Loyalty Card Research Articles

Sustainable purchasing behavior and the consumer: Pre- and in-store interventions to facilitate the use of carbon labelling

This empirical article presents the results of two interventions at different stages in the purchase process designed to influence consumer purchase behavior towards lower carbon products. Survey and questionnaire results show the potential for targeted interventions to increase awareness and understanding of complex sustainability initiatives, such as carbon labelling. Analysis of supermarket loyalty card data provides further evidence of the widely cited attitude-intention-behavior gap. The value of measuring the behavioral impact of interventions objectively is therefore highlighted. The challenge of attempting to change behavior within real world contexts, such as a retail supermarket setting, is illustrated and the consequences of this discussed. Our findings present valuable insights for researchers studying behavioral change and stakeholders attempting to influence pro-environmental purchasing behavior.

A design of cross-selling products based on frequent itemset mining for coffee shop business

Using a case study at the XYZ coffee shop in Bandar Lampung, Indonesia, this work conducts knowledge extraction on a sales dataset to create a cross-selling model for product bundling advice. Understanding Knowledge extraction was done using a frequent itemset mining technique based on an Apriori algorithm to extract a set of association rules between products. The study implemented a five-stage frequent itemset structure that encompasses business comprehension, data preparation, data exploration, model creation using the Apriori algorithm, and rules evaluation. The framework that comes from this research provides a set of bundling association rules across items for cross-selling strategies that involve many products and complex sales timeframes. Additionally, we recommended sales activities based on loyalty cards to enhance our dataset with consumer attributes and purchasing trends. Based on customized services and tailored offers based on consumers' past purchases and spending patterns, the loyalty card recommendation was created. Consequently, we may target the appropriate clients and items with our marketing initiatives

Exploring the impact of giving free food samples and loyalty cards on sustainable food choices: A stepped wedge trial in workplace food outlets

Free samples and loyalty cards are frequently used, but there is little rigorous empirical testing of their effects. We conducted a stepped wedge trial in 29 workplace food outlets to investigate their effects on sales of plant-based meals. Outlets were randomly assigned to three sequences that entered the intervention in the first, second, or third week of August 2022. Free samples of plant-based meals were given out in the first week of the intervention; loyalty cards were available throughout, entitling the bearer to a free meal after they had bought three. The intervention period ended in the last week of August for all outlets. The free meal could be redeemed until one month later. We did not find statistically significant effects of the interventions compared to the baseline period. Our process evaluation indicated that many participants preferred to eat their habitual meal or were unaware of the loyalty cards.

Using Grocery Data for Credit Decisions

Many consumers across the world struggle to gain access to credit because of their lack of credit scores. This paper explores the potential of a new alternative data source, grocery transaction data, for evaluating consumers’ creditworthiness. Our analysis takes advantage of a unique, individual-level match of credit card data and supermarket loyalty card data. By developing credit scoring algorithms that either exclude or include grocery data, we illustrate both the incremental value of grocery data for credit decisions and its boundary conditions. We demonstrate that signals from grocery data can improve credit approval decisions, particularly for individuals who lack traditional credit scores. Furthermore, as a consumer establishes a relationship with lenders and builds a credit history, the marginal value of incorporating grocery data diminishes. These findings highlight the potential of grocery data in informing credit decisions and, consequently, in enabling financial institutions to extend credit to consumers who lack traditional credit scores. This paper was accepted by David Simchi-Levi, marketing. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02364 .

The effects of nudging and pricing strategies on the availability and purchases of ultra-processed foods: A secondary analysis of the Supreme Nudge trial

