Cracking Customer Pain Points: NLP Topic Modeling of Satisfaction Surveys

Author(s)	Vinay Kumar Yaragani
Country	USA
Abstract	This paper explores the use of Natural Language Processing (NLP) topic modeling to identify customer pain points from satisfaction surveys. As businesses increasingly rely on customer feedback to shape their strategies, extracting actionable insights from vast volumes of text data remains a challenge. We apply advanced NLP techniques, focusing on topic modeling, to uncover recurring themes and sentiments hidden within open-ended survey responses. By systematically identifying pain points, this approach provides a data-driven understanding of customer concerns, enabling businesses to address key issues that impact satisfaction and loyalty. The study emphasizes the potential of NLP to transform qualitative feedback into quantitative insights, offering a scalable solution for enhancing customer experience and guiding strategic decision-making. Our results demonstrate how these insights can be directly tied to operational improvements, driving both customer retention and competitive advantage.
Keywords	NLP Topic Modeling, Customer Pain Points, Satisfaction Surveys, Text Analysis, Customer Experience
Published In	Volume 4, Issue 2, March-April 2022
Published On	2022-04-19
Cite This	Cracking Customer Pain Points: NLP Topic Modeling of Satisfaction Surveys - Vinay Kumar Yaragani - IJFMR Volume 4, Issue 2, March-April 2022.

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International Journal For Multidisciplinary Research