Customers' experience of purchasing event tickets: mining online reviews based on topic modeling and sentiment analysis

Abstract

PurposeIn search of creating an extraordinary experience for customers, services have gone beyond the means of a transaction between buyers and sellers. In the event industry, where purchasing tickets online is a common procedure, it remains unclear as to how to enhance the multifaceted experience. This study aims at offering a snapshot into the most valued aspects for consumers and to uncover consumers' feelings toward their experience of purchasing event tickets on third-party ticketing platforms.Design/methodology/approachThis is a cross-disciplinary study that applies knowledge from both data science and services marketing. Under the guise of natural language processing, latent Dirichlet allocation topic modeling and sentiment analysis were used to interpret the embedded meanings based on online reviews.FindingsThe findings conceptualized ten dimensions valued by eventgoers, including technical issues, value of core product and service, word-of-mouth, trustworthiness, professionalism and knowledgeability, customer support, information transparency, additional fee, prior experience and after-sales service. Among these aspects, consumers rated the value of the core product and service to be the most positive experience, whereas the additional fee was considered the least positive one.Originality/valueDrawing from the intersection of natural language processing and the status quo of the event industry, this study offers a better understanding of eventgoers' experiences in the case of purchasing online event tickets. It also provides a hands-on guide for marketers to stage memorable experiences in the era of digitalization.

Identifying hidden semantic structures in Instagram data: a topic modelling comparison

“…Meanwhile, in many cases, insufficient information exists in tourism concerning the necessary steps of data pre-processing and how hyperparameters are defined. For instance, in some recent tourism studies, the default hyperparameters were used, and the number of topics was defined without estimating these parameters (Celuch, 2021; Yu and Egger, 2021). Consequently, non-ideal results might have been reported as a successful topic extraction.…”

Strategies to Improve Customer Experience: A Case of an Online Shopping Platform in Indonesia

“…Moreover, it is crucial for customer service personnel to have skills and knowledge for online support because it is one of the factors that can affect online customer purchasing experiences. Therefore, follow-ups on customer's feedback after purchasing and obtaining positive feedback leads to a better online customer experience (Camilleri, 2022;Celuch, 2021). The previous study shows that good customer complaint handling can be a determinant for the customer to return once they have a good online shopping experience (Izogo & Jayawardhena, 2018;Singh & Söderlund, 2020).…”

Topic Modeling: Perspectives From a Literature Review

“…We also found studies in which TM was applied to sentiment analysis of information published on social networks and opinion sites on topics such as customer satisfaction in tourism and hotel services [93], [94], [95], [96], [97], consumer behavior [98], [99], [100], [101], and nursing staff experience [102], [103].…”