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Enhancing Organizational Performance: Harnessing AI and NLP for User Feedback Analysis in Product Development

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  • Tian Tian
  • Liu Ze hui
  • Huang Zichen
  • Yubing Tang

Abstract

This paper explores the application of AI and NLP techniques for user feedback analysis in the context of heavy machine crane products. By leveraging AI and NLP, organizations can gain insights into customer perceptions, improve product development, enhance satisfaction and loyalty, inform decision-making, and gain a competitive advantage. The paper highlights the impact of user feedback analysis on organizational performance and emphasizes the reasons for using AI and NLP, including scalability, objectivity, improved accuracy, increased insights, and time savings. The methodology involves data collection, cleaning, text and rating analysis, interpretation, and feedback implementation. Results include sentiment analysis, word cloud visualizations, and radar charts comparing product attributes. These findings provide valuable information for understanding customer sentiment, identifying improvement areas, and making data-driven decisions to enhance the customer experience. In conclusion, promising AI and NLP techniques in user feedback analysis offer organizations a powerful tool to understand customers, improve product development, increase satisfaction, and drive business success

Suggested Citation

  • Tian Tian & Liu Ze hui & Huang Zichen & Yubing Tang, 2024. "Enhancing Organizational Performance: Harnessing AI and NLP for User Feedback Analysis in Product Development," Papers 2405.04692, arXiv.org.
    • Handle: RePEc:arx:papers:2405.04692
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    References listed on IDEAS

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    1. Wang, Zheng & Wang, Lun & Ji, Ying & Zuo, Lulu & Qu, Shaojian, 2022. "A novel data-driven weighted sentiment analysis based on information entropy for perceived satisfaction," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
    2. S. Kujala, 2008. "Effective user involvement in product development by improving the analysis of user needs," Behaviour and Information Technology, Taylor & Francis Journals, vol. 27(6), pages 457-473.
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