Examining retail purchases of cigarettes and nicotine
replacement therapy in Finland

Timberlake, David S.; Joensuu, Johanna; Kurko, Terhi; Rimpelä, Arja; Nevalainen, Jaakko

doi:10.18332/tid/108537

Cited by 6 publications

(3 citation statements)

References 19 publications

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“…Customer data that are complemented with appropriate information can provide unique empirical access to study many contemporary challenges, such as obesity, malnutrition, or unsustainable diets. Furthermore, using customer data for research purposes can uncover new opportunities––for example, studying health (e.g., Aiello et al, 2019; Nevalainen et al, 2018), alcohol consumption (Lintonen et al, 2020), sustainable food purchasing (Erkkola et al, 2022; Meinilä et al, 2022), and nicotine replacements (Timberlake et al, 2019)––and thus, direct attention towards using customer data to benefit individuals and society at large (Hermann, 2022; Saarijärvi et al, 2019). While there seems to be increasing interest in examining tensions revolving around collecting, analysing, and leveraging customer data in retailing (see Krafft et al, 2021; Martin & Palmatier, 2020; Wieringa et al, 2021), harnessing data's potential to benefit society through scientific research can increase customers' willingness to continue to share their data.…”

Section: Discussionmentioning

confidence: 99%

Examining consumer food waste through grocery retailers' customer data: Segments and practical implications

Närvänen

Mesiranta

Saarijärvi

et al. 2023

Int J Consumer Studies

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Grocery retailers have a large amount of societally relevant data on food purchases in their databases, which they have traditionally used merely for marketing purposes.However, despite the potential, the data have so far been rarely used in sustainability-related research. This study addresses a key sustainability challengethe reduction of food waste-by using retailers' customer data and a questionnaire. This paper's purpose is to identify and analyse household food waste segments and discuss their actual purchasing behaviour patterns. By doing so, we also illustrate and exemplify the potential use of customer loyalty card data to address global challenges related to food at the consumer level. The study utilizes an extensive data set of food purchases together with a survey of self-reported reasons for food waste in households. Utilizing cluster analysis, Phase 1 identifies six household segments: no food waste; trust in date labels; safety first; occasional wasters; overpurchasers and overpreparers; and family first. These segments differ in their sociodemographic and purchase profiles. In Phase 2, these segments' purchasing behaviour is examined further by applying tree-based methods. This study contributes to food waste research and studies on sustainable retailing. It also has practical implications for how retailers can facilitate household food waste reduction.

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Section: Discussionmentioning

confidence: 99%

Examining consumer food waste through grocery retailers' customer data: Segments and practical implications

Närvänen

Mesiranta

Saarijärvi

et al. 2023

Int J Consumer Studies

Self Cite

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“…The level of details in product groups (eg, amount and price) and time within loyalty card data make it an attractive source of information in, for instance, public health research [2]. Loyalty card data provide new insights into health-related purchase behavior [6][7][8][9][10]. Food, tobacco, and alcohol are well-known risk factors for many chronic diseases; however, their consumption is difficult to measure using traditional methods [11,12].…”

Section: Introductionmentioning

confidence: 99%

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

Autio,

Virta,

Nordhausen

et al. 2023

J Med Internet Res

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Background 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. Objective 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. Methods 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. Results 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. Conclusions 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.

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“…Individual level shopping data can now be shared with research organisations by individuals requesting access to the data collected about them. This unprecedented access path is transformative given the promise this data holds for population health research [22][23][24][25][26][27] In this paper we explore how individual-level shopping data can contribute to health research, discuss their potential for understanding the interplay between health and consumption patterns, and suggest ways these could be best utilised by overcoming potential biases in the data.…”

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confidence: 99%

Overcoming biases of individual level shopping history data in health research

Skatova

2023

Preprint

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Novel sources of population data, especially administrative and medical records, as well as the digital footprints generated through interactions with online services, present a considerable opportunity for advancing health research and policymaking. An illustrative example is shopping history records that can illuminate aspects of population health by scrutinizing extensive sets of everyday choices made in the real world. However, like any dataset, these sources possess specific limitations, including sampling biases, validity issues, and measurement errors. To enhance the applicability and potential of shopping data in health research, we advocate for the integration of individual-level shopping data with external datasets containing rich repositories of longitudinal population cohort studies. This strategic approach holds the promise of devising innovative methodologies to address inherent data limitations and biases. By meticulously documenting biases, establishing validated associations, and discerning patterns within these amalgamated records, researchers can extrapolate their findings to encompass population-wide datasets derived from national supermarket chain. The validation and linkage of population health data with real-world choices pertaining to food, beverages, and over-the-counter medications, such as pain relief, present a significant opportunity to comprehend the impact of these choices and behavioural patterns associated with them on public health.

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Examining retail purchases of cigarettes and nicotine replacement therapy in Finland

Cited by 6 publications

References 19 publications

Examining consumer food waste through grocery retailers' customer data: Segments and practical implications

Examining consumer food waste through grocery retailers' customer data: Segments and practical implications

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

Overcoming biases of individual level shopping history data in health research

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