Regular consumption of ultra-processed foods (UPF) is a risk factor for morbidity and mortality. UPF are widely available in supermarkets. Nudging and pricing strategies are promising strategies to promote healthier supermarket purchases and may reduce UPF purchases. We investigated whether supermarket nudging and pricing strategies targeting healthy foods, but not specifically discouraging UPF, would change UPF availability, price, promotion and placement (UPF-APPP) in supermarkets and customer UPF purchases. We used data from the Supreme Nudge parallel cluster-randomized controlled trial, testing the effect of a combined nudging and pricing intervention promoting healthy products. The Dutch Consumer Food Environment Score (D-CFES) was used to audit 12 participating supermarkets in terms of UPF-APPP. We used customer loyalty card data of the first twelve intervention weeks from 321 participants to calculate the proportion of UPF purchases. Descriptive statistics were used to assess differences in D-CFES between supermarkets. Mixed model analyses were used to assess the association between the D-CFES and UPF purchases and the effect of the intervention on UPF purchases. No difference in the D-CFES between intervention and control supermarkets were found. No statistically significant association between the D-CFES and UPF purchases (β = −0.00, 95%CI: −0.02, 0.01) and no significant effect of the intervention on UPF purchases (β = 0.02, 95%CI: −0.07, 0.12) was observed. Given the significant proportion of unhealthy and UPF products in Dutch supermarkets, nudging and pricing strategies aimed at promoting healthy food purchases are not sufficient for reducing UPF–APPP nor purchases, and nationwide regulation may be needed.Trial registration number: Dutch Trial Register ID NL7064, May 30, 2018, https://trialsearch.who.int/Trial2.aspx?TrialID=NTR7302.

CheckOut: User-Controlled Anonymization for Customer Loyalty Programs

To resist the regimes of ubiquitous surveillance imposed upon us in every facet of modern life, we need technological tools that subvert surveillance systems. Unfortunately, while cryptographic tools frequently demonstrate how we can construct systems that safeguard user privacy, there is limited motivation for corporate entities engaged in surveillance to adopt these tools, as they often clash with profit incentives. This paper demonstrates how, in one particular aspect of everyday life -- customer loyalty programs -- users can subvert surveillance and attain anonymity, without necessitating any cooperation or modification in the behavior of their surveillors. We present the CheckOut system, which allows users to coordinate large anonymity sets of shoppers to hide the identity and purchasing habits of each particular user in the crowd. CheckOut scales up and systematizes past efforts to subvert loyalty surveillance, which have been primarily ad-hoc and manual affairs where customers physically swap loyalty cards to mask their real identities. CheckOut allows increased scale while ensuring that the necessary computing infrastructure does not itself become a new centralized point of privacy failure. Of particular importance to our scheme is a protocol for loyalty programs that offer reward points, where we demonstrate how CheckOut can assist users in paying each other back for loyalty points accrued while using each others' loyalty accounts. We present two different mechanisms to facilitate redistributing rewards points, offering trade-offs in functionality, performance, and security.

Effects of discounting fruits, vegetables, and noncaloric beverages in New York City supermarkets on purchasing, intake, and weight.

The objective of this study was to assess purchasing, intake, and weight after discounting fruits and vegetables (F&V) and noncaloric beverages in New York City supermarkets. A 16-week randomized controlled trial was conducted with a 4-week baseline, an 8-week intervention with 50% discounts on F&V and noncaloric beverages, and a 4-week follow-up. Purchasing was tracked via loyalty cards, and intake was tracked via 24-h dietary recalls. Weights were measured at five in-person visits. Data from 67 participants were analyzed (38 in the experimental group; 29 in the control group). F&V and noncaloric beverage weekly purchasing was greater in the experimental than the control group (mean difference [SD], $4.64 [$1.44], p < 0.0001; $0.53 [$0.39], p = 0.008) during intervention, with F&V purchasing remaining greater in the experimental versus control group during follow-up (p = 0.005). F&V intake was greater for the experimental than the control group during intervention (142 [105] g/day; p = 0.009) and follow-up (p = 0.001). Although no difference in noncaloric beverage consumption was observed between groups, there was lower alcohol intake in the experimental than the control group during follow-up (-85.8 [60.2] g/day; p = 0.004). The experimental group lost weight compared with the control group (-1.33 [0.92] kg; p = 0.006 intervention and p = 0.106 follow-up). No differences in nutrient composition or high energy-dense product consumption were found. A 50% discount on F&V and noncaloric beverages promoted increased purchasing and intake of F&V and induced weight loss.

