Predicting Customer Satisfaction Using Data Mining Techniques
Customer satisfaction is one of the most important factors in determining the success/failure of a customer-focused business. Loyal customers are the main assets for businesses to survive and succeed in today’s competitive market. Although businesses spend a lot of resources to collect raw data regarding the customers’ behaviors, few would succeed in converting the data to meaningful results. Rigorous and advance analysis of the collected data would be required to convert the raw data into valuable and useful results. In this work, we utilize historical data, along with machine learning technics to predict customer satisfaction and determine the key-drivers for achieving a high rate customer satisfaction. The data consists of survey responses of over 76000 customers from a well-known bank. We have used several classification models for classification purpose. The result of the models are compared and analyzed to find the advantage/disadvantage of each method. Despite the fact that more than 80% of the data was missing (due to unanswered questions by the survey takers), we achieved an overall accuracy of more than 80%.