How COVID-19 Has Influenced the Purchase Patterns of Young Adults in Developed and Developing Economies: Factor Analysis of Shopping Behavior Roots

The paper presents survey results from shopping behavior transformation in developed and developing countries due to the COVID-19 pandemic outbreak in spring 2020. The survey includes the polling process that covered 515 and 117 young adults, respectively, for two economies and factor analysis to determine the latent intentions of purchase behavior. Shopping patterns were studied for food, medicine, goods of first priority, electronics, clothing, and shoes. According to factor analysis results, we determined nine factors that reveal some similarities in shopping behavior as pro-safe purchases and belt-tightening patterns for both economies. Along with that, we revealed that people from developed countries perceived the greater danger and fear due to the COVID-19 crisis than young adults from developing economy. Based on polling results, the post–COVID-19 shopping channel choice behavior was evaluated for developed and developing economies.

Sequential Movie Genre Prediction Using Average Transition Probability with Clustering

In recent movie recommendations, one of the most important issues is to predict the user’s sequential behavior to be able to suggest the next movie to watch. However, capturing such sequential behavior is not easy because each user’s short-term or long-term behavior must be taken into account. For this reason, many research results show that the performance of recommending a specific movie is not good in a sequential recommendation. In this paper, we propose a cluster-based method for classifying users with similar movie purchase patterns and a movie genre prediction algorithm rather than the movie itself considering their short-term and long-term behaviors. The movie genre prediction does not recommend a specific movie, but it predicts the genre for the next movie to watch in consideration of each user’s preference for the movie genre based on the genre included in the movie. Using this, it will be possible to provide appropriate guidelines for recommending movies including the genres to users who tend to prefer a specific genre. In particular, in this study, users with similar genre preferences are organized into clusters to recommend genres. For clusters that do not have relatively specific tendencies, genre prediction is performed by appropriately trimming genres that are not necessary for recommendation in order to improve performance. We evaluate our method on well-known movie data sets and qualitatively determine that it captures personalized dynamics and is able to make meaningful recommendations.

HIV Pre-Exposure Prophylaxis (PrEP) purchase patterns and STI occurrence among Israeli men: A cohort analysis

Background HIV Pre-exposure prophylaxis (PrEP) is the regular use of antiretroviral medication by people who are not infected with HIV to prevent seroconversion. Israel approved PrEP for continuous use in 2017, and Israeli Health Maintenance Organizations (HMO) offered PrEP with a copayment to eligible members. Methodology This retrospective cohort study included all people who were dispensed PrEP between September 2017 to June 2019 in the second largest HMO in Israel. Statistical analysis, including Kaplan Meier, was conducted to evaluate user PrEP purchase, adherence to medical follow-up, and clinical outcomes. Results In total, a cohort of 757 PrEP users were followed for 657.8 person-years. All but one user were male; median age was 35 years. At baseline, 0.8% had gonorrhea and 1.5% had chlamydia infections and 4.4% had recent syphilis infection. Continuous use of PrEP (without interruption/discontinuation) was observed in 29.9%, while 39.9% interrupted and 30.3% discontinued use. Median time to first interruption/discontinuation was 4.0 months. At 6–12 months after initiation, 79.8% of users had a documented HIV test, 77.3% a Chlamydia-Gonorrhea panel, and 78.9% a creatinine test. There was one new case of HIV among the cohort, five months after PrEP discontinuation. Estimated first-year infection rates were 5.0%, 8.6% and 6.8% for gonorrhea, chlamydia and first-time syphilis, respectively. Conclusions This study shows heterogeneous PrEP purchase patterns and required medical follow-up, and an increase in STIs among consistent PrEP users. Improving adherence to recommended medical follow-up during PrEP use is essential in PrEP’s integration into Israel’s national HIV prevention strategy.

Clustering Algorithm For Determining Marketing Targets Based Customer Purchase Patterns And Behaviors

Customer segmentation is one of the most important applications in the business world, specifically for marketing analysis, but since the Corona Virus (Covid-19) spread in Indonesia it has had a significant impact on the level of digital shopping activities because people prefer to buy their needs online, so It is very important to predict customer behavior in marketing strategy. In this study, the K-Means Clustering technique is proposed on the RFM (Recency, Frequency, Monetary) model for segmenting potential customers. The proposed model starts from the data cleaning stage, exploratory analysis to understand the data and finally applies K-Means Clustering to the RFM Model which produces three clusters based on the Elbow model. In cluster 0 there are 2,436 customers, in cluster1 1,880 and finally in cluster2 there are 18 customers. RFM analysis can segment customers into homogeneous groups quickly with a minimum set of variables. Good analysis can increase the effectiveness and efficiency of marketing plans, thereby increasing profitability with minimum costs.

