Mining Customer Behavior in Trial Period of a Web Application Usage—Case Study
| Autor: | Goran Matošević, Vanja Bevanda |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Engineering
business.industry Decision tree 02 engineering and technology computer.software_genre Login Naive Bayes classifier Identification (information) Statistical classification Web mining 020204 information systems 0202 electrical engineering electronic engineering information engineering Web application 020201 artificial intelligence & image processing Data mining business Cluster analysis computer |
| Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9783319336237 |
| DOI: | 10.1007/978-3-319-33625-1_30 |
| Popis: | This paper proposes models for predicting customer conversion from trial account to full paid account of web application. Two models are proposed with focus on content of the application and time. In order to make a customer’s behavior prediction, data is extracted from web application’s usage log in trial period and processed with data mining techniques. For both models, content and time based, the same selected classification algorithms are used: decision trees, Naive Bayes, k-Nearest Neighbors and One Rule classification. Additionally, a cluster algorithm k-means is used to see if clustering by two clusters (for converted and not-converted users) can be formed and used for classification. Results showed high accuracy of classification algorithms in early stage of trial period which can serve as a basis for an identification of users that are likely to abandon the application and not convert. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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