Understanding Customer Satisfaction Factors: A Logistic Regression Analysis

Abstract

Background: In the dynamic landscape of modern business, understanding customer satisfaction is crucial for success and competitiveness. This research intends to analyze the various elements that affect customer contentment, concentrating on the impact of demographic characteristics, economic inclinations, and involvement in loyalty programs. Methods: Employing logistic regression analysis, this research analyzes data collected from a diverse customer base across various industries. The study explores the relationship between customer satisfaction (binary dependent variable) and key independent variables, including age, income level, and loyalty program participation. Model validation is conducted through the Hosmer-Lemeshow test, along with the assessment of model fit using Cox & Snell and Nagelkerke R² metrics. Multicollinearity is checked using the Variance Inflation Factor (VIF). Results: The logistic regression model reveals that age and income level significantly influence customer satisfaction, with younger customers and individuals with greater income levels being more probable to report satisfaction. Additionally, participation in loyalty programs emerges as a strong predictor of customer satisfaction. The model demonstrates good fit and predictive ability, as indicated by statistical tests and graphical analyses, including an ROC curve and a Predicted Probability Plot. Conclusions: The research offers significant understanding into the determinants of customer satisfaction, highlighting the importance of demographic factors, economic status, and loyalty programs. These findings offer both theoretical contributions to the field of customer satisfaction research and practical implications for business strategies focused on customer engagement and loyalty.

Received: 17 November 2023 / Accepted: 17 February 2024 / Published: 5 March 2024