An Unsupervised Approach for Customer Need Assessment in E-commerce: A Case Study of Japanese Customer Reviews

Kovacs, Mate; Buryakov, Daniil; Kryssanov, Victor V.

doi:10.1145/3493287.3493294

Cited by 3 publications

References 6 publications

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A novel approach to voice of customer extraction using GPT-3.5 Turbo: linking advanced NLP and Lean Six Sigma 4.0

Shahin,

Chen,

Hosseinzadeh

et al. 2024

Int J Adv Manuf Technol

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This research breaks new ground by utilizing the advanced natural language processing (NLP) capabilities of OpenAI's GPT-3.5 Turbo model for the extraction of Voice of Customer (VoC) data from online customer support interactions on Twitter. Traditional methods of VoC extraction have typically fallen short in capturing the richness and nuance of customer sentiment. Contemporary Machine Learning (ML) approaches, while improved, still struggle to interpret the contextual subtleties of digital customer communications effectively. This study showcases the innovative deployment of GPT-3.5 Turbo, demonstrating its superior performance in extracting VoC through a deeper understanding of conversational context and a more intuitive, chat-based data processing. Furthermore, the large-scale, multilingual processing capabilities of this model offer a more comprehensive and inclusive analysis of VoC. The study ties these advancements to Lean Six Sigma 4.0, illustrating how the integration of GPT-3.5 Turbo's transformative capabilities can elevate the customer-centric approach of Lean Six Sigma in the era of Industry 4.0. This innovative exploration points to a signi cant evolution in VoC analysis, offering potential for more insightful, real-time data-driven customer service strategies and a more substantial foundation for decision-making in product development and process improvement. Future research is encouraged to validate these preliminary ndings and to investigate ethical considerations associated with the use of such advanced NLP models.

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A novel approach to voice of customer extraction using GPT-3.5 Turbo: linking advanced NLP and Lean Six Sigma 4.0

Shahin,

Chen,

Hosseinzadeh

et al. 2024

Int J Adv Manuf Technol

View full text Add to dashboard Cite

show abstract

Harnessing customized AI to create voice of customer via GPT3.5

Shahin,

Chen,

Hosseinzadeh

2024

Advanced Engineering Informatics

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A Novel Approach to Voice of Customer Extraction using GPT-3.5 Turbo: Linking Advanced NLP and Lean Six Sigma 4.0

Shahin,

Chen,

Hosseinzadeh

et al. 2023

Preprint

View full text Add to dashboard Cite

This research breaks new ground by utilizing the advanced natural language processing (NLP) capabilities of OpenAI's GPT-3.5 Turbo model for the extraction of Voice of Customer (VoC) data from online customer support interactions on Twitter. Traditional methods of VoC extraction have typically fallen short in capturing the richness and nuance of customer sentiment. Contemporary Machine Learning (ML) approaches, while improved, still struggle to interpret the contextual subtleties of digital customer communications effectively. This study showcases the innovative deployment of GPT-3.5 Turbo, demonstrating its superior performance in extracting VoC through a deeper understanding of conversational context and a more intuitive, chat-based data processing. Furthermore, the large-scale, multilingual processing capabilities of this model offer a more comprehensive and inclusive analysis of VoC. The study ties these advancements to Lean Six Sigma 4.0, illustrating how the integration of GPT-3.5 Turbo's transformative capabilities can elevate the customer-centric approach of Lean Six Sigma in the era of Industry 4.0. This innovative exploration points to a significant evolution in VoC analysis, offering potential for more insightful, real-time data-driven customer service strategies and a more substantial foundation for decision-making in product development and process improvement. Future research is encouraged to validate these preliminary findings and to investigate ethical considerations associated with the use of such advanced NLP models.

show abstract

An Unsupervised Approach for Customer Need Assessment in E-commerce: A Case Study of Japanese Customer Reviews

Cited by 3 publications

References 6 publications

A novel approach to voice of customer extraction using GPT-3.5 Turbo: linking advanced NLP and Lean Six Sigma 4.0

A novel approach to voice of customer extraction using GPT-3.5 Turbo: linking advanced NLP and Lean Six Sigma 4.0

Harnessing customized AI to create voice of customer via GPT3.5

A Novel Approach to Voice of Customer Extraction using GPT-3.5 Turbo: Linking Advanced NLP and Lean Six Sigma 4.0

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