Who donates food purchase data for research? Results from two nationwide data collection efforts in Finland

Introduction &amp; BackgroundFood retailers’ transaction data are increasingly used for research. Unlike many other digital footprints, the representativeness of automatically accumulating food purchase data as such is less biased as food is consumed by all individuals. However, the process of obtaining individual/household level data requires consents from the consumers and, thus, may create selection bias. Objectives &amp; ApproachThe aim of this work is to describe the recruitment process and participant characteristics in the Finnish LoCard study to evaluate the selection mechanism. The Finnish LoCard study comprises two cohorts collected in 2018 and 2023 with 47,066 and 42,340 participants, respectively, who have consented to release their food purchase data for research. The study collaborates with S group, the leading retailer in Finland with 47% market share. Members from their loyalty card program were invited to consent to their purchase data being used for research and voluntarily respond to a background questionnaire. Relevance to Digital FootprintsOur analyses may provide further insights into the selectivity of consumers who are willing to share their purchase data for research purposes. ResultsFor both LoCard cohorts, all loyalty card holders (n~2.3M) were considered. In LoCard I, the invitations were sent to 1.1M primary card holders with confirmed email addresses, of whom 47,066 (3.9%) participated and consented to their purchase data being used for research. In this cohort, women, middle-aged individuals, individuals with higher education, and employed individuals were overrepresented whereas the retired individuals, those with lower education and individuals with children were underrepresented. In the LoCard II, loyalty card holders (n ~2.24M) with an email address were invited. Of these, 852,009 (37.7%) opened the invitation link, and a further 42,340 provided the consent, resulting in response rate of 1.9% from the original population and 4.9% from those reacting to the email invitation. Data on the characteristics of LoCard II participants are not available yet but will be presented in the conference presentation. Conclusions &amp; ImplicationsThis work investigates selection mechanisms in the Finnish LoCard study and evaluates the feasibility of reaching underrepresented groups in health studies, such as socioeconomically disadvantaged groups or young men, through a combination of loyalty card program and email invitations.

Predicting Healthy Start Scheme Uptake using Deprivation and Food Insecurity Measures.

Introduction &amp; BackgroundThe level of food insecurity in England is widening, with low-income families requiring more support to reduce income inequalities. The government have introduced policies to address these issues with targeted subsidies on healthy food on programs such as the Healthy Start Scheme. Despite this, national uptake of the Healthy Start Scheme remains lower than the government target. Objectives &amp; ApproachOur study aims to predict uptake and take up discrepancies at a local authority level and understand the measures contributing to the prediction using anonymised supermarket loyalty card data records for over 4 million customers, deprivation and food insecurity measures. We used a machine-learning approach utilising transactional data, ONS Index of Deprivation datasets, neighbourhood statistics, and NHS Healthy Start Scheme uptake data. Regression prediction models were used to evaluate and predict the outcomes, whilst feature importance tools were used to evaluate the variables weighing within the model. Relevance to Digital FootprintsThis study leverages transaction data from a UK retailer to understand lifestyle factors at a local authority level and assesses their usefulness in predicting the scheme’s uptake. Loyalty card transactional data can provide valuable insight into purchase behaviour linked to health and nutrition. ResultsThe Linear and Ridge Regression models performed better than other prediction models. Analysis of measures revealed that whilst deprivation and population-related measures had a high contribution to the prediction model, findings from transactional data measures provided valuable insight into shopping behavioural characteristics that contribute to the model performance. Results suggested that areas with higher spending on fruits and vegetables and high-calorie food were associated with higher uptake prediction in test data but the converse for high spend on fish. Conclusions &amp; ImplicationsOur study indicates that shopping data measures such as spend on fruits and vegetables, high-calorie food, fish and products bought can be utilised for prediction models for uptake and take-up discrepancy of the Healthy Start Scheme. This study highlights the complexity of understanding factors influencing public policy effectiveness and the need for tailored approaches in diverse urban contexts.

Understanding moderators of consent regarding the sharing of supermarket shopping data in ALSPAC.

