Stark, widening health and income inequalities in the United Kingdom underpin the need for increased support for low-income families to access affordable and nutritious foods. Using anonymised supermarket loyalty card transaction records, this study aimed to assess how an additional Healthy Start voucher (HSV) top-up of £2, redeemable only against fruit and vegetables (FVs), was associated with FV purchases among at-risk households. Transaction and redemption records from 150 loyalty card-holding households, living in northern England, who had engaged with the top-up scheme, were analysed to assess the potential overall population impact. Using a pre-post study design, 133 of these households' records from 2021 were compared with equivalent time periods in 2019 and 2020. Records were linked to product, customer and store data, permitting comparisons using Wilcoxon matched-pairs sign-ranked tests and relationships assessed with Spearman's Rho. These analyses demonstrated that 0.9 more portions of FV per day per household were purchased during the scheme compared to the 2019 baseline (p = 0.0017). The percentage of FV weight within total baskets also increased by 1.6 percentage points (p = 0.0242), although the proportional spend on FV did not change. During the scheme period, FV purchased was higher by 0.4 percentage points (p = 0.0012) and 1.6 percentage points (p = 0.0062) according to spend and weight, respectively, in top-up redeeming baskets compared to non-top-up redeeming baskets with at least one FV item and was associated with 5.5 more HSV 'Suggested' FV portions (p < 0.0001). The median weight of FV purchased increased from 41.83 kg in 2019 to 54.14 kg in 2021 (p = 0.0017). However, top-up vouchers were only redeemed on 9.1% of occasions where FV were purchased. In summary, this study provides novel data showing that safeguarding funds exclusively for FV can help to increase access to FV in low-income households. These results yield important insights to inform public policy aimed at levelling up health inequalities.

OBJECTIVE:Scalable methods are required for population dietary monitoring. The Supermarket Transaction Records In Dietary Evaluation (STRIDE) study compares dietary estimates from supermarket transactions with an online FFQ. DESIGN:Participants were recruited in four waves, accounting for seasonal dietary variation. Purchases were collected for 1 year during and 1 year prior to the study. Bland-Altman agreement and limits of agreement (LoA) were calculated for energy, sugar, fat, saturated fat, protein and sodium (absolute and relative). SETTING:This study was partnered with a large UK retailer. PARTICIPANTS:Totally, 1788 participants from four UK regions were recruited from the retailer's loyalty card customer database, according to breadth and frequency of purchases. Six hundred and eighty-six participants were included for analysis. RESULTS:The analysis sample were mostly female (72 %), with a mean age of 56 years (sd 13). The ratio of purchases to intakes varied depending on amounts purchased and consumed; purchases under-estimated intakes for smaller amounts on average, but over-estimated for larger amounts. For absolute measures, the LoA across households were wide, for example, for energy intake of 2000 kcal, purchases could under- or over-estimate intake by a factor of 5; values could be between 400 kcal and 10000 kcal. LoA for relative (energy-adjusted) estimates were smaller, for example, for 14 % of total energy from saturated fat, purchase estimates may be between 7 % and 27 %. CONCLUSIONS:Agreement between purchases and intake was highly variable, strongest for smaller loyal households and for relative values. For some customers, relative nutrient purchases are a reasonable proxy for dietary composition indicating utility in population-level dietary research.

Introduction & BackgroundSupermarket transactions leave a digital footprint which offers insight into dietary habits. Use of transactions in nutrition research has increased, but these data are rarely validated. The STRIDE (Supermarket Transaction Records In Dietary Evaluation) study compares dietary estimates from supermarket transactions with self-reported intake from an online Food Frequency Questionnaire (FFQ). Objectives & ApproachWorking with a large UK supermarket, loyalty card customers were recruited to one of four waves (accounting for seasonal dietary variation). Participants completed an online FFQ and consented to sharing their transaction records for one year during the study, and one year prior. The Bland-Altman method was used to calculate agreement and limits of agreement between transactions and intake for daily energy, sugar, total fat, saturated fat, protein and sodium (absolute and energy-adjusted). Relevance to Digital FootprintsSupermarket transactions are a form of digital footprints data with advantages over survey methods, with regards scalability and objectivity, for monitoring population-level diets. Results1,788 participants from four UK regions gave consent. 686 participants who completed the FFQ and made purchases during the same period, were included for analysis. Participants were mostly female (72%), with a mean age of 56 years (SD 13). A regression equation for agreement is presented for estimating intake from purchases. Agreement for absolute measures was poor overall, but higher for single-person households and households reporting a higher proportion of total food purchases from the study retailer. Agreement was stronger for energy-adjusted nutrient estimates, particularly fat, with purchase records under-estimating the proportion of total energy intake from fat by just 2%. Conclusions & ImplicationsThe STRIDE study found that household purchases from a single retailer were a poor proxy for individual-level nutrient intakes. However, close agreement on average energy-adjusted estimates suggests purchases are a good indicator of dietary composition. Supermarket transactions have utility for population dietary assessment, ecological studies, and identifying intervention targets based on dietary patterns. Digital footprint data from transactions can contribute to the design and monitoring of national and local-level interventions.

