Anthocyanins are a class of polyphenols that have received widespread recent attention due to their potential health benefits. However, estimating the dietary intake of anthocyanins at a population level is a challenging task, due to the difficulty of scaling dietary surveys. Further, there is limited evidence as to who regularly consumes anthocyanins, whether temporally, spatially, or culturally according to levels of socioeconomic deprivation. Leveraging a massive retail loyalty card dataset in the UK, we pair two years of real-world purchasing data for 619,524 regular shoppers and 207 million shopping baskets with anthocyanin estimates drawn from polyphenol databases. We subsequently analyse relative deprivation levels of the neighbourhoods in which shoppers reside, illustrating how anthocyanin intake varies according to affluence. Results indicate that deprivation is linked dramatically with both lower total intake of anthocyanins and lower breadth of dietary sources for them, potentially aggravating the incidence of diet-related diseases in the poorest sections of society.

Understanding and measuring the predictability of consumer purchasing (basket) behaviour is of significant value. While predictability measures such as entropy have been well studied and leveraged in other sectors, their development and application to very large multi-dimensional data sets present in the retailing sector are less common. While a small number of methods exist, we demonstrate they fail to accord with intuition, leading to the potential for misunderstandings between those who conduct the analysis and those who act on the insights. We delineate the requirements for such a measure in this domain to demonstrate these issues in context. A novel measure is then developed based on entropy to directly measure the predictability of basket composition. The measure is designated as bundle entropy (zero denotes a bundle’s total predictability, one the total unpredictability). We empirically compare the proposed bundle entropy against existing measures using two large-scale real-world transactional data sets, each including more than 2,000 households (frequent shoppers) over two years. First, we demonstrate how the proposed measure is the only measure that behaves according to the desired properties. Second, we show empirically that bundle entropy differs noticeably from the other measures. Finally, we consider some use case analyses and discuss the utility of the proposed measure in practice.