Brand Service Quality Evaluation Using the SERVQUAL Customer Algorithm

Abstract

As the competitive landscape grows and customer expectations accelerate, brands are under increasing pressure to deliver quality service and ensure consumer retention and loyalty. This research presents an on-demand, data-driven evaluation of brand service quality. Utilizing the SERVQUAL model in conjunction with a robust Customer Feedback Algorithm, the SERVQUAL model assesses the gap between customer expectations and customer perceptions through the five critical perspectives on service quality: tangibles, reliability, responsiveness, assurance, and empathy. Our methodology collects customer feedback through systematic surveys and captures real-time feedback using customer digital interaction data. This feedback is then processed with a customer feedback algorithm that calculates gap scores, yielding a brand service quality index. To increase the reliability and the overall accuracy of the surveys, we integrate various advanced analytics features (including sentiment analysis and weighted scoring) to produce quality indicators based on customers' subjective responses. The algorithm also has a built-in dynamic learning capability to accommodate changes to customer expectations over time. We collected empirical data from brands across various service industries, including, but not limited to, service-based companies in retail, banking, and telecommunications. Quantitative analysis of data revealed the factors that customer satisfaction is significantly influenced by, and how they differ across sectors. Above all, this system has the potential to help brands identify service quality deficits and assist in strategic decision-making through real-time data-driven analytics. The formulation of the SERVQUAL model with the real-time and automated nature of dynamic data processing is not only scalable, it is scalable and replicable.