The Analysis of Customers' Transactions Based on POS and RFID Data Using Big Data Analytics Tools in the Retail Space of the Future  
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Analysis of retail transactions based on data obtained using POS and RFID systems and the use of Big Data tools allow us to identify patterns and trends that are not visible with traditional approaches to analysis and, in fact, are not particularly applicable due to the large volume of such data [10]. Using powerful algorithms and machine learning to process large volumes of data opens up new opportunities for retail forecasting, risk management and informed decision making [3; 11; 12; 13; 14; 15; 16].  
Analyzing retail transactions is a complex process that requires a deep understanding of both retail principles and advanced technologies [17; 18; 19].  
POS systems are terminals or points of sale that not only process payments but also collect important transaction data, such as the time of the transaction (purchase), the items purchased, the payment methods, and the customer's (buyer's) personal information. This data can then be analyzed to identify purchasing trends and consumer behavior.  
RFID technologies enable the automatic identification of objects, people, and animals using radio waves [20]. In the context of retail transactions, RFID tags can be used to track the movement of customers and goods, which helps in inventory management, logistics, and the organization of customer flows/visits in physical or online space [21].  
Big Data tools play an important role in processing and analyzing the huge amounts of data collected through POS and RFID. They include various technologies, such as databases, cloud platforms, analytics tools, and machine learning. These tools enable real-time data processing, pattern detection, and deep analytical reporting [22; 23].  
These technologies are used for demand forecasting, which is the analysis of historical sales data to predict future demand for products; inventory management, which is the optimization of inventory levels in warehouses by analyzing sales data and product movement; and personalization of offers, which is the creation of personalized offers for customers based on their purchasing history and preferences.  
These technologies are transforming the retail industry, making it more efficient, accurate and customer-centric. In the future, we can expect to see even more development of these tools and their application to improve retail services and customer satisfaction.  
Based on the analysis of RFID and POS data in this paper, valuable insights were gained into customer behavior and store operations. Tracking customer journeys through RFID revealed key patterns, such as movement paths [11] and time spent in different sections of the store. For example, customers tended to spend more time in high-traffic areas, like the dairy section, which can inform product placement and staffing decisions. Additionally, the integration of RFID and POS data allows for more accurate inventory forecasting, helping to avoid stockouts and overstocking. These insights can also be used to create a more personalized shopping experience, including tailored promotions and recommendations, ultimately enhancing customer satisfaction and boosting sales.