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Valuing data assets presents a significant challenge in the modern business environment, even though it is widely accepted that data have significant value for commercial outcomes. Data are an intangible asset with non-rival characteristics and moreover its future uses may be highly uncertain as the market environment and technology change. In this paper, we propose one practical and flexible solution for valuing business data using well-established real option pricing theory. We discuss how to best select the appropriate proxy for each variable involved. As an illustration of our method, we present a calculation (using only publicly available data) of the initial option value of setting up a supermarket loyalty card scheme for customer data. The value resulting from the calculation is reasonably close to the recently reported realized sales figure for the supermarket data. The easy-to-use tool we propose can serve practitioners as a starting point for strategic decision-making regarding investing in their data assets, even in situations where their use cases are not fully known, and future project outcomes are uncertain. [ABSTRACT FROM AUTHOR]

The way products are placed in a supermarket can be effective in increasing sales and profit. A reasonable approach is to group together items that are likely to be purchased together. Thus, managers with the support of mining methods and techniques, can assist customers locating the products they want to buy in an easy and quick way. Many product placement strategies have been proposed over the years to leverage an effective and efficient way to achieve this goal. In this paper, association rules for product arrangement in supermarkets are studied and an algorithm based on such rules is proposed. The algorithm considers several factors, such as the number of units sold of each product, a hierarchical structure for product classification developed by the United Nations Department of Economic and Social Affairs, a set of association rules generated from sales, and a set of constraints that restrict some products to be placed physically close to each other in a supermarket, even if they are usually purchase together. Real public sales data of a supermarket were used for the experiments, where the proposed algorithm is applied for the generation of supermarket layouts. The results show that some supermarket departments may share the same products or product categories. [ABSTRACT FROM AUTHOR]

Summary: The objective of the study was to evaluate the sensory and cognitive perception of different types of consumers and contexts of sale of Mexican Persian lime using digital images and an online survey. A questionnaire in Google® Forms was responded by 293 Mexican consumers grouped into housewives, conventional consumers, and lime producers who evaluated images of Mexican Persian lime in different sale contexts: farm; producer's house; local market; supermarket and sale at packing facilities Mexican Persian lime. Sensory attributes elicited by digital images were evaluated, and emotions and memories were collected using the check‐all‐that‐apply technique. The results showed that the producers used less sensory attributes for the characterisation of the Persian lime images. Persian lime images evoked more positive emotions (free, loving, calm, adventurous, nostalgic, happy, joyful, interested, active, satisfied, and warm), positive memories (traditional food, family, nature, climate, and birthplace), and few negative emotions and memories (worried, pain, and poverty). The liking of the three groups of consumers was oriented to the contexts of sale of Mexican Persian lime in the farm and supermarket. [ABSTRACT FROM AUTHOR]

Interest in retail seafood sales increased dramatically with the onset of the COVID‐19 pandemic. The diversity of species, types of products, product forms, and packaging of the seafood sold at retail, combined with diverse consumer preferences across the United States, requires detailed data and analysis to provide guidance and understanding of emerging trends. Weekly, store‐based, Nielsen Scantrack data for the period of September 2016 through August 2021 were used to compare trends in US retail (supermarket) seafood sales across the 5‐year study period, in continental regions, cities, and species categories sold. Results showed continuous increases in retail seafood sales over the study period at an average annual growth rate of 8.1%, much of which was fueled by the 21% increase in total sales (19.5% increase in quantity sold) the first year after the onset of the pandemic. The South Atlantic region was found to have the greatest total sales and sales per capita among regions. New York City had the greatest total seafood sales, followed by Los Angeles and Philadelphia. The top five most important species categories in terms of sales were, in declining order, shrimp, salmon, tuna, crab, and tilapia, although regional variability became apparent from the fourth‐ranked species. The most important package sizes were 454‐ and 907‐g packs. Frozen and refrigerated categories dominated sales (70% in 2021), with little growth in entrées and a decline in market share of shelf‐stable seafood products in 2021. Retail supermarket seafood sales increased dramatically following the onset of the pandemic, with especially notable percentage increases in lobster (77%) and crab sales (70%) and the lowest percentage increases in tuna (1%) and tilapia (13%). Given that US per capita seafood consumption did not show a corresponding increase over the study period, study results likely indicate a shift to greater relative consumption at home and not an overall increase in US seafood sales. [ABSTRACT FROM AUTHOR]

