In today’s business landscape, the volume of transaction data is rapidly increasing. This study explores the integration of Point of Sale (POS) and Radio-Frequency Identification (RFID) technologies to enhance the analysis of customer transactions using big data tools. By leveraging these technologies, businesses can extract valuable insights to improve processes, optimize inventory, and boost customer satisfaction. The research employs an object—subject management approach, which facilitates real-time decision-making by merging retail transactions of the clients with their movement patterns. An experiment involving around 7000 customers demonstrates the effective collection and processing of POS and RFID data, highlighting the benefits of integrating these data streams. Key metrics, such as time spent in different store sections, provide deeper insights into consumer behavior. The findings reveal the potential of these technologies to transform retail services, offering opportunities for demand forecasting, risk management, and personalized customer experiences. The study concludes that merging POS and RFID data opens new avenues for developing management solutions aimed at enhancing customer engagement and the operational efficiency of the retailer. Future research will focus on further elaborating these solutions to maximize the benefits of integrated data analysis.

Consumer choice modeling takes center stage as we delve into understanding how personal preferences of decision makers (customers) for products influence demand at the level of the individual. The contemporary choice theory is built upon the characteristics of the decision maker, alternatives available for the choice of the decision maker, the attributes of the available alternatives and decision rules that the decision maker uses to make a choice. The choice set in our research is represented by six major brands (products) of laundry detergents in the Japanese market. We use the panel data of the purchases of 98 households to which we apply the hierarchical probit model, facilitated by a Markov Chain Monte Carlo simulation (MCMC) in order to evaluate the brand values of six brands. The applied model also allows us to evaluate the tangible and intangible brand values. These evaluated metrics help us to assess the brands based on their tangible and intangible characteristics. Moreover, consumer choice modeling also provides a framework for assessing the environmental performance of laundry detergent brands as the model uses the information on components (physical attributes) of laundry detergents. Through a comprehensive evaluation of product performance, including brand tangible estimation, we shed light on the sustainability attributes of laundry detergents, offering a roadmap for consumers and manufacturers alike to make more informed, environmentally responsible choices of laundry detergents based on their physical attributes. Knowing the estimates of the attributes for the laundry detergent products, manufacturers can modify their physical attributes, e.g., decrease the amount of the detergent needed for one wash while increasing the total weight of the laundry powder in the package. In this way, more ecology- and consumer-friendly decisions can be made by manufacturers of laundry detergents.