Analyzing Customer Satisfaction of a Mobile Application using Data Mining Techniques


  • Jirapon Sunkpho College of Innovation, Thammasat University, Thailand
  • Markus Hofmann Department of Informatics and Engineering, Technological University Dublin, Ireland


Data mining, Customer analysis, CRISP-DM, Decision tree


Traditionally, statistical methods were used to analyze customer data to gain insight. Recently, data mining techniques have evolved and have been used to analyze customer data. This paper demonstrated how Data Mining techniques can be applied to customer satisfaction analysis of MEA Smartlife, a mobile application (app) developed by Metropolitan Electricity Authority (MEA) of Thailand in order to provide some e-service features to its customers. User satisfaction rating along with demographic profile from 1,446 subjects with diverse backgrounds were collected. Machine learning techniques were then applied to the dataset following the CRISP-DM methodology. Modeling techniques for this study include decision tree, Naïve Bayes, and logistic regression as intuitiveness of the model rather than the predictive performance is more important than predicting whether the customers like the app. The resulting models achieved more than 90% accuracy while having a lower level of precision on the negative class. Feature selection techniques help improve overall accuracy and improve the negative class precision. The resulting model indicated ‘ease of use’ is the most important factor in determining whether customers are satisfied or dissatisfied with the app. The payment feature also plays an important role in making customers satisfied with the app.


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How to Cite

Sunkpho, J., & Hofmann, M. (2019). Analyzing Customer Satisfaction of a Mobile Application using Data Mining Techniques. Thammasat Review, 22(2), 50–64. Retrieved from