Introduction: Biscuits are small bakery products made with wheat flour, fat, sugar and other ingredients, and they can be processed or ultraprocessed food. This paper aimed to evaluate the nutritional quality of biscuits sold in Brazilian supermarkets. Materials and methods: After authorization from the managers, two supermarkets participated in the study. The data collected from labels was the nutritional information and the list of ingredients of cookoies, through photos and notes. Data were tabulated and statistically analyzed with analysis of variance (ANOVA) with a significant level of p<0,05. Results: A total of 412 different products were included, which 73,5% are sweet and 26,5% are salty biscuits. About processing, 98,05% are ultraprocessed and only 1,95% processed. The average of ingredients was 14,80 and 84,7% of biscuits were produced with 11 or more ingredients; the maxim number of ingredients was 29. Vegetal fat was found in 79,6% of products. Sweet biscuits have higher values of sugar (p<0,0001), total fat (p=0,002) and saturated fat (p<0,0001), and salty biscuits were higher in proteins (p<0,0001) and sodium (p<0,0001). When compared with processed biscuits, the ultraprocessed ones were higher in carbohydrates (p=0,001), sugar (p<0,0001), total fat (p<0,0001) and saturated fat (p<0,0001). Conclusion: In this study, processed and salty biscuits had better nutritional quality than sweet and ultraprocessed ones, and are considered better choice options for consumers in supermarkets. [ABSTRACT FROM AUTHOR]

We present a case study and methodological developments in large-scale hierarchical dynamic modelling for personalized prediction in commerce. The context is supermarket sales, where improved forecasting of household-specific purchasing behaviour informs decisions about personalized pricing and promotions. This setting involves many thousands of heterogeneous customers and items. Models developed are fully Bayesian, interpretable and multi-scale, with hierarchical forms overlaid on the inherent structure of the retail setting. Customer behavior is modelled at several levels of aggregation, and information flows from aggregate to individual levels. Methodological innovations include extensions of Bayesian dynamic mixture models, their integration into multi-scale systems, and forecast evaluation with context-specific metrics. The use of simultaneous predictors from multiple hierarchical levels improves forecasts at the customer-item level of main interest. [ABSTRACT FROM AUTHOR]

The Rubin Causal Model (RCM) is a framework that allows to define the causal effect of an intervention as a contrast of potential outcomes. In recent years, several methods have been developed under the RCM to estimate causal effects in time series settings. None of these makes use of autoregressive integrated moving average (ARIMA) models, which are instead very common in the econometrics literature. In this paper, we propose a novel approach, named Causal-ARIMA (C-ARIMA), to define and estimate the causal effect of an intervention in observational time series settings under the RCM. We first formalise the assumptions enabling the definition, the estimation and the attribution of the effect to the intervention. We then check the validity of the proposed method with a simulation study. In the empirical application, we use C-ARIMA to assess the causal effect of a permanent price reduction on supermarket sales. The CausalArima R package provides an implementation of the proposed approach. [ABSTRACT FROM AUTHOR]

This article analyzes the temporal programming of sales for a horticultural marketing company, e.g. a cooperative. The empirical study references the European tomato market, where most of the production is sold through the retail channel dominated by large distribution chains. We study the marketing schedule for an individual company, or even a prominent farmer, using a modified Markowitz model, assuming that his decisions do not affect the balance of market prices. As a result, this model can manage risk and improve decision-making. The data also provide information on the risk borne by marketers depending on their sales calendar, which often depends on their geographic location. [ABSTRACT FROM AUTHOR]

These days, with the fast advancement of Internet innovation, purchasing through online is getting normal and even inexorable among individuals in a different age. Internet is most common stands for people to do online purchase even for grocery products. Consumers starts to prefer to purchase their requirements through online because of more convenient and beneficial concern than the traditional method of purchasing and this additionally pulls in an ever-increasing number of individuals to sell items on the web, from anchor stores to little individual businesses. Since increasing number of online suppliers, the rivalry among suppliers on the web based will be more concentrated. Most of the studies have been conducted on determining factors which is identifying the consumer intention for online purchase but they are very limited study have been examined on factors ascending people to purchase grocery items through online. The researcher aims to explore the factors like Technology, Convenience risk, trust, perceived risk, E-WOM of various consumers which influence the consumer intention to purchase grocery items from online. The questionnaire will be the primary instrument of the examination and they will be disseminated to target respondents utilizing an online review. Respondents of the examination will be chosen on snowball sampling method. After the data assortment, the Statistical Package for the Social Sciences (SPSS) will be utilized for an analysis. Generally, the result of the study is vital for retailers to recognize the significant components in expanding their consumers' expectations to purchase grocery products through online. [ABSTRACT FROM AUTHOR]

