Date of Graduation
5-2024
Document Type
Thesis
Degree Name
Bachelor of Science in Data Science
Degree Level
Undergraduate
Department
Data Science
Advisor/Mentor
Schubert, Karl
Committee Member
Sharma, Nikhil
Second Committee Member
Barrett, David
Abstract
This project leverages Natural Language Processing (NLP) to analyze customer feedback from Sam’s Club, aiming to pinpoint key factors influencing Net Promoter Score (NPS). Using sentiment analysis, bigram, and trigram techniques, the project analyses textual data to identify underlying themes and patterns that affect customer satisfaction. These analyses reveal actionable insights into customer preferences and pain points, facilitating a deeper understanding of what drives customer satisfaction in retail environments. By correlating these findings with NPS, this paper details strategies to enhance customer experiences at Sam’s Club, ultimately aiming to improve both satisfaction levels and NPS.
Keywords
Natural Language Processing; Net Promoter Score
Citation
Moreno, G. (2024). Employing Natural Language Processing to Link Customer Survey Feedback with Net Promoter Scores. Data Science Undergraduate Honors Theses Retrieved from https://scholarworks.uark.edu/dtscuht/10