Changes in Consumers’ Purchase Patterns as a Consequence of the COVID-19 Pandemic

The current COVID-19 pandemic has affected every aspect of consumer behavior—their expenses, investments, and financial reserves, as well as their financial and social wellbeing. As a consequence of different restrictions, consumers and their shopping patterns have changed significantly; thus, the factors that influence new purchase patterns need to be identified to help traders, retailers, and marketers develop appropriate strategies to respond to crucial consumer changes in the market. A categorical analysis (Pearson’s chi-square test) and correspondence analysis (simple and multivariate) were applied to a sample of 425 Slovak respondents to reveal the most important factors impacting consumers’ financial situations, as well as the effects on the maintenance of new shopping habits established during the pandemic period. The results revealed that consumers´ income, age, and sector of occupation play important roles in the context of new shopping patterns. These findings are in agreement with other global studies, confirming both the worldwide impact of the pandemic on consumer behavior and the importance of national studies on consumer shopping behavior in order for state authorities, traders, marketers, and entrepreneurs to be able to take necessary measures.

Effectiveness of Visual Merchandising on Consumer Buying Behaviour: A Study of Reliance Digital, Puttur, DK

Purpose: In this networked world, the buyer's purchase decisions are influenced by the arrangement and presentation of items in the store. Nowadays, the furniture and furnishing types have become more viable in the retail industry. Many new businesses are entering into the organized format of retail in this category. Hence the retailers need to differentiate themselves from each other. As the products are similar, it is necessary to differentiate themselves by presenting them with visual merchandising. Objective: The core objective of this work is to study the influence of visual merchandising on consumer purchase patterns based on store attributes. Another objective of this study is to know the dimensions which influence the purchase behavior or decision of the consumers, Design/Methodology/Approach: For this analysis, we have considered many online sources, namely websites and blogs, which guide and review display merchandising. Percentages, charts, diagrams are being used to present the tabular data. Findings/Result: It is found that variables like store appearance, lighting, music, window display, mannequins, and price tags have an impact on the purchase choice of consumers, as per the changing need of consumer the retailer should always try to maximize the quantity of new merchandising relating to the trend. The store must adopt a congenial store atmosphere to attract a maximum number of customers and retain them in their business for long. Originality/Value: Through the help of primary and secondary data, the study revealed that male youngster customers are more frequently visiting the store. The study reveals that the seating arrangement inside the store is not sufficient. The majority of the customer is visiting Reliance Digital to buy some specific product. Spot purchase activities are happening very rarely. Paper Type: Visual Merchandising on Consumer buying behaviour at Reliance Digital is a case study analysis work.

Emergency supplies purchase patterns during COVID-19 outbreak in the developing economy: frequency and stockpiling drivers’ assessment

PurposeThe purpose of the paper is to assess the roots of stockpiling behaviors and to give a quantitative assessment of shopping frequency changes for emergency supplies during the coronavirus disease 2019 (COVID-19) pandemic. In addition, the authors aim to determine the sources that influenced emergency supply purchases during the COVID-19 outbreak.Design/methodology/approachThe study used a polling or survey process implementation to collect the data on shopping patterns and to determine the drivers of stockpiling behaviors for the assessment. The polling was conducted using a snowball technique, and descriptive and regression analyses were used to define the roots of the stockpiling behaviors and the shopping frequency changes.FindingsIt was determined that 88.0% of end-consumers increased their shopping volumes for emergency supplies. An almost twofold increase in the average duration of usage for stockpiled goods (from 11 to 21 days) was also determined. Also revealed was a reduction in shopping frequency from an average of seven (pre-COVID-19 period) to five (first wave of COVID-19 pandemic) days. Such disproportional increases in purchase volumes along with a slight reduction in shopping frequency indicate the strong stockpile patterns that occurred during the pandemic.Originality/valueThe research is based on data from Ukraine, where the number of COVID-19 cases was low. Despite the comparatively low spread of COVID-19 in large cities in Ukraine in relation to other cities globally, people still revealed panic and stockpiling behaviors. The study's quantitative assessment of shopping behaviors reveals the social and economic determinants of the shopping frequency.

dSubSign: Classification of Instance-Feature Data Using Discriminative Subgraphs as Class Signatures

Applications like customer identification from their peculiar purchase patterns require class-wise discriminative feature subsets called as class signatures for classification. If the classifiers like KNN, SVM, etc. which require to work with a complete feature set, are applied to such applications, then the entire feature set may introduce errors in the classification. Decision tree classifier generates class-wise prominent feature subsets and hence, can be employed for such applications. However, all of these classifiers fail to model the relationship between features present in vector data. Thus, we propose to model the features and their interrelationships as graphs. Graphs occur naturally in protein molecules, chemical compounds, etc. for which several graph classifiers exist. However, multivariate data do not exhibit the graphs naturally. Thus, the proposed work focuses on (1) modeling multivariate data as graphs and (2) obtaining class-wise prominent subgraph signatures which are then used to train classifiers like SVM for decision making. The proposed method dSubSign can also classify multivariate data with missing values without performing imputation or case deletion. The performance analysis of both real-world and synthetic datasets shows that the accuracy of dSubSign is either higher or comparable to other existing methods.

Dietary Patterns Derived from UK Supermarket Transaction Data with Nutrient and Socioeconomic Profiles

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.