Introduction &amp; BackgroundShopping data is a valuable resource, offering insights into consumer behaviour and health. In recent years, there has been a growing interest in using shopping data for the purposes of health research. However, little is known about the characteristics of those who are willing to share their shopping data with researchers. Objectives &amp; ApproachThis study aims to investigate the factors that influence individuals' decisions to consent to sharing their shopping data for research purposes. We will leverage data from a cohort study – the Avon Longitudinal Study of Parents and Children (ALSPAC) – to address this question. We will draw upon the responses of a 2018 survey, which asked ALSPAC participants about their use of supermarket loyalty cards, their perceived acceptability of sharing this data with ALSPAC, and their perspectives on potential privacy concerns. Of the 4,462 respondents, 65.4% indicated ownership of at least one major UK supermarket or store loyalty card. Among these, 88.4% expressed a potential willingness to share this data with ALSPAC for research purposes. In the present day – around 2023 – participants have explicitly either granted or withheld consent for the sharing of this data. Our analysis approach will consider factors such as biological gender, ethnicity, education, employment, socioeconomic status, and anxiety as potential moderators to the consent process. We plan to employ a mix of standard statistical methods to analyse sampling biases in the dataset, including regression modelling and correlation tests. Relevance to Digital FootprintsOur study will contribute to the growing body of literature on data linkage between cohort studies and digital footprints datasets. ResultsThe findings from this study will offer valuable insights into the factors influencing participants' consent decisions regarding data sharing. We will contribute to ongoing discussions about privacy and the ethical use of digital footprints data, informing the development of more inclusive consent processes in this field. Conclusions &amp; ImplicationsThis study will inform consent practices in the realm of digital footprints, identifying and addressing potential barriers to consent, and contributing to the ongoing discourse on the responsible and ethical use of shopping data.

The role of inequalities in managing symptoms of menstruation: harnessing shopping data to innovate female reproductive health research

Introduction &amp; BackgroundMenstruation affects half the population, yet its patterns and management are greatly under-researched. A regular and functioning menstrual cycle is considered an important vital sign and menstrual-related issues can be strong indicators of both reproductive and wider health issues. This project explores a novel health data source - shopping data history - to study how individuals manage menstrual symptoms such as pain, intensity of flow, mental health, and other issues, and explore potential social inequalities in these management strategies. Objectives &amp; ApproachWe present a conceptual framework of studying management of menstruation symptoms using shopping data. The core objectives of this research are to enhance our understanding of menstrual management strategies and potential inequalities in these whilst evaluating the utility and acceptability of shopping data for future female reproductive health research. Our research will focus on harnessing loyalty card data from UK supermarkets and pharmaceutical retailers to provide insights into the management of menstrual symptoms at a national level. We will study retail data to identify products and patterns of purchasing which may be relevant to menstrual management and conduct surveys for linkage with shopping data. The public will be consulted to investigate attitudes towards shopping data for health research and inform interpretations of patterns in the data. Relevance to Digital FootprintsThis project contributes to advancing of understanding of using digital data for health research on an important societal challenge. We investigate the practical applications for menstrual health and other female reproductive health issues, with scope to enact meaningful change. Conclusions &amp; ImplicationsBy analysing shopping behaviour, combined with survey data and area-level socioeconomic data, we aim to identify regions of the UK and individual characteristics which influence the risk of experiencing menstrual symptoms and ability to manage these within a high-income context. Our research will contribute to understanding of menstrual management strategies for women and people who menstruate, and associated inequalities.

Studying Health and Illness Experience using Linked Data (SHIELD): Empowering customers to donate shopping data for chronic pain research

