An Effective Analysis of Customer Satisfaction with Retail Sales Services

Analyzing retail sales data is essential for enhancing the product sales rate and helps to maintain trust between retailers and customers. Various existing methods employed strategies to increase customer satisfaction but failed to achieve significant results due to improper analysis of retail sales factors. Analyzing collected online shopping data is crucial for optimizing retail sales. The personalized experience, business-to-business (B2B), and add-on benefit factors are the hypotheses considered for the evaluation of the relationship between the consumers and the retailer. These hypotheses play a key role in analyzing customer satisfaction towards retail sales. These factors greatly influence the customer’s interest in the products. Retailers must offer additional benefits to earn customer trust. The validation and reliability of these hypotheses were evaluated, yielding Cronbach’s Alpha scores of of 0.924 for personalized experience, 0.914 for $B 2 B$, and 0.949 for add-on benefits. The analysis of customer satisfaction toward retail sales has obtained a minimum error of 0.359 for Root Mean Square Error (RMSE) and $\mathbf{0. 6 1 9}$ for Mean Absolute Scaled Error (MASE) compared to the existing analysis.