Abstract
The paper addresses the problem of e-customer behavior characterization based on Web server log data. We describe user sessions with the number of session features and aim to identify the features indicating a high probability of making a purchase for two customer groups: traditional customers and innovative customers. We discuss our approach aimed at assessing a purchase probability in a user session depending on categories of viewed products and session features. We apply association rule mining to real online bookstore data. The results show differences in factors indicating a high purchase probability in session for both customer types. The discovered association rules allow us to formulate some predictions for the online store, e.g. that a logged user who has viewed only traditional, printed books, has been staying in the store from 10 to 25 min, and has opened between 30 and 75 pages, will decide to confirm a purchase with the probability of more than 92 %.
Highlights
Along with the development of the information society and the knowledge-based economy, electronic commerce has been gaining increasing popularity all over the world
We describe user sessions with the number of session features and aim to identify the features indicating a high probability of making a purchase for two customer groups: traditional customers and innovative customers
Among them 6 171 sessions were classified as performed by innovative customers (466 of these sessions, i.e. 7.55 %, ended with a purchase), and 5 415 sessions were performed by traditional customers
Summary
Along with the development of the information society and the knowledge-based economy, electronic commerce has been gaining increasing popularity all over the world. The most common type of e-commerce has been B2C (Business-toConsumer) trade, typically realized through online stores. Chodak environment, detailed data on e-customer behavior may be collected and analyzed using data mining techniques. The acquired knowledge may be used to improve the customer service in a Web store, to increase customer satisfaction, and to raise the online store’s conversion rate in the long run
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
