Cart-State-Aware Discovery of E-Commerce Visitor Journeys with Process Mining

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dc.authoridDOGAN, ONUR/0000-0003-3543-4012
dc.authoridTOPALOGLU, BILAL/0000-0001-8466-3095
dc.contributor.authorTopaloglu, Bilal
dc.contributor.authorOztaysi, Basar
dc.contributor.authorDogan, Onur
dc.date.accessioned2025-03-20T09:50:31Z
dc.date.available2025-03-20T09:50:31Z
dc.date.issued2024
dc.departmentİzmir Bakırçay Üniversitesi
dc.description.abstractUnderstanding customer journeys is key to e-commerce success. Many studies have been conducted to obtain journey maps of e-commerce visitors. To our knowledge, a complete, end-to-end and structured map of e-commerce journeys is still missing. In this research, we proposed a four-step methodology to extract and understand e-commerce visitor journeys using process mining. In order to obtain more structured process diagrams, we used techniques such as activity type enrichment, start and end node identification, and Levenshtein distance-based clustering in this methodology. For the evaluation of the resulting diagrams, we developed a model utilizing expert knowledge. As a result of this empirical study, we identified the most significant factors for process structuredness and their relationships. Using a real-life big dataset which has over 20 million rows, we defined activity-, behavior-, and process-level e-commerce visitor journeys. Exploitation and exploration were the most common journeys, and it was revealed that journeys with exploration behavior had significantly lower conversion rates. At the process level, we mapped the backbones of eight journeys and tested their qualities with the empirical structuredness measure. By using cart statuses at the beginning and end of these journeys, we obtained a high-level end-to-end e-commerce journey that can be used to improve recommendation performance. Additionally, we proposed new metrics to evaluate online user journeys and to benchmark e-commerce journey design success.
dc.identifier.doi10.3390/jtaer19040138
dc.identifier.endpage2879
dc.identifier.issn0718-1876
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85213474984
dc.identifier.scopusqualityQ1
dc.identifier.startpage2851
dc.identifier.urihttps://doi.org/10.3390/jtaer19040138
dc.identifier.urihttps://hdl.handle.net/20.500.14034/2236
dc.identifier.volume19
dc.identifier.wosWOS:001386759000001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofJournal of Theoretical and Applied Electronic Commerce Research
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250319
dc.subjecte-commerce
dc.subjectonline user behavior
dc.subjectcustomer journey
dc.subjectprocess mining
dc.subjectbig data
dc.titleCart-State-Aware Discovery of E-Commerce Visitor Journeys with Process Mining
dc.typeArticle

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