Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/113546
Título: Online museums segmentation with structured data: the case of the Canary Island's online marketplace
Autores/as: Díaz Meneses, Gonzalo 
Estupiñán Ojeda, Miriam 
Vilkaite Vaitone, N
Clasificación UNESCO: 580101 Medios audiovisuales
531106 Estudio de mercado
Palabras clave: Segmentation
Museum
Online user behaviour
Digital marketing
Fecha de publicación: 2021
Publicación seriada: Journal of Theoretical and Applied Electronic Commerce Research 
Resumen: This paper’s primary objective is to segment the online marketplace of the Canary Islands’ museums by using different conversion funnel metrics. Little systematic research exists on digital user behaviour, and much less is known about how to segment cultural users with structured data from manually extracted and SEO software sources. With this aim in mind, we built a database with data related to the different phases of the conversion funnel of the museums to segment this online museum marketplace. In the findings, not only do we acknowledge the existence of different segments, but we also provide insight into the user’s digital behaviour by considering different metrics from the different phases of the conversion model process (awareness, consideration, conversion and loyalty). The originality of this paper is multifold. Firstly, it estimates the potential optimisation of these websites to improve the digital marketing implemented by the museum sector of the Canary Islands. Secondly, it sheds light on what benchmarking tactics and statistics procedures can be followed to carry out a non-hierarchical segmentation with standardised and comparable data. Thirdly, it contributes to the literature of digital marketing by eclectically combining the conversion funnel model, benchmarking techniques and non-hierarchical segmentation procedures.
URI: http://hdl.handle.net/10553/113546
ISSN: 0718-1876
DOI: 10.3390/jtaer16070151
Fuente: Journal of Theoretical and Applied Electronic Commerce Research [ISSN 0718-1876], v. 16(7), p. 2750-2767, (Diciembre 2021)
Colección:Artículos
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