The deployment of loyalty card and other consumer data in geographic research brings opportunities to explore and understand patterns of purchasing behaviour in unprecedented detail. However, valid generalisation requires thorough evaluation of their potential bias. We argue that, in competitive markets where consumers can choose to shop across competing retailers, loyalty card data from just one of these may not represent a ‘complete’ view of all purchases, and that this ‘completeness’ must be controlled for when assessing bias. To this end, we undertake a UK wide analysis of loyalty card data assembled by a major UK grocery retailer and provide guidelines for their effective deployment in the domains of urban and retail analysis. We assess, for the first time, the ‘completeness’ of circa 500 million customer transactions recorded by a major customer loyalty programme in representing the overall purchasing patterns of circa 16 million consumers across the entire UK, and develop a method by which to do this. Moreover, no operator has complete national store coverage, and so we suggest ways of accommodating this when conducting analysis using loyalty card data. We illustrate the importance of these issues before providing recommendations for the wider use of consumer loyalty card data.

Poor diet is a leading cause of death in the United Kingdom (UK) and around the world. Methods to collect quality dietary information at scale for population research are time consuming, expensive and biased. Novel data sources offer potential to overcome these challenges and better understand population dietary patterns. In this research we will use 12 months of supermarket sales transaction data, from 2016, for primary shoppers residing in the Yorkshire and Humber region of the UK (n = 299,260), to identify dietary patterns and profile these according to their nutrient composition and the sociodemographic characteristics of the consumer purchasing with these patterns. Results identified seven dietary purchase patterns that we named: Fruity; Meat alternatives; Carnivores; Hydrators; Afternoon tea; Beer and wine lovers; and Sweet tooth. On average the daily energy intake of loyalty card holders -who may buy as an individual or for a household- is less than the adult reference intake, but this varies according to dietary purchase pattern. In general loyalty card holders meet the recommended salt intake, do not purchase enough carbohydrates, and purchase too much fat and protein, but not enough fibre. The dietary purchase pattern containing the highest amount of fibre (as an indicator of healthiness) is bought by the least deprived customers and the pattern with lowest fibre by the most deprived. In conclusion, supermarket sales data offer significant potential for understanding population dietary patterns.

The existence of dietary inequalities is well-known. Dietary behaviours are impacted by the food environment and are thus likely to follow a spatial pattern. Using 12 months of transaction records for around 50,000 ‘primary’ supermarket loyalty card holders, this study explores fruit and vegetable purchasing at the neighbourhood level across the city of Leeds, England. Determinants of small-area-level fruit and vegetable purchasing were identified using multiple linear regression. Results show that fruit and vegetable purchasing is spatially clustered. Areas purchasing fewer fruit and vegetable portions typically had younger residents, were less affluent, and spent less per month with the retailer.

Store loyalty card data is a contemporary form of ‘Big Data’ with uses in geography and planning. These data capture a multitude of consumer behaviours, many of which have a spatial and temporal element. Using data provided by a major grocery multiple, this chapter discusses the application of this type of data in two key areas of retail analysis: calibrating customer behaviour in retail models and understanding the geodemographics of uptake of e-shopping by consumers. The wider insights gained from this application of store loyalty card data contribute to the understanding of the utility of big data, the proliferation of e-commerce engagement and the potential role of novel forms of data in the store location planning process.