Introduction &amp; BackgroundChronic pain is considered a priority in healthcare and a threat to well-being across the globe, it is thus crucial to accurately measure the national levels of pain conditions and their impacts on workplace productivity and well-being. Chronic pain has traditionally been studied in isolation with either self-reported survey data or standalone shopping records. The former are limited in scale and can be marred by response biases, while the latter lack ‘ground truths’: what research teams can measure are usually the purchase patterns of pain relief products, but neither the severity nor types of pain conditions. Objectives &amp; ApproachData donation tools offer a novel approach to study chronic pain by linking the two aspects and establish statistical relationships between medicine consumptions and the multiple facets of pain experience. In a survey, we asked participants (N = 953) to share their loyalty card data with us, which is made possible with the data portability tool provided by Tesco (i.e., the largest supermarket chain in the United Kingdom) as part of the General Data Protection Regulation (GDPR). Based on questions adopted from popular inventories used in health research (e.g., EQ5D Health States, ONS4 Well-being, WEMWBS scales), we also asked participants to report the details of their pain conditions, hours of employment, and both general and mental health states. This allowed us to associate chronic pain - both subjective and objective (i.e., reflected by medicine consumption) - with its economic and personal consequences. Data collection was conducted via research panel providers, thus should approximate national representativeness. Relevance to Digital FootprintsThis work links digital footprints data donated by individuals to self-reported survey data, also develops an infrastructure for these data to be collected and safely stored. Conclusions &amp; ImplicationsOne key value of this project is to pioneer a measure of chronic pain that can be applied to transactional records that are much bigger in scale in future analytic works. Our research team has access to an array of different digital footprints data, including longitudinal transactional data provided by a major pharmacy chain (~20 million customers and ~429 million baskets). In order to utilise these data to associate them with regional workplace productivity measures and well-being data released by the Office for National Statistics, a metric must be defined to extract the prevalence of chronic pain from shopping data, which is informed by the patterns found by the data donation project.

Supporting young women’s health through girl-friendly drug vendors in Lake Zone, Tanzania: protocol for the AmbassADDOrs for Health cluster-randomised controlled trial

IntroductionAdverse sexual and reproductive health (SRH) outcomes, such as unplanned pregnancies and HIV infection, disproportionately affect adolescent girls and young women (AGYW; aged 15–24 years) in east Africa. Increasing uptake...

Building nutritionally meaningful classification for grocery product groups: the LoCard Food Classification process.

Analysing customer loyalty card data is a novel method for assessing nutritional quality and changes in a population's food consumption. However, prior to its use, the thousands of grocery products available in stores must be reclassified from the retailer's original hierarchical structure into a structure that is suitable for the use of nutrition and health research. We created LoCard Food Classification (LCFC) and examined how it reflects the nutritional quality of the grocery product groups. Nutritional quality was considered the main criterion guiding the reclassification of the 3574 grocery product groups. Information on the main ingredient of the product group, purpose of use and carbon footprint was also used at the more granular levels of LCFC. The main challenge in the reclassification was a lack of detailed information on the type of products included in each group, and some of the groups included products that have opposite health effects. The final LCFC has four hierarchical levels, and it is openly available online. After reclassification, the product groups were linked with the Finnish food composition database, and the nutrient profile was assessed by calculating the Nutrient-Rich Food Index (NRFI) for each product group. sd in NRFI decreased from 0·21 of the least granular level to 0·08 of the most granular level of LCFC indicating that the most granular level of LCFC has more homogeneous nutritional quality. Studies that apply LCFC to examine loyalty card data with health and environmental outcomes are needed to further demonstrate its validity.

