## 4 Case Study
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Advert** \\ **Version** \\ \end{tabular} } & \multirow{2}{*}{**Content**} & \multicolumn{2}{c}{**Results**} & \multicolumn{2}{c}{**Reach, Thou**} & \multicolumn{2}{c}{**Views, Thou**} & \multicolumn{2}{c}{**Cost per Result**} \\ \cline{3-10}  & & **Value** & **Change** & **Value** & **Change** & **Value** & **Change** & **Value** & **Change** \\ \hline \begin{tabular}{c} Version 0 \\ (initial) \\ \end{tabular} & \begin{tabular}{c} “Women’s handbags \\ for everyone” \\ \end{tabular} & 85 & 100\% & 1236 & 100\% & 2365 & 100\% & 0.75 & 100\% \\ \hline \begin{tabular}{c} Version 1 \\ (rule usage) \\ \end{tabular} & \begin{tabular}{c} “Women’s handbag \\ of good design” \\ \end{tabular} & 90 & 6\% & 1896 & 53\% & 3698 & 56\% & 0.33 & \(-\)56\% \\ \hline \begin{tabular}{c} Version 2 \\ (rule usage) \\ \end{tabular} & \begin{tabular}{c} “Handbag for good \\ price with high \\ review score” \\ \end{tabular} & 120 & 41\% & 4251 & 244\% & 5698 & 141\% & 0.12 & \(-\)84\% \\ \hline \begin{tabular}{c} Version 3 \\ (rule usage) \\ \end{tabular} & \begin{tabular}{c} “Great price nice \\ design. Details in \\ Messenger” \\ \end{tabular} & 136 & 60\% & 5548 & 349\% & 9683 & 309\% & 0.08 & \(-\)89\% \\ \hline \begin{tabular}{c} Version 4 \\ (rule usage) \\ \end{tabular} & \begin{tabular}{c} “Great price nice \\ design” \\ \end{tabular} & 115 & 35\% & 3442 & 178\% & 4771 & 102\% & 0.16 & \(-\)79\% \\ \hline \
\begin{tabular}{c} Version 5 \\ (rule usage) \\ \end{tabular} &
\begin{tabular}{c} “Women’s handbag \\ for great price and \\ with nice design” \\ \end{tabular} & 86 & 1\% & 1453 & 18\% & 1867 & \(-\)21\% & 0.63 & \(-\)16\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of effectiveness for the generated advertising content based on learning associative rules. Figure 12: Derivation of associative rules. Table 2 shows that all versions of the new advertisement have improved results. In addition, the result indicator shows how many times customers interacted with the ad. As it's evidenced, the advert v.3 is performed best (60% better than version 0) (see Table 2). In particular, the Reach indicator is 4.5 times better than that of version 0, and the Views indicator is 4.1 times better. It also made it possible to reduce the cost per result by 89%. So, the effectiveness of advertising in social networks increased by at least 60%, and the cost decreased by 89%. The intelligent method of creating advertising content based on semantic analysis can be used to create the content too. To determine the accuracy of this method, a semantic analysis of customer surveys (similar to the previous approach) was conducted based on the LSA and LDA methods (Figure 13). Figure 13 shows that based on the LSA method, the vast majority of keywords are present in documents 0-81%. Based on the LDA method, the majority of keywords are represented as well in the document 0-78%. The following advertising text was formed on the basis of these words:  
_\"You'll love the design and the color. More details in Messenger\"_  
Table 3 presents a comparison of the effectiveness for the generated advertising content based on LSA and LDA methods. The results of an advertising campaign with text content, conducted from 1 May to 15 May 2022, were used as initial data. Table 3 shows that the Reach indicator increased by 80 people and the Results indicator--by 94%, the cost per result is decreased by USD 0.70 accordingly. The comparison results show that the effectiveness of the advertisement based on the LSA and LDA methods is increased approximately thrice, and the cost per result is decreased by 31%. Figure 13: Probability of including keywords in individual phrases of LSA and LDA methods. Table 4 presents a comparison of the effectiveness for the created advertising content based on the created new content for the relevant target group in the period 20 May 2022-4 June 2022.