Predictive Model presentation for Customer Satisfaction from Software Support Services with Data Mining Approach

Subject Areas : Strategic Management Researches

babak sohrabi ¹ , Iman raeesi ² , Samaneh Keshavarzi ³

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Received: 2017-05-01 Accepted : 2017-09-11 Published : 2018-05-22

Keywords: Data mining, Classification, Customer Satisfaction, Regression, Customer Satisfaction Model, Software Industry Services.  ,

Abstract :

Nowadays, productive or service organizations consider the customer's satisfaction as a significant criterion to assess their work quality. Since almost all the organizations need to compete in different areas including services, giving a high quality service is so important to achieve a permanent competitive advantage. In order to survive in competitive markets, organizations and companies have to provide high quality customer services. The results of many researches illustrate that the service quality is the necessity for customer's satisfaction. Though, a lot of customer oriented companies have problem in recognizing and evaluating the customers' preferences and they often misunderstand the customers' demands. Because providing a high quality service requires understanding the relationship between the demands of customers and the quality of services provided by company. The organizations and companies which give software service also include this rule. The purpose of this research is to present a model to predict the customer's satisfaction from the provided services , also determine the influence of each effective variable on customer's satisfaction, as well be informed of customer's satisfaction level from provided service by the mentioned company. The proposed study used predictive algorithms such as Regression and Classification on data by Rapid Miner. Finally the method with the highest accuracy and minimum error were selected. In addition, in order to determine the most effective variables in customer's satisfaction, the weighting method was used. In order to make decisions and improve customer satisfaction, the results will be available for managers.

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