Bundling is an important strategy for online supermarkets to promote sales. A proper bundle can not only bring profits to the retailer, but also can provide convenience to consumers. However, the number of products sold by the online supermarkets are extremely large, and customers may vary at different cities. How to generate bundles from a large number of products and according to the consumption characters of different cities is a major challenge for online supermarkets. In this paper, a product bundling model considering different city levels was proposed. First, the frequent itemset were found based on the analysis of association rules. The sales contribution rate was introduced to identify the profitability of frequent itemset for different city levels, and based on which an optimizing model was formulated to select proper itemset as bundles. The results of numerical experiments show that bundles can be effectively selected across different city levels, and by bundling the income of the seller can be increased. [ABSTRACT FROM AUTHOR]

"The golden highways of the technological route serve as the ultimate metaphor for the society of artificial intelligence," explains the author of this present excursion. Unsupervised predictor is a technique that is used in all upbringings and algometry computations, and it is the foundation of all of them. The accuracy-based artificial intelligence and genetic algorithm-based prediction are becoming more important in business intelligence, with the crossover revealing the highest percentage of interest-based profit in the food industry. Our suggested approach aims to determine the utmost amplification of the food business profit with the help of food sale prediction. Hence, owners of outlet sale, supermarket sale, grocery store, and so on can maintain the stock. Therefore, our study strives to elucidate the relationship between food business intelligence and artificial intelligence in the context of soft computing. The study combines the previous data with the record of the current data and compares the existing development of business management in order to create the topic of the TOR method, which will be the artificial intelligence buzzword in the town-based food business intelligence. This paper uses artificial intelligence, business intelligence, and neural networks to forecast the food sale prediction by evaluating the data of each individual client. Consider the situation in which this technology is applied in every grocery store in the country. Our suggested model has achieved low mean squared error and low variance. Food mart owners will have a far better grasp of their clientele and will be able to categorise them in this situation, enabling them to proceed with future sales forecasting. [ABSTRACT FROM AUTHOR]

Zero‐alcohol beverages, classified as products containing ≤0.5% alcohol by volume, are becoming increasingly popular. These beverages may contribute to alcohol harm reduction efforts if drinkers switch to them from regular strength beverages. However, there is currently a lack of evidence to support a substitution hypothesis, particularly as alcohol companies frequently market zero‐alcohol beverages as drinks to consume in new locations, such as in the workplace, or by previously non‐drinking groups, such as pregnant women. Additionally, zero‐alcohol beverages are currently available for sale in supermarkets by all consumers, including minors. This is problematic as there is a lack of evidence around what impact consumption of zero‐alcohol beverages in youth might have on social norms and initiation of alcohol consumption. In this commentary, we explore the existing evidence on zero‐alcohol beverages and their impact on drinking behaviours, concluding that a current paucity of knowledge makes developing evidence‐based policy a challenge. As such, substantial research efforts are required to inform policies regulating the availability and marketing of zero‐alcohol beverages in Australia. [ABSTRACT FROM AUTHOR]

Manufacturers increasingly adopt health symbols, which translate overall product healthiness into a single symbol, to communicate about the overall healthiness of their grocery products. This study examines how the performance implications of adding a front-of-pack health symbol to a product vary across products. We study the sales impact of a government-supported health symbol program in 29 packaged categories, using over four years of scanner data. The results indicate that health symbols are most impactful when they positively disconfirm pre-existing beliefs that a product is not among the healthiest products within the category. More specifically, we find that health symbols are more effective for (i) products with a front-of-pack taste claim, (ii) lower priced products, and (iii) private label products. Furthermore, these results are more pronounced in healthier categories than in unhealthier categories. Our findings imply that health symbols can help overcome lay beliefs among consumers regarding a product's overall healthiness. As such, adding a health symbol provides easy-to-process information about product healthiness for the consumer and can increase product sales for the manufacturer. [ABSTRACT FROM AUTHOR]

Prediction of sales promotion is a significant method to attract customers and enable retailers in successful planning and forecasting. The core objectives of this paper are to introduce a novel interpretable machine language algorithm to enhance the sales in supermarkets based on the buying behaviour of the customers, and to find the factor of sensitivity that stimulates, satisfies, and builds loyalty among the customers. Decision tree, gradient boosted trees, and support vector machine learning algorithms will be mainly used to compare the accuracy level and to analyse the promotion of sales in supermarkets based on the buying behaviour of customers, and also to find factors influencing customer retention. [ABSTRACT FROM AUTHOR]