Tinjauan Atas Inovasi Produk Pada Cafe Dua Tiga Coworking Bogor

Currently, business competition is increasingly fierce, requiring us to always think creatively in building a business. This situation results in the level of competition in gaining and retaining consumers being very high. The business of opening a coffee shop turns out to be profitable for many parties, so it has an influence on Indonesia's creative economy. These parties include: business entrepreneurs, consumers, and workers. Business entrepreneurs get big opportunities and income. In the business world, innovation and creativity are things that need to be done for the development and success of a business. Every company must create innovations to expand into new markets and maintain their current market share. One innovation that can be developed is through product innovation.The objectives of this research are: To find out the product innovations that have been carried out by Cafe Two Three Coworking Bogor, to find out the superiority of product innovation at Cafe Two Three Coworking Bogor, to find out consumer evaluations of product innovation at Cafe Two Three Coworking Bogor.The results of this research show that the product innovations carried out at the Two Three Coworking Cafe in Bogor include food products and beverage products. In pizza products, two or three coworking cafés in Bogor have innovated by making pizza volcanoes. Meanwhile, the beverage products that have been innovated by cafe two three coworking Bogor are Baileys Coffee, Es Kopi Susu Pandan and Es Kopi Susu Moccacino. Meanwhile, the superiority of the food product, the superiority of the pizza food product, is of course in terms of the wider serving size and the taste of the volcano pizza which shows more of the taste of cafe two three coworking Bogor style pizza, and apart from that the superiority of the coffee drink product at the two three coworking cafe Bogor, every purchasing coffee will get a loyalty card. Meanwhile, the evaluation carried out by cafe two three coworking Bogor carried out the latest innovations in every beverage and food product.The conclusion of this research is, the innovation carried out by cafe two three coworking Bogor on pizza products, pizza meat lover food products before innovation and after innovation became pizza volcano. Meanwhile, the beverage products are iced milk coffee which has not yet been innovated by cafe two three, and which after being innovated has become bailyes coffee, pandan milk iced coffee and moccacino milk iced coffee. Meanwhile, the advantage of the beverage product is the superiority of the coffee drink product at Cafe Two Three Coworking Bogor, every time you purchase coffee you will get a loyalty card. And the advantages of the pizza volcano food product are: For the advantages of the pizza food product, of course, in terms of the wider serving size and the taste of the volcano pizza, it shows more of the taste of two three coworking cafe style pizza in Bogor because the taste is prepared directly from the chef. Meanwhile, the evaluation carried out by Cafe Two Three Coworking in Bogor involves making innovations in each food and beverage product in order to increase sales and win the coffee shop competition in the city of Bogor.

The dynamics in food selection stemming from price awareness and perceived income adequacy: a cross-sectional study using 1-year loyalty card data

BackgroundHigher cost of healthy foods may explain unhealthy dietary patterns in lower-income households. Unfortunately, combining food selection and nutrient intake data to price and expenditure is challenging. Food retailer’s customer loyalty card data, linked to nutrient composition database, is a novel method for simultaneous exploration of food purchases, price, and nutrition. ObjectivesWe studied the associations between perceived income adequacy (PIA) as a grouping variable with price (per kilogram or megajoule) and the volume of purchases (percentage of expenditure or energy) simultaneously as outcome variables for 17 most purchased food groups. MethodsWe used 1-year (2018) loyalty card data from the largest grocery chain in Finland. Participants were 28,783 loyalty cardholders who made ≥41% of food purchases from the retailer and answered an online questionnaire at the midpoint of data collection. The 5-level PIA described the perceived financial situation in the household. Energy and nutrient content of foods purchased were from the Finnish Food Composition Database Fineli. We calculated the Nutrient Rich Food Index per 100 g food using 11 nutrients. Trends in prices and expenditures between PIA levels were analyzed using 2-sided Jonckheere–Terpstra tests, with false discovery rate control (Benjamini–Hochberg method) and confounder adjustments (inverse probability weighting). ResultsLower PIA participants selected cheaper foods per kilogram and megajoule within most food groups. They also favored unhealthy food groups cheap in energy [<1 € (USD 1.18)/MJ]. Despite lower purchase price, the expenditure (%) among lower PIA was higher on alcohol, snacks, sugar-sweetened beverages, and sweets and chocolates. ConclusionsParticipants with lower PIA showed stronger price awareness. It is crucial to consider the pricing of competing alternative food groups, when steering toward environmentally sustainable and healthier food purchases. Package labeling might also direct the selection of healthier choices among the less expensive items within a food group.

Using loyalty card data to understand the impact of weather on click & collect behaviours in UK retailing

ABSTRACT This study assesses the influence of weather on Click & Collect (C&C) usage in England, examining differences between the day of order and day of collection, and how the weather influences the time taken to collect an order. Using sales and loyalty card data from a national retailer, we employ a series of gradient boosting models to analyse the influence of weather on daily order/collection totals, totals stratified by demographic group, and the time taken to collect an order. Weather is found to be consistently more influential on the day of order than the day of collection, however store location and thetiming of purchase have a higher relative influence than weather overall. The findings also evidence significant differences in influence between male and female customers and those aged above and below 65. Temperature and humidity are typically the most influential weather conditions, demonstrating a wide variation in effect on order totals. Temperature was also found to be significantly more influential than other weather conditions on the time taken to collect an order, with the highest temperatures leading to the longest collection times. Understanding the influences of different weather conditions on online consumption behaviours is of significant value to retailers, policy makers and associated stakeholders. However, it is an area that has been significantly under-researched compared to physical retailing, often due to a lack of reliable data. This study provides key insights into how different types of consumers behave on these platforms.

