Nowadays, e-commerce has enabled tourists to plan and purchase holidays more efficiently. Therefore, an efficient service provider needs to gain insight into the behaviour of potential tourists in the pre-purchase phase so as to better target promotions and move customers to purchase. Using clickstream data on the traceable and anonymous users of a tourist accommodation services website, our paper explores their online journey by considering browsing profiles as predictors of purchasing probability. We perform a two-stage analysis: first, identifying browsing profiles by means of a mixture hidden Markov model, and second, investigating purchase probability using a logit model based on users’ website history. We apply this approach to data from Sunweb, a Dutch online holiday provider.
Furio Urso - Nicola Argentino - Antonino Abbruzzo - Reza Mohammadi - Kevin Pak - Maria Francesca Cracolici (2025). Analysing Traceable and Anonymous Browsing Patterns to Understand Purchase Intent in Online Tourism. In Statistics for Innovation ISIS 2025. Short Papers, Plenary, Specialized, and Solicited Sessions (pp. 428-434) [10.1007/978-3-031-96736-8].
Analysing Traceable and Anonymous Browsing Patterns to Understand Purchase Intent in Online Tourism
Furio Urso
Primo
;Nicola ArgentinoSecondo
;Antonino Abbruzzo;Maria Francesca CracoliciUltimo
2025-06-17
Abstract
Nowadays, e-commerce has enabled tourists to plan and purchase holidays more efficiently. Therefore, an efficient service provider needs to gain insight into the behaviour of potential tourists in the pre-purchase phase so as to better target promotions and move customers to purchase. Using clickstream data on the traceable and anonymous users of a tourist accommodation services website, our paper explores their online journey by considering browsing profiles as predictors of purchasing probability. We perform a two-stage analysis: first, identifying browsing profiles by means of a mixture hidden Markov model, and second, investigating purchase probability using a logit model based on users’ website history. We apply this approach to data from Sunweb, a Dutch online holiday provider.File | Dimensione | Formato | |
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