Patterns of Individual Shopping Behavior

Much of economic theory is built on observations of aggregate, rather than individual, behavior. Here, we present novel findings on human shopping patterns at the resolution of a single purchase. Our results suggest that much of our seemingly elective activity is actually driven by simple routines. While the interleaving of shopping events creates randomness at the small scale, on the whole consumer behavior is largely predictable. We also examine income-dependent differences in how people shop, and find that wealthy individuals are more likely to bundle shopping trips. These results validate previous work on mobility from cell phone data, while describing the unpredictability of behavior at higher resolution.Comment: 4 pages, 5 figure

Cyber purchasing behavior of adolescent: Family communication relationships and parental influence

[[abstract]]With the advent of internet-consumer generation and changes in family structures, the purchasing power of teenagers has greatly increased. Since family communication patterns are still the primary source from which adolescents learn their purchasing behavior, parental influence on adolescent purchasing decisions cannot be ignored. Parents play a critical role in family communication and, thus, establish the consumer socialization model for cyber purchasing behavior of adolescents in this study. A sample of young people aged 16–30 was divided across three age spans. Family socioeconomic status was explored to see if it relates to different family communication patterns, and whether family communication patterns in turn influence cyber purchasing behavior of adolescent. The results show that compulsive purchasing behavior is affected by stages of the adolescent life cycle and by the mother’s education level. Adolescents whose family communication patterns are concept-oriented tend to incur more planned buying. If the family communication patterns are social-oriented, the individual tends towards unplanned shopping behavior. The results show that, in addition to the adolescent life cycle and family communication patterns, family socioeconomic status and parental marital status also affect adolescent cyber purchasing behavior.[[notice]]補正完

Sequences of purchases in credit card data reveal life styles in urban populations

Zipf-like distributions characterize a wide set of phenomena in physics, biology, economics and social sciences. In human activities, Zipf-laws describe for example the frequency of words appearance in a text or the purchases types in shopping patterns. In the latter, the uneven distribution of transaction types is bound with the temporal sequences of purchases of individual choices. In this work, we define a framework using a text compression technique on the sequences of credit card purchases to detect ubiquitous patterns of collective behavior. Clustering the consumers by their similarity in purchases sequences, we detect five consumer groups. Remarkably, post checking, individuals in each group are also similar in their age, total expenditure, gender, and the diversity of their social and mobility networks extracted by their mobile phone records. By properly deconstructing transaction data with Zipf-like distributions, this method uncovers sets of significant sequences that reveal insights on collective human behavior.Comment: 30 pages, 26 figure

Discovering temporal regularities in retail customers’ shopping behavior

In this paper we investigate the regularities characterizing the temporal purchasing behavior of the customers of a retail market chain. Most of the literature studying purchasing behavior focuses on what customers buy while giving few importance to the temporal dimension. As a consequence, the state of the art does not allow capturing which are the temporal purchasing patterns of each customers. These patterns should describe the customerâ\u80\u99s temporal habits highlighting when she typically makes a purchase in correlation with information about the amount of expenditure, number of purchased items and other similar aggregates. This knowledge could be exploited for different scopes: set temporal discounts for making the purchases of customers more regular with respect the time, set personalized discounts in the day and time window preferred by the customer, provide recommendations for shopping time schedule, etc. To this aim, we introduce a framework for extracting from personal retail data a temporal purchasing profile able to summarize whether and when a customer makes her distinctive purchases. The individual profile describes a set of regular and characterizing shopping behavioral patterns, and the sequences in which these patterns take place. We show how to compare different customers by providing a collective perspective to their individual profiles, and how to group the customers with respect to these comparable profiles. By analyzing real datasets containing millions of shopping sessions we found that there is a limited number of patterns summarizing the temporal purchasing behavior of all the customers, and that they are sequentially followed in a finite number of ways. Moreover, we recognized regular customers characterized by a small number of temporal purchasing behaviors, and changing customers characterized by various types of temporal purchasing behaviors. Finally, we discuss on how the profiles can be exploited both by customers to enable personalized services, and by the retail market chain for providing tailored discounts based on temporal purchasing regularity

The Environmental Cost of Shopping: A Comparison Between Online and In-Person Shopping

Over the past twenty years, online shopping has grown in popularity as more and more companies began expanding their business online. A common belief is that online shopping is an environmentally friendly substitute for in-person shopping because consumers stop taking individual shopping trips. This paper challenges this substitution assumption. I also investigate how consumers\u27 gas consumption patterns vary according to their shopping practices and interpret those results from a sustainability point of view. The shopping behavior correlated with a decrease in gas consumption, and therefore a reduction in greenhouse gas emissions, will be ruled as the more environmentally responsible practice from a consumer point of view. Using the National Household Travel Survey data set from 2017, I investigated the sociodemographic characteristics of in-person shoppers. I also analyzed the relationship between the number of shopping trips, number of deliveries, and total gas consumption. I find evidence that online shoppers keep practicing in-person shopping, meaning that both practices are complementary. I also find evidence that online shopping decrease gas expenditure, which means that from a gas consumption and consumer point of view, online shopping is better for the environment, but this conclusion needs to be interpreted with caution

Consumer behavior during the pandemic Covid 19

An outbreak of Covid-19 is not simply a global pandemic and public health crisis only. The pandemic has affected the global economy and financial markets. According to Sheth (2020), the Covid-19 pandemic and lockdown and the social distancing mandates have disrupted consumer buying behavior, especially at shopping. Many industries have undoubtedly been impacted, particularly service and industrial activities, which have been stopped to the point of insolvency. This is because much of the globe has imposed quarantine restrictions to contain the pandemic's spread, advising residents to stay at home and only go to acquire the necessities. Many customers' options for shopping are limited as a result of the lockdown and social distancing. This scenario has undoubtedly had an impact on consumer purchasing patterns. As a result, the shutdown significantly influences consumer behaviour, personal views, individual and household experiences, and traits

Mining urban lifestyles: urban computing, human behavior and recommender systems

This is the author accepted manuscript. The final version is available from IET via the DOI in this recordIn the last decade, the digital age has sharply redefined the way we study human behavior. With the advancement of data storage and sensing technologies, electronic records now encompass a diverse spectrum of human activity, ranging from location data, phone, and email communication to Twitter activity and opensource contributions on Wikipedia and OpenStreetMap. In particular, the study of the shopping and mobility patterns of individual consumers has the potential to give deeper insight into the lifestyles and infrastructure of the region. Credit card records (CCRs) provide detailed insight into purchase behavior and have been found to have inherent regularity in consumer shopping patterns; call detail records (CDRs) present new opportunities to understand human mobility, analyze wealth, and model social network dynamics