Dealing with consumer differences in liking during repeated exposure to food; typical dynamics in rating behavior

Consumers show high interindividual variability in food liking during repeated exposure. To investigate consumer liking during repeated exposure, data is often interpreted on a product level by averaging results over all consumers. However, a single product may elicit inconsistent behaviors in consumers; averaging will mix and hide possible subgroups of consumer behaviors, leading to a misinterpretation of the results. To deal with the variability in consumer liking, we propose to use clustering on data from consumer-product combinations to investigate the nature of the behavioral differences within the complete dataset. The resulting behavioral clusters can then be used to describe product acceptance. To test this approach we used two independent data sets in which young adults were repeatedly exposed to drinks and snacks, respectively. We found that five typical consumer behaviors existed in both datasets. These behaviors differed both in the average level of liking as well as its temporal dynamics. By investigating the distribution of a single product across typical consumer behaviors, we provide more precise insight in how consumers divide in subgroups based on their product liking (i.e. product modality). This work shows that taking into account and using interindividual differences can unveil information about product acceptance that would otherwise be ignored

Dealing with consumer differences in liking during repeated exposure to food; typical dynamics in rating behavior

Consumers show high interindividual variability in food liking during repeated exposure. To investigate consumer liking during repeated exposure, data is often interpreted on a product level by averaging results over all consumers. However, a single product may elicit inconsistent behaviors in consumers; averaging will mix and hide possible subgroups of consumer behaviors, leading to a misinterpretation of the results. To deal with the variability in consumer liking, we propose to use clustering on data from consumer-product combinations to investigate the nature of the behavioral differences within the complete dataset. The resulting behavioral clusters can then be used to describe product acceptance. To test this approach we used two independent data sets in which young adults were repeatedly exposed to drinks and snacks, respectively. We found that five typical consumer behaviors existed in both datasets. These behaviors differed both in the average level of liking as well as its temporal dynamics. By investigating the distribution of a single product across typical consumer behaviors, we provide more precise insight in how consumers divide in subgroups based on their product liking (i.e. product modality). This work shows that taking into account and using interindividual differences can unveil information about product acceptance that would otherwise be ignored