6533b859fe1ef96bd12b83d3
RESEARCH PRODUCT
Using association rules to assess purchase probability in online stores
Grzegorz ChodakGrażyna Suchackasubject
Web usage miningWeb serverclick-stream analysise-CommerceAssociation rule learningComputer sciencebusiness.industrylog file analysisdata mining02 engineering and technologyE-commercecomputer.software_genreSession (web analytics)association rulesWorld Wide WebWeb mining020204 information systemsLog dataClick stream analysis0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingbusinesscomputerInformation Systemsdescription
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 %.
year | journal | country | edition | language |
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2016-09-06 | Information Systems and e-Business Management |