We develop and exemplify application of new classes of dynamic models for time series of nonnegative counts. Our novel univariate models combine dynamic generalized linear models for binary and conditionally Poisson time series, with dynamic random effects for over-dispersion. These models estimate dynamic regression coefficients in both binary and nonzero count components. Sequential Bayesian analysis allows fast, parallel analysis of sets of decoupled time series. New multivariate models then enable information sharing in contexts when data at a more highly aggregated level provide more incisive inferences on shared patterns such as trends and seasonality. A novel multiscale approach—one new example of the concept of decouple/recouple in time series—enables information sharing across series. This incorporates cross-series linkages while insulating parallel estimation of univariate models, and hence enables scalability in the number of series. The major motivating context is supermarket sales forecasting. Detailed examples drawn from a case study in multistep forecasting of sales of a number of related items showcase forecasting of multiple series, with discussion of forecast accuracy metrics, comparisons with existing methods, and broader questions of probabilistic forecast assessment. [ABSTRACT FROM AUTHOR]

Objective: To assess the presence of nutrition declarations and nutritional quality in pre-packaged food products sold in Guatemala.Design: We photographed nutrition labels of pre-packaged foods. We extracted information about declaration of energy, total/saturated/trans-fats, total/added sugars and Na content (critical nutrients). We classified all products according to their degree of processing (NOVA classification) and nutritional quality (PAHO and WHO-Europe nutrient profile models).Setting: Pre-packaged foods for sale in seven supermarkets in Guatemala City.Participants: This study did not involve human subjects.Results: We assessed 3459 pre-packaged foods, including 80 % ultra-processed, 7 % processed and 13 % unprocessed/minimally processed foods or culinary ingredients. Nutritional information was available in 3021 products (87·3 %). Energy content was declared in 87·0 %; total fats in 86·1 %; saturated fats in 81·5 %; trans-fats in 48·9 %; total sugars in 70·3 %; added sugars in 0·5 % and Na/salt in 85·5 % of products. Insufficient nutrient information made impossible to assess nutritional quality in 36·6 and 17·1 % of products with the PAHO and WHO-Europe models, respectively. Using PAHO and WHO nutrient profiles, we found that 66·2 and 50 % of food products did not meet the model's nutritional criteria.Conclusions: A high proportion of pre-packaged foods with nutritional information available in Guatemalan supermarkets do not meet the nutritional criteria recommended by WHO and PAHO. Furthermore, a high proportion of products did not declare critical nutrients and many did not even provide any nutritional information. National regulations should consider making critical nutrient declarations (including trans-fats and sugars) mandatory for all products. [ABSTRACT FROM AUTHOR]

Competition among different brands in supermarkets motivates consumer goods companies to develop and implement and activate merchandising materials at the point of sales (POS). These merchandising materials attempt to influence shoppers and promote sales. On the other hand, behavioral economics studies the decision-making of individuals based on their preferences and beliefs, influenced by cognitive bias. This study attempts to understand the effect of loss aversion and mental accounting in POS materials on supermarket sales. We conduct two experiments with a consumer brand in a supermarket chain. The results reveal a positive and significant effect on sales only for the cognitive bias of loss aversion, applied to the POS materials. [ABSTRACT FROM AUTHOR]

Objective: Ready‐to‐eat (RTE) meat is a kind of popular instant food easily contaminated by microbes, which is one of the causes of foodborne diseases. This study analyzes the possible sources of RTE food bacterial contamination during processing and subsequent selling. Method: Samples of eight kinds of RTE meat were collected from four supermarkets in Nanjing, China. The knives, chopping boards, trays(containers of food), clamps, air, water, and hands of the sales staff were sampled, and the enumeration of aerobic plate count and total coliforms and pathogenic bacteria was performed. Results: The survey revealed that poor hygienic levels was the causes that RTE meat products were contaminated by bacteria at different levels. With regard to pathogen, the incidences of Salmonella spp. and Staphylococcus aureus were 4.2% and 2.1%, respectively. These results also revealed that the bacterial contamination of RTE food was caused by the air, as well as clamps, chopping boards, knives, trays, and hands of the operators. The total number of aerobic colonies were positively correlated with the amount of RTE food in one pot (r =.87728, p =.0217), and negatively correlated with the maximum temperature in the center of the meat (r = −.81633, p =.0475). Conclusion: The high number of bacteria in RTE foods indicates potential food safety risks and the need to improve the health of supermarket sales staff. The most important thing is to determine how to raise hygiene awareness of employees through food safety education. Meanwhile, a comprehensive set of regulations on hand cleaning and disinfection should be developed to facilitate public health and reduce foodborne illness caused by the consumption of RTE food. [ABSTRACT FROM AUTHOR]

