Voice in customer loyalty research

Balducci, Bitty; Marinova, Detelina

doi:10.4337/9781800371637.00015

Cited by 1 publication

(3 citation statements)

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“…Surveying research related to the VoC, text mining techniques to extract the VoC, especially in the field of hospitality, the research found some gaps: First, studies such as (Nicholas and Lee, 2017;Rajendran, 2021;Marcolin et al, 2021;Balducci and Marinova, 2022;Rajendran et al, 2023) just focused on exploring the VoC on texts in one language, that is English. Either the research of Alamsyah et al (2021) only mentioned Indonesian; Second, the techniques to extract features from the customer's voice are tokenization (Rajendran, 2021), LSA (Sezgen et al, 2019), and IDF (Nicholas and Lee, 2017).…”

Section: Research Gaps and Noveltymentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

“…Therefore, the VoC is also seen as a way to shape and build user profiles, creating a personalized marketing strategy that meets the needs of each customer. In which, a study by Balducci and Marinova (2022) has also proposed a solution to build customer loyalty based on the VoC. In general, the VoC has evident values that businesses need to care about, the VoC helps businesses keep up with the rapidly changing customers, thereby surpassing competitors and increasing revenue.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Le,

Ho,

Nguyen

et al. 2024

JHTI

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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.

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