# Personalized Advertising in E-Commerce: Using Clickstream Data to Target High-Value Customers
## 5 Conclusions and Future Works  
The majority of e-commerce sites face the challenge of selecting customers who need assistance and encouragement to make purchasing decisions. The problem is that customers do not always admit they need help, and may simply switch to a different e-shop where they feel more attended to. Moreover, e-commerce platforms typically aspire to autonomously control advertising campaign costs and delineate the dimensions of their target audience. To address this issue, the article presents an algorithm for calculating the Customer Merit index (CM), which estimates the value of a customer based on their clickstream data and determines whether assistance should be provided during their online shopping experience. Typically, this assistance comes in the form of personalized advertising campaigns that reflect the customer's interests. While the duration and modes of these campaigns were not specified, the presented scenario of accumulating customer surfing history data could facilitate decision-making in this area. Given the agile and migratory nature of e-commerce websites, which cater to a wide range of customers and goods, the proposed method of dynamic customer ranking allows for the weights of evaluation factors to be recalculated and adjusted CM values to be \begin{table}
\begin{tabular}{c r r r r} \hline \hline  & \multicolumn{4}{c}{_E-Shops_} \\ \cline{2-5} \multicolumn{1}{c}{\(L\)_Values_} & \multicolumn{1}{c}{**A**} & \multicolumn{2}{c}{**B**} \\ \cline{2-5} \multicolumn{1}{c}{\(L\) & **Target Group**} & \multicolumn{1}{c}{\
\begin{tabular}{c} **Other** \\ **Customers** \\ \end{tabular} } & \multicolumn{1}{c}{**Target Group**} & \multicolumn{1}{c}{
\begin{tabular}{c} **Other** \\ **Customers** \\ \end{tabular} } \\ \hline
0.2 & 8.28 & 1.43 & 4.65 & 3.11 \\
0.3 & 10.32 & 1.25 & 5.49 & 2.84 \\
0.4 & 12.58 & 1.15 & 6.56 & 2.64 \\
0.5 & 15.32 & 1.08 & 7.55 & 2.52 \\
0.6 & 17.54 & 1.04 & 8.96 & 2.40 \\
0.7 & 19.85 & 1.03 & 9.83 & 2.35 \\
0.8 & 22.49 & 1.01 & 11.16 & 2.30 \\
0.9 & 25.21 & 1.01 & 12.14 & 2.28 \\
1.0 & 27.81 & 1.01 & 13.37 & 2.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The percent of ads success in selected customer group. obtained in response to changes in the trading situation. Additionally, the setting of CM limit value L allows for the modification of the cluster size of customers who are assigned to the advertising campaign. This feature is crucial for meeting changes in advertising budget, modes, and campaign formats. The experimental testing of the proposed method was carried out on two online shops A and B with different parameters such as size, number of customers, and assortment of goods. The e-shop A has more than 40 thousand visitors per day and near half million clicks records, The B respectively 20 and 110 thousand. The results of the empirical research confirm the stability of the method, which can reliably select a sample of the most valuable customers by applying the same value of L across different e-commerce structures. By analyzing the effectiveness of advertising campaigns on two different e-commerce sites A and B, we have selected 9 different size target groups. The target group size is directly influenced by the limiting value L of the CM index. The study found that the success of advertising campaigns highly depends on the size of the targeted customer group. Specifically, a bigger CM index limit value L resulted in a higher success rate, as fewer customers were targeted but the advertising was more effective. As example, for target group L = 0.2 which consists of 41% customers, we have only 8% of successful transactions, while for smaller target group with L = 1 and 12% of customers we have almost 28% of success in advertising company. On the other hand, only about 1% of purchases are observed among unselected customers.