Objectives. To examine the prevalence and magnitude of price promotions in a major Australian supermarket and how they differ between core (healthy) and discretionary (less healthy) food categories. Methods. Weekly online price data (regular retail price, discount price, and promotion type) on 1579 foods were collected for 1 year (April 2017 to April 2018) from the largest Australian supermarket chain. Products audited were classified according to Australian Dietary Guidelines definitions of core and discretionary foods and according to their Health Star Rating (a government-endorsed nutrient profiling scheme). Results. On average, 15.1% (95% confidence interval [CI] = 14.7%, 15.3%) of core foods and 28.8% (95% CI = 28.6%, 29.0%) of discretionary foods were price promoted during a given week. Average discounts were −15.4% (95% CI = −16.4, −14.4) for core products and −25.9% (95% CI = −26.8, −25.1) for discretionary products. The percentage of products on price promotion and the size of the discount were larger for products with a lower Health Star Rating (P <.05). Conclusions. Price promotions were more prevalent and greater in magnitude for discretionary foods than for core foods. Policies to reduce the prevalence and magnitude of price promotions on discretionary foods could improve the healthiness of food purchased from supermarkets. [ABSTRACT FROM AUTHOR]

New goods and expanding product variety are thought to provide enormous welfare gains. New products can influence the pricing of competing products, but often the most important way that new products improve the welfare is through their direct consumption value. The demographic profile of the buyers of new goods suggests those welfare gains are unequally distributed. For supermarket products in the US, expenditures on new goods are disproportionately concentrated among high earners and younger consumers. [ABSTRACT FROM AUTHOR]

This study collected the annual revenue data of the top three supermarket chains in Taiwan (PX Mart, Simple Mart, and Wellcome), shopping frequency in supermarkets, and the average expenditure of the general public in order to construct models for simulating supermarket revenue by calibrating model parameters to the actual data. This study employed the competing destinations model (CDM) to designate supermarket shoppers' shopping areas and simulate supermarket sales. The CDM calculated the probability of consumers selecting each supermarket, simulated the total revenues of supermarkets and the average daily revenue of a single store using the expenditure equation, and added spatial competition and agglomeration effect parameters to the model to observe variations in revenue with changing parameter values. The study results revealed profitable expansion opportunities and highly competitive locations not advised for new outlets. Beitou district had the lowest population density in Taipei City yet a high-sales cluster, making it an area suitable for new PX Mart stores. Shilin and Xinyi districts could also be considered suitable areas for new stores because they had insignificant distributions of stores, high-sales clusters, and competition was not yet severe. The study extends the use of analytical revenue and spatial models to empirically study the agglomeration forces between supermarket chains, analyzes the influence of distance variation on supermarket revenues, and determines the most suitable locations for the future inauguration of supermarket stores in Taipei City. • This study collected the annual revenue data of the top three supermarket chains in Taiwan. • The agglomeration forces between supermarket chains were investigated. • This study constructed models for simulating supermarket revenue by calibrating parameters to the actual data. • The study analyzed the influence of distance variation on supermarket revenues. • The study results revealed profitable expansion opportunities. [ABSTRACT FROM AUTHOR]

Object detection and recognition are challenging computer vision tasks receiving great attention due to the large number of applications. This work focuses on the detection/recognition of products in supermarket shelves; this framework has a number of practical applications such as providing additional product/price information to the user or guiding visually impaired customers during shopping. The automatic creation of planograms (i.e., actual layout of products on shelves) is also useful for commercial analysis and management of large stores. Although in many object detection/recognition contexts it can be assumed that training images are representative of the real operational conditions, in our scenario such assumption is not realistic because the only training images available are acquired in well-controlled conditions. This gap between the training and test data makes the object detection and recognition tasks far more complex and requires very robust techniques. In this paper we prove that good results can be obtained by exploiting color and texture information in a multi-stage process: pre-selection, fine-selection and post processing. For fine-selection we compared a classical Bag of Words technique with a more recent Deep Neural Networks approach and found interesting outcomes. Extensive experiments on datasets of varying complexity are discussed to highlight the main issues characterizing this problem, and to guide toward the practical development of a real application. [ABSTRACT FROM AUTHOR]