Retail Complaint Patterns: A Statistical and Predictive Analysis Framework

Abstract

Customer complaints provide essential insights for improving service quality and customer satisfaction in retail. This study analyzes complaint patterns across multiple store locations to identify significant variations and potential causes. Using a chi- square goodness-of-fit test, we evaluate whether observed complaint frequencies differ from expected distributions, high- lighting stores with atypical complaint rates. Data visualization techniques reveal temporal and spatial trends, while classification models predict the likelihood of complaints based on product and store characteristics. To enhance model performance, we apply Recursive Feature Elimination (RFE) to identify influential factors, such as store size and product category. The results offer actionable insights for retailers, enabling targeted strategies to reduce complaints and improve the customer experience. This study underscores the value of complaint data in strategic decision-making and resource allocation for enhanced operational efficiency.