Abstarct: Nowadays, recognizing affective factors on customer's loyalty in the competitive markets is vital and it plays a decisive role in increasing sales and raising profits and having long-term relationships with customers and ultimately promoting the transcendental goals of organizations. Taking correct decisions by managers in this field they require having sufficient information about customer's loyalty and choosing the right marketing strategies for each group of customers. Each day the services provided by rival companies are becoming more and more similar to each other and it is getting hard to surprise the customers by long term services, since the most innovative services are quickly imitated by competitors and launched to the market. Hence, investing in customer's loyalty is an effective and beneficial investment for companies. Loyalty leads to enhancing profits by: Increasing revenue, reducing the cost of absorbing new customers, reducing customer sensitivity to costs and reducing the cost of introducing procedures to customers. Therefore, this research attempts to help organizations fulfill their goals by providing a suitable method for better and more accurate recognition of effective factors on customer's loyalty and stable patterns that describe customer's behavior in the future. This is the first time that perceptual factors which affecting customer loyalty have been recognized and designed in a systematic structure with axiomatic design. Then Rough theory seeks to discover consistent patterns to predict customer's behavior as a norm. It should be noted that customers loyalty have been measured by Net Promoter Score which is a new method, it applied on 250 samples of Samsung cell phone customers. baxsed on the results of the research conducted by ROSETTA software,3 rules with higher certainty coefficients showed that for creating customer satisfaction, age and income in individual