How to deeply understand the voice of the customer? A proposal for a synthesis of techniques for analyzing online reviews in the hospitality industry

Abstract

PurposeThis study aims to use the voice of the customer (VoC) strategy to collect user-generated content (UGC) compare customer expectations with reality, make the necessary improvements for the business and create personalized strategies for each customer to maximize revenue, focus on hospitality industry in Vietnam market.Design/methodology/approachThis study proposes a synthesis of techniques for a deep understanding of the VoC based on online reviews in the hospitality industry. First, 409,054 comments were collected from websites in the hospitality sector. Second, the data will be organized, stored, cleaned, analyzed and evaluated. Next, research using business intelligence (BI) solutions integrating three models, including net promoter score (NPS), graph model and latent Dirichlet allocation (LDA), based on natural language processing (NLP) technique, experiment on Vietnamese and English data to explore the multidimensional voice of customer’s row. Finally, a dashboard system will be implemented to visualize analysis results and recommendations on marketing strategies to improve product and service quality.FindingsExperimental results allow analysts and managers to “listen to the customer’s voice” accurately and effectively, identify relationships between entities, topics of discussion in favor of positive and negative trends.Originality/valueThe novelty in this study is the integration of three models, including NPS, graph model and LDA. These models are combined based on the BI solution and NLP technique. The study also conducted experiments on both Vietnamese and English languages, which ensures more effective practical application.

Guest editorial: Marketing via smart technologies in hospitality and tourism

“…Le et al . (2024) use the voice of the customer strategic approach to collect user-generated content and compare customer expectations with reality, to make the necessary improvements for the business and create personalized strategies for each customer to ultimately maximize the revenue of hospitality business units.…”

“…The findings show that conjoining TAM and TPB with trust results in a robust model for predicting customers' intention to use smart technologies in the tourism and hospitality industry. Le et al (2024) use the voice of the customer strategic approach to collect user-generated content and compare customer expectations with reality, to make the necessary improvements for the business and create personalized strategies for each customer to ultimately maximize the revenue of hospitality business units. Methodologically, a combination of three models took place: the net promoter score (NPS), graph model and latent Dirichlet allocation (LDA).…”