Behavioral Analysis Model for Enhancing Attendees Experiences in Events Through K-Clustering Technique

Abstract

Events management landscape plays an important role in delivering an exceptional attendee experience, from planning to implementation and attendee’s engagement which serves as a critical success element for event organizers. Despite the increasing use of technology in event management, there remains a limited understanding of attendees' behavioural engagement in events either during or after for enhanced attendees’ experiences. This study seeks to bridge the gap and examine attendees’ behavioural segmentation using K-clustering technique for the identification of attendees’ engagement during and after events. This research aims to develop a behavioural analysis model using K-clustering techniques to identify attendees' engagement in events for improved attendees’ experiences. The quantitative research method was used for this research. The designed and model implementation was developed using Python Programming language. The results showed that the attendees’ engagement was clustered into four namely the minimally, multidimensional, highly cognitive and quietly, highly affective and socially engaged. Also, there was no string engagement in terms of the observed age or gender. The elbow performance metrics shows that the four behavioural engagement patterns best represent the data without complexities with the within-cluster sum of squares value of 171820.15 as the inflection point. The silhouette score of 0.37 indicates a decent but not perfect clustering and good enough for the earl-stage attendee segmentation and need further tuning for decision making. The study concludes that event attendees participate more in highly affective and multidimensional segments and that K-clustering technique serve as an important method for understanding both low- and high-involvement of attendees in events