## 5 Conclusions  
The analysis of RFID and POS data provided valuable insights into customer behavior and store operations. The RFID dataset, which includes customer IDs, timestamps, movement coordinates, and product interactions, was used to track customer journeys throughout the store. A detailed examination of this data revealed key patterns, such as customer trajectories--the path taken by customers in the store was visualized by combining RFID movement data with time stamps. For example, customer #5110203 was tracked as moving swiftly through the store, with notable stops in certain sections, like the dairy area. These movements were categorized as either \"sustained\" (stopping to shop) or \"migration\" (moving without stopping). Another key metric, which is time spent in sections, was also computed from the data. The amount of time customers spent in different departments was calculated, revealing that certain areas, like the dairy section, saw longer engagement, while others experienced quick movements. A histogram of time distribution across customer visits showed that most customers spent between 8-14 min in the store, with a smaller group spending 20-40 min or more. This insight can be used to optimize staffing and customer engagement strategies. The overall visualization of customer movement was demonstrated by aggregating data from all customers over a 4-week period, and a comprehensive map of customer trajectories was created, providing an overview of traffic patterns within the sales area. This helps identify high-traffic areas and sections that require further optimization. The integration of RFID and POS data offers significant potential for future retail spaces. By continuing to track and analyze customer movement, stores can optimize store layouts. With a clearer understanding of customer movement, retailers can redesign store layouts to improve traffic flow and enhance the shopping experience. High-traffic areas can be stocked with popular products, while under-shopped sections can be reimagined to boost engagement. By correlating customer behavior with sales data, stores can better predict inventory needs, avoiding stock-outs or overstocks. This is particularly useful for managing high-demand products or adjusting stock levels in under-performing sections. Therefore, inventory management can be improved. As retailers gain more insights into customer preferences and behavior patterns, they can tailor the shopping experience to individual customers. Personalized promotions, targeted marketing campaigns, and recommendations based on past behavior will likely enhance customer satisfaction and drive sales. By doing so, personalized shopping experience can be provided. Advanced analytics using RFID and POS data can lead to better demand forecasting and implementation of dynamic pricing. Retailers can predict product demand more accurately, enabling dynamic pricing strategies and optimized stock replenishment. This can improve operational efficiency and reduce costs. In conclusion, combining RFID and POS data can dramatically improve store efficiency, inventory management, and customer satisfaction. By leveraging business intelligence tools and machine learning, retailers can not only optimize their operations but also create a more personalized and engaging shopping environment for their customers. Conceptualization, M.K. ; methodology, M.K., A.C. and G.C. ; software, M.K. ; validation, A.C.; formal analysis, A.C.; investigation, G.C. ; resources, M.K., A.C. and G.C. ; data curation, M.K. ; supervision, G.C. ; funding acquisition, G.C. All authors have read and agreed to the published version of the manuscript.