# Personalized Advertising in E-Commerce: Using Clickstream Data to Target High-Value Customers
## 1 Introduction  
E-commerce has become a popular activity among internet users both for searching and for buying goods, with a consistent increase in sales volume and visitors over time. According to the research completed by eMarketer [1] and Statista [2], online retail e-commerce sales in 2022 exceeded 5.7 trillion U.S. dollars worldwide and will reach $6.51 trillion by 2023, with e-commerce websites taking up 22.3% of total retail sales. Online shops meet challenge to attract high-value customers and capture their interest for making purchase decisions during their online search. A high-value customer is someone who actively engages with a business, showing a strong interest in their products or services. These customers make more frequent purchases compared to others, and when they do buy, they tend to spend a significant amount of money. In essence, their consistent engagement and higher spending make them particularly valuable to the business. Our research introduces a novel algorithm designed to gauge customer engagement and identify high-value customers. Important part of the activities aiming to engage customers and convince them to make buying decision is made in the areas of improving purchase experience via shopping sites, extending payment modes, using direct communication with the customer and other online marketing strategies. Despite wide usage of customer engagement approaches it is still not clear what drives such big e-commerce growth. In 2022 the survey \"Drivers of online purchases\" was conducted among 9989 U.S. respondents [3]. According to their opinion, online purchases are most influenced by the convenient delivery and shopping method, avoiding crowds, cheaper goods price, wide product range, etc. To attract customers and influence their purchasing decisions, e-commerce businesses aim to enhance the customer experience on their websites, make a clickstream analysis and utilize diverse online marketing strategies. Clickstream analysis is used in web analytics to track and analyze the navigational behavior of users as they interact with a website. The term \"clickstream\" refers to the sequence of clicks or actions that a user takes during a session on a website. Clickstream analysis involves collecting and analyzing this data to gain insights into user behavior, preferences, and the overall user experience. Clickstream data records contain online actions of internet users, including the number of visited pages, time spent on each page, and devices used for browsing, as explained by [PERSON] et al. [4]. By analyzing clickstream data, companies can identify potential buyers, measure their level of engagement, and offer personalized promotions and incentives to encourage them to make a purchase. Different methods have been proposed to analyze clickstream data, such as visualization of online shopping behavior [5], evaluating time spent on each item page [6], and detecting browsing patterns to identify potential customers [7; 8; 9]. Overall, clickstream analysis provides valuable insights into customer behavior and preferences, allowing e-commerce companies to offer more personalized and effective advertising campaigns. With the increasing importance of e-commerce in today's digital economy, clickstream analysis is becoming an essential tool for companies looking to optimize their online sales and marketing strategies. The research works tend to employ more customer history information for increasing precision of the models. However, measuring customer engagement still remains a challenge, especially for predicting behavior and ad targeting success to the new customers who do not have long browsing history in general, or losing their browsing history due to deleting cookies, changing browsing devices. This article proposes a novel algorithm to measure customer engagement and identify high-value customers for personalized advertising campaigns.