and salary, low consumption of meat, pay for quality and do not know the benefits of lamb meat The neural network confusion matrix for classifying consumers in the profile determined that 75.5% were classified in their group of origin, which validates the construction of the typology of consumers by the cluster analysis algorithms. Age was the main variable to segregate the nodes of the decision tree (P < 0.001): Node 1- < = 22; Node 2 > 22 < 29 and Node 3 > 29 years The use of a machine learning approach was able to reveal three types of consumers and defined patterns that could serve as strategies for increasing consumption and greater insertion of lamb in the meat market, as well as analyzing the perception of Brazilians in relation to the meat quality, where these findings may have implications for labelling schemes, marketing and sales strategies for cooked lamb products.

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