Predicting Customer Satisfaction in Urban Company: A Regression-Based Approach Using SERVQUAL Dimensions and Central Composite Design
Keywords:
Customer Satisfaction, Urban Company, Response Surface Methodology (RSM), SERVQUAL Model, Central Composite Design (CCD), Service Quality, Performance Metrics.Abstract
Urban Company, a prominent hyperlocal online home service provider, relies heavily on customer satisfaction to maintain its competitive edge. Understanding and predicting customer satisfaction levels are critical for enhancing services and retaining loyal customers. This study employed a regression-based approach to predict customer satisfaction scores (CSS) on the Urban Company platform. The SERVQUAL model's five dimensions (Tangibles, Reliability, Responsiveness, Assurance, and Empathy) were integrated as input factors, with the calculated CSS using Root Mean Square (RMS) as the output parameter. A dataset comprising 514 survey responses was collected, with corresponding CSS values calculated. A Central Composite Design (CCD) methodology within the Response Surface Methodology (RSM) framework was employed to develop and evaluate the prediction model. From the 514 survey responses was selected based on the experimental design specified by the CCD approach. The Central Composite Design (CCD) approach optimizes experimental designs by thoroughly examining the influence of significant process variables using performance metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Squared Error (MSE). By integrating into information systems, CCD ensures a detailed analysis to improve decision-making based on customer feedback, resulting in a well-fitted and predictive model. The study's findings highlight the benefit of leveraging regression-based approach techniques within an RSM and CCD framework for Urban Company to gain insights into customer satisfaction score (CSS) dynamics and proactively retain loyal customers. This predictive model is valuable for enhancing service quality and customer experience.
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