Tensorial Principal Component Analysis in Detecting Temporal Trajectories of Purchase Patterns in Loyalty Card Data: Retrospective Cohort Study.

Loyalty card data automatically collected by retailers provide an excellent source for evaluating health-related purchase behavior of customers. The data comprise information on every grocery purchase, including expenditures on product groups and the time of purchase for each customer. Such data where customers have an expenditure value for every product group for each time can be formulated as 3D tensorial data. This study aimed to use the modern tensorial principal component analysis (PCA) method to uncover the characteristics of health-related purchase patterns from loyalty card data. Another aim was to identify card holders with distinct purchase patterns. We also considered the interpretation, advantages, and challenges of tensorial PCA compared with standard PCA. Loyalty card program members from the largest retailer in Finland were invited to participate in this study. Our LoCard data consist of the purchases of 7251 card holders who consented to the use of their data from the year 2016. The purchases were reclassified into 55 product groups and aggregated across 52 weeks. The data were then analyzed using tensorial PCA, allowing us to effectively reduce the time and product group-wise dimensions simultaneously. The augmentation method was used for selecting the suitable number of principal components for the analysis. Using tensorial PCA, we were able to systematically search for typical food purchasing patterns across time and product groups as well as detect different purchasing behaviors across groups of card holders. For example, we identified customers who purchased large amounts of meat products and separated them further into groups based on time profiles, that is, customers whose purchases of meat remained stable, increased, or decreased throughout the year or varied between seasons of the year. Using tensorial PCA, we can effectively examine customers' purchasing behavior in more detail than with traditional methods because it can handle time and product group dimensions simultaneously. When interpreting the results, both time and product dimensions must be considered. In further analyses, these time and product groups can be directly associated with additional consumer characteristics such as socioeconomic and demographic predictors of dietary patterns. In addition, they can be linked to external factors that impact grocery purchases such as inflation and unexpected pandemics. This enables us to identify what types of people have specific purchasing patterns, which can help in the development of ways in which consumers can be steered toward making healthier food choices.

The Use of Loyalty Card Promotion: Analyzing Customer Loyalty and Satisfaction of Card Holders

The increasing consumption of coffee in Indonesia makes business people try to create innovation by establishing marketing facilities that can provide satisfaction for customers. In addition, each company creates a loyalty program in the form of a member card that provides benefits and privileges in making transactions. This study aims to determine the effect of member card usage and customer satisfaction on customer loyalty. The research method used through descriptive and quantitative approaches with data collection techniques through the distribution of online questionnaires through social media was obtained by 244 respondents. The sampling technique uses nonprobability sampling with incidental methods. This research uses SMARTPLS software version 3.2.9 to conduct data processing. The results of this study show that member cards have a significant positive influence on customer satisfaction and member cards also have a significant positive influence on customer loyalty. In the end, customer satisfaction also has a significant positive influence on customer loyalty.

Ecolabels and the economic recession.

We examine the effect of the 2008 economic recession on consumers' observed expenditures for eco-labelled grocery products. Traditional price theory predicts that consumers change their spending during an economic downturn and we would expect the sales share of eco-labelled products to fall since these are relatively more expensive than non-labelled products. We use supermarket loyalty card data from the UK and show that the recession had widely different effects on the expenditure share of different eco-labelled grocery products. We confirm, empirically, that expenditure shares on organic products declined over the time period under study but the expenditures share for fair-trade products increased over the same period. We evaluate alternative models of decision making to explain our results, viz., a salience model and a model of reputation signalling. We find that both of these models give a plausible explanation of our empirical results.