# Personalized Advertising in E-Commerce: Using Clickstream Data to Target High-Value Customers
## 3 Methodology for Calibrating the Customer's Value Metric  
The efficient advertising problem is firstly related to cost decisions, as showing ads for each customer means expenses for the company in case those ads are clicked. Achieving high precision of identification of target customer group which can potentially bring biggest turnover becomes an extremely important marketing problem. The conservative and widely accepted selection strategy mean that the target group for advertising is selected from customers who have added an item to their shopping basket. The number of such customers is about 10-30% from total number of surfers. The maximalist advertising strategy includes all customers who have ever viewed the description of at least one item, which should address about 90% of total number of surfers. Increasing the target group of customers for showing advertising ads dramatically increases advertising budget, and lowers the profit of e-shop. The features which could enable optimal selection of target group for advertising should increase the interest and response of the customers to the advertisement which shows for them. This article presents a comprehensive approach to targeting advertising scenarios for e-commerce websites by utilizing clickstream data analysis and calculating the accumulated customer activeness indicator. Target clusters of customers are built by weight-based adjustment of visited links of different types. Clickstream data provides a detailed record of a visitor's online journey, including the sequence of pages visited, visit durations, timestamps, search terms, ISPs, countries, browsers, and computer specifications. The algorithm used in our strategy dynamically evaluates the accumulated activeness indicator, taking into account the customer's activity level, expediency in choosing items, and time spent for exploring them. This helps to identify customers who are actively engaged to the e-shop and are more likely to make a purchase. The most widespread approach of reaching the customers by ads is to display advertising and tagging, which implies that the advertising banner is shown when the customer has left the webpage of the e-shop. This advertising tool attempts to retain visitors by offering particular products and attractive pricing. Any type of classification based on highlighting particular characteristics of customer behavior meets difficulties of identifying them by data analysis and machine learning tools, it especially concerns e-shop visitors who have not made any purchase decisions, and their clickstream data consists mainly of browsing history. In general, we can characterize the e-shop surfers by assigning them to three large classes:  
* The first class consists of random surfers who are amused by the web-searching process, but they do not have any buying intentions. * The second class consists of customers who already have made a buying decision and are looking for the particular item in the e-shop. In this case the surfers pre-analyze the supply for these particular goods, make comparative evaluation of prices, and in some cases they even pre-test the quality of goods by arriving to the \"real\" shop. Therefore, the online shop is used only for getting discounts and other price awards, also for making payment and arranging delivery of goods. * The third class of the web-surfers consists of individuals who have not yet decided about the object and location of their purchase, they use online search for discovering most attractive propositions of e-shops. This particular class is the most attractive for the organizers of marketing advertising campaigns. The size of the third class greatly depends on the goals of the e-shop, its size and popularity. The approximate assumption on the size of these parameters is based on expert knowledge, which generally specify number of indecisive customers as of approximately 50%. Our research covers the population of e-commerce online site users and their interactions within the web browser, all meticulously documented in a clickstream file.