## 3 Experimental Results: Measuring Key Customer Transaction Metrics Based on POS and RFID Data  
The collected RFID dataset consists of such data as customer ID number, dates, time, coordinates of a customer on axis X and Y, ID number of every basket in the store, position, movement time, and type of movement. Below is a visual image of x and y axis positions on store floor map (Figure 7). The x axis 150 and y axis 300 in the graph map below represent a customer position, and the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Department** & **Section** & **Product** & **Department** & **Section** & **Product** \\ \hline E & E & Entrance & & B1 & Food1 (Fast food) \\ \hline \multirow{2}{*}{V} & V1 & Fruits and Veg1 & & B2 & Food2 (Spices, sauces) \\ \cline{2-6}  & V2 & Fruits and Veg2 & & B3 & Food3 (Butter, flour, sugar, salt) \\ \hline \multirow{2}{*}{F} & F1 & Seafood1 & & B4 & Food4 (Pasta, cereals) \\ \cline{2-6}  & F2 & Seafood2 & & B5 & Food5 (Sweets) \\ \hline G & G & Prepared Food & & B6 & Food6 (Yogurt, jams) \\ \hline M & M & Meat & & C1 & Snacks and Sweets1 (Candies) \\ \hline K & K & Frozen food & C & C2 & Snacks and Sweets2 (Chocolate, cookies) \\ \hline I & I & Western Deli & & C3 & Snacks and Sweets3 (Snacks) \\ \hline J & J & Japanese Deli & & D1 & Liqour1 (Wine, whiskey, vodka) \\ \hline L & L & Drinks & & D2 & Liqour2 (Beer) \\ \hline \multirow{2}{*}{S} & S &
\begin{tabular}{c} Ceremonial Goods \\ (Holiday Products) \\ \end{tabular} & H & H & Central Aisle \\ \hline \multirow{3}{*}{A} & A1 & Household goods1 & R & R & Registers \\ \cline{2-6}  & A2 & Household goods2 & & S & Ceremonial Goods (Holiday Products) \\ \cline{1-1} \cline{2-6}  & A3 & Household goods3 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Departments, sections and products of the supermarket. Figure 6: Grid layout of the store and section abbreviations. identification number of this customer is 5110203. The customer's location is in the Vegetable Department and specifically in section V2 during her customer journey through the store. The travel time in the dataset refers, as mentioned before, to the duration time of the shopping trip. The duration time is displayed in seconds, as shown in Figure 8 below. Customer number 5110203's shopping journey started from her entrance and ended at the checkout and the duration time is shown spent in each \"sustained\" position during his trip. Figure 8 shows that the customer spent 1 s at each \"sustained\" position, i.e., moved fairly quickly along the outer perimeter of the sales area and stopped only at section J for 10 s. When examining the financial transaction, this customer actually bought only milk and salad (which she did not spend much time buying and, judging by the data, simply grabbed them from the shelf). Figure 8: Demonstration of “sustained” time at some of the stop points of customer #5110203 on the map of the supermarket. Figure 7: Demonstration of X, Y coordinates of customer #5110203 in the map of the supermarket with different coloring according to the section. It is important to identify the customer's movement around the store, such as her stopping to look at goods, buying specific goods in any department or keeping moving along her path. The stop points are actually fixed in the data by \"sustained\" status, while moving status is marked as \"migration\" (Figure 9). The same customer #5110203 with her non-stop movement during her shopping is shown in Figure 10.  
If we combine the information visualized in Figures 9 and 10, we obtain the customer trajectory with stop points and movement combined, which is pictured in Figure 11.  
Figure 10: Customer movements (“migration”) marked with blues lines during her shopping trip (example of customer #5110203). Figure 9: Customer stop points, marked with red dots, during her shopping trip (the example of customer #5110203). Based on the above data, measurements of key metrics were made, i.e., the time spent by the client in each section and in each of the departments. (Figure 12).