A panoptic framework of visitor intelligence

The Council for Australasian Tourism and Hospitality Education 2020 Conference

A panoptic framework of visitor intelligence


Oral Presentation Working Paper


Prof. Andi Smart 1, Dr. Laura Phillips 1, Dr. David Ross 1, Dr. Pikakshi Manchanda 1, Mrs. Cristina Mosconi

1.University of Exeter

This paper presents a conceptual framework for comprehensive visitor intelligence and discusses digital approaches for capturing, analysing, and synthesising visitor data. The Datasphere provides a reference framework for research that seeks to identify and understand commonality and difference in visitor preference and behaviour. In addition, it can help site managers develop targeted experiences and assess which aspects of the tourist attraction require improvement.



Digital technologies, visitor intelligence, innovation, audience research, segmentation, tourism


Visitor studies have witnessed a shift with the rise of new data streams including user generated content, devices, and operations (Li, Xu, Tang, Wang, & Li, 2018). Innovative digital technologies enable the collection and analysis of visitor data allowing a better understanding of visitor profiles, behaviour and experience. Despite advancements made, an overarching framework of data streams that provides a comprehensive understanding of visitors, from multiple dimensions, has not emerged in intellectual discourse. The provision of a visitor intelligence tool provides significant opportunities for curatorial activities at tourist sites; a tool that makes use of big data in a practical way. Thus, the aim of this paper is two-fold. First, to present a conceptual framework of visitor data streams based on a synthesis of state of the art in visitor studies. Second, to propose a tool that provides tourist attractions with an opportunity to implement and operationalise the conceptual framework.


The datasphere is a visitor-centred framework that illustrates data generated by visitors during their visit (Fig. 1). It is panoptic in that each data stream can be captured and analysed semi-automatically with a device, e.g. smartphone/tablet.

Figure 1: The visitor intelligence datasphere


Personal data comprises visitor demographic (e.g. age, gender, education, income, marital status), geographic (e.g. residence, nationality, country of origin) and psychographic (e.g. motivations, interests, attitudes). Personal data offers an overall understanding of visitors and enables basic segmentation according to age, nationality or personal motivation (Dawson & Jensen, 2011). To collect personal data, a questionnaire-based research instrument can be deployed at the start of the visit and embedded within digital interpretation devices offered to visitors.


Visitors’ motivations may change depending on the size of the visiting party, e.g. visiting alone, with partner, friends or children, or the type of holiday surrounding the visit, e.g. extended holiday, short break, or day trip (Falk, 2009). The choice of interpretative media used (e.g. audio-guide, guided tour) also influences the way visitors make sense of a site (Wang, Park, & Fesenmaier, 2012). These contextual aspects, captured in the pre-visit survey, impact the perceived experience and add layers of visitor intelligence to traditional forms of segmentation.


Geospatial technologies such as GPS and geofencing provide affordances for tracking visitors’ temporal and spatial behaviour, e.g. visitor pathways/trajectories and dwell time in proximity to points of interest (POI), producing visual representation (e.g. heatmaps of visitor movement). This allows site managers to examine visitor dwell times, identify congested areas of interest and understand patterns of navigating the heritage site (Wolf, Hagenloh, & Croft, 2012). It also provides insight into curatorial effectiveness with respect to POIs. Visitor journey analysis and spatial behaviour segmentation can complement other data streams, e.g. demographic or psychographic groups (East, Osborne, Kemp, & Woodfine, 2017).


In addition to visitor dwell time, having accurate access to visitors’ visual attention and fixation during the visit provides further insights into which site artefacts or features attract most interest within the same space (Wedel & Pieters, 2008). Visitors’ gaze time and direction can be recorded using devices equipped with eye-tracking sensors. Analysis of visual fixation/attention observing certain artefacts and displays together with frequency of visual visits (counts) to a POI provides, following the mind-eye hypothesis, insight into visitor cognition. The analysis of this data highlights variances of visual interest among different audiences segmented in terms of their spatial journey or personal data.


Artefacts and POIs stimulate a range of physiological responses in visitors baring emotional arousal and engagement (Antón, Camarero, & Garrido, 2018). Recording visitors’ emotional arousal during the visit offers non-verbal data revealing the impression certain POIs have on visitors (Li, Walters, Packer, & Scott, 2018). Emotional arousal can be captured with a device (e.g. ring or bracelet) that measures visitor’s skin conductance and heart rate variation throughout the visit. This data can be triangulated with other data streams, allowing to understand if an exhibit stirs greater emotional arousal across visitors of different segments. Pupil dilation from PCCR-based analyses can also be used (pending correct environmental conditions) to corroborate this data.


Each individual experiences and benefits from a site differently depending on their motivation, demographics, or context of visit.

Visitor experience can be conceptualised into four realms: Escapist (diverging to a new self), Esthetic (indulged in environments), Entertainment (being entertained), and Educational (learning something new) (Pine & Gilmore, 1998). This data can be captured via a survey at the end of the visit with items assessing type of experience (Oh, Fiore, & Jeoung, 2007).


Visitor satisfaction can be assessed using measures such as intention to revisit the site, willingness to recommend others to visit, the perceived memorability of the experience, and overall satisfaction (Oh, 1999). These tangible outputs can be measured in a post-visit survey. Widely employed in the tourism industry, satisfaction assessment is enhanced when analysed against other data streams.


During their visit, visitors interact direct and indirectly with many aspects of the site, e.g. staff, interpretation available, facilities (Ponsignon, Durrieu, & Bouzdine-Chameeva, 2017). These dimensions can be captured from natural language feedback posted on digital platforms (social media, e.g. TripAdvisor, Google, Twitter) or collected on-site (verbal or written visitor reviews) and analysed using Natural Language Processing (NLP) techniques. Using a classification framework, a sentiment analysis concerning broad positive or negative performance of each interaction and aspect level instances found within the text is produced (Ma, Cheng, & Hsiao, 2018). Data coded to the schema allows site managers to observe general patterns with respect to the sentiment of individual dimensions and provides evidence in support of improvement activity.


The datasphere enables new ways of articulating the visitor experience, offering easy access to elements such as experiential interactions, emotions and outcomes. Theoretically, it provides a conceptual reference model for studies exploring visitor intelligence and audience research. The datasphere may be used to support contemporary research that is seeking to address ‘what drives visitor experience?’. From a management perspective, it offers a comprehensive view of visitor preferences and behaviours that permits the enhancement of curatorial activities and overall site design.

In practice, some data streams serve operational purposes and can be continuously collected, while others are more useful at a strategic level thus can be collected periodically. Therefore, the datasphere can be applied in modular style according to the type of tourist attraction, aim of visitor intelligence (e.g. to identify target audiences for specific experiences developed), resources available, and depth of visitor intelligence required.


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