AbstractIntroduction:Traditional dietary assessment methods in research can be challenging, with participant burden to complete an interview, diary, 24 h recall or questionnaire and researcher burden to code the food record to obtain a nutrient breakdown. Self-reported assessment methods are subject to recall and social desirability biases, in addition to selection bias from the nature of volunteering to take part in a research study. Supermarket loyalty card transaction records, linked to back of pack nutrient information, present a novel opportunity to use objective records of food purchases to assess diet at a household level. With a large sample size and multiple transactions, it is possible to review variation in food purchases over time and across different geographical areas.Materials and methods:This study uses supermarket loyalty card transactions for one retailer's customers in Leeds, for 12 months during 2016. Fruit and vegetable purchases for customers who appear to shop regularly for a ‘complete’ shop, buying from at least 7 of 11 Living Cost and Food Survey categories, were calculated. Using total weight of fruits and vegetables purchased over one year, average portions (80g) per day, per household were generated. Descriptive statistics of fruit and vegetable purchases by age, gender and Index of Multiple Deprivation of the loyalty card holder were generated. Using Geographical Information Systems, maps of neighbourhood purchases per month of the year were created to visualise variations.Results:The loyalty card holder transaction records represent 6.4% of the total Leeds population. On average, households in Leeds purchase 3.5 portions of fruit and vegetables per day, per household. Affluent and rural areas purchase more fruit and vegetables than average with 22% purchasing more than 5 portions/day. Conversely poor urban areas purchase less, with 18% purchasing less than 1 portion/day. Highest purchases are in the winter months, with lowest in the summer holidays. Loyalty cards registered to females purchased 0.4 portions per day more than male counterparts. The over 65 years purchased 1.5 portions per day more than the 17–24 year olds. A clear deprivation gradient is observed, with the most deprived purchasing 1.5 portions less per day than the least deprived.Discussion:Loyalty card transaction data offer an exciting opportunity for measuring variation in fruit and vegetable purchases. Variation is observed by age, gender, deprivation, geographically across a city and throughout the seasons. These insights can inform both policymakers and retailers regarding areas for fruit and vegetable promotion.

AbstractIntroductionSupermarket transaction data, generated from loyalty cards, offers a novel source of food purchase information. Data are available for large sample sizes, over sustained periods of time, allowing for habitual purchasing patterns to be generated. In the UK, recommended dietary patterns to achieve a healthy diet are pictorially illustrated using the Eatwell Guide. Foods include: Fruit and vegetables; starchy products including potatoes, bread, pasta, rice; dairy or dairy alternatives; proteins such as beans, pulses, fish, eggs and meat; oils and spreads; and advice to limit foods high in salt, fat and sugar. Through mapping of foods purchased to the categories of the Eatwell Guide it is possible to review population performance against these national recommendations.Materials and methodsAll loyalty card transaction records for purchases made in a UK supermarket chain, by residents of Yorkshire and the Humber during 2016 were included in this research. Customers who purchased foods from 7 or 11 Living Cost and Food Survey (LCFS) categories on ten or more occasions throughout the year were included in the sample, as these customers were considered to be purchasing the majority of their foods from the supermarket. All foods purchased were mapped to the Eatwell Guide food groups via the LCFS categories.ResultsHouseholds purchased: 25% of their total spend on fruits and vegetables, compared with 39% recommended; 13% on starchy products compared to 37% recommended; 23% of protein rich foods compared with 12% recommended; 12% dairy and alternatives compared to 8%; oils and spreads 2% compared to 1% recommended; and 25% foods that should be limited compared to 3% (recommended, but not pictorially illustrated on the plate).DiscussionSupermarket transaction data is a novel source of food purchase information which can be used to illustrate dietary behaviours in the UK population. However, it represents foods purchased, not consumed and is at a household level, not individual. Food purchases outside the home are not included. That said, it is arguably an objective measure for dietary assessment. From this study, it is clear to see that food purchases do not match the recommendations. Purchases of high sugar, high fat and high salt snacks constitute a significant proportion of spending, when they should in fact be limited. Protein rich products are also over-represented. Fruit and vegetables and starchy products are under-represented. This insight can benefit both retailers and policy makers for understanding the food purchase behaviours of our society.

Novel forms of data such as loyalty card or transaction data come with new challenges such as representation, messiness and incompleteness. These can cause uncertainty within analysis. The transactions for loyalty card holders of a major UK retailer are validated by investigating their completeness and spatial interactions in stores through comparison with Census travel to work flows and other neighbourhood patterns. A small percentage of cardholders (3%) are found to have atypical patterns, but almost half are estimated to have some degree of incompleteness, creating uncertainty when generalising to the wider population.

Ethnicity has long been a major subject in the realm of social research in the UK. It describes an umbrella of characteristics that are based on the premise that groups of people who have their roots in common ancestry, religion, nationality, language and territory share similar traits and culture (Bulmer, 1996). The definition, measurement and classification of ethnicity has attracted on-going debate in amongst researchers due to its multidimensional, subjective and complex nature (Mateos et al., 2009).