Application of Mann–Whitney U Technique for Comparative Analysis in Entrepreneurship Research

Comprehending client happiness is fundamental for the entrepreneurship and consumer behavior studies, since it directly impacts loyalty, purchasing choices, and long-term company viability. This research uses the Customer Satisfaction Scores and Behavior Data from Kaggle, including 120 entries over 10 well specified criteria, to analyze these dynamics. The dataset has demographic variables like Age, Gender (Male, Female), and Location (e.g., Phoenix, AZ; Los Angeles, CA), in addition to behavioral metrics such as Purchase_History (Yes/No), Support_Contacted (Yes/No), and Loyalty_Level (Low, Medium, High). Core outcome measures include the Satisfaction Score on a 1-10 scale and the Satisfaction Factor, which reflects variables such as Price and Product Quality. Distinct IDs such as Customer_ID (e.g., 81-237-4704) facilitate organised data and enable segmentation analysis. The dataset’s combination of category, ordinal, and continuous variables makes it especially appropriate for both non-parametric and parametric methodologies. Methods such as the Mann-Whitney U test, Kruskal-Wallis test, regression analysis, and clustering facilitate comparative analysis and the identification of patterns. The research emphasizes that using Kaggle datasets may provide practical insights for augmenting client interaction, optimizing loyalty tactics, and strengthening entrepreneurial decision-making.