This paper discusses three primary techniques for detecting multicollinearity in regression analysis using questionnaire survey data on customer satisfaction. The three techniques are correlation coefficients, variance inflation factor (VIF), and eigenvalue method. The study found that product attractiveness is the most significant factor influencing customer satisfaction. The presence of multicollinearity increases the standard errors of regression coefficients, making some significant variables statistically insignificant. The study also found that there is no evidence of multicollinearity among the variables. Advanced regression procedures such as principal components regression, weighted regression, and ridge regression can be used to determine the presence of multicollinearity. The study used a structured questionnaire survey among 310 young smartphone users aged 18-27 years. The data was analyzed using IBM SPSS version 23.0. The results showed that the correlation coefficients between the variables were moderate and not close to 0.8, indicating low multicollinearity. The VIF values were between 1 and 5, indicating moderate correlation between variables. The eigenvalue method also showed no evidence of multicollinearity. The study concluded that the relationship between customer satisfaction and the major factors (product quality, brand experience, product feature, product attractiveness, and product price) is significant. The variable product attractiveness is the most significant factor influencing customer satisfaction. The study recommends further research to identify other significant variables that influence customer satisfaction.This paper discusses three primary techniques for detecting multicollinearity in regression analysis using questionnaire survey data on customer satisfaction. The three techniques are correlation coefficients, variance inflation factor (VIF), and eigenvalue method. The study found that product attractiveness is the most significant factor influencing customer satisfaction. The presence of multicollinearity increases the standard errors of regression coefficients, making some significant variables statistically insignificant. The study also found that there is no evidence of multicollinearity among the variables. Advanced regression procedures such as principal components regression, weighted regression, and ridge regression can be used to determine the presence of multicollinearity. The study used a structured questionnaire survey among 310 young smartphone users aged 18-27 years. The data was analyzed using IBM SPSS version 23.0. The results showed that the correlation coefficients between the variables were moderate and not close to 0.8, indicating low multicollinearity. The VIF values were between 1 and 5, indicating moderate correlation between variables. The eigenvalue method also showed no evidence of multicollinearity. The study concluded that the relationship between customer satisfaction and the major factors (product quality, brand experience, product feature, product attractiveness, and product price) is significant. The variable product attractiveness is the most significant factor influencing customer satisfaction. The study recommends further research to identify other significant variables that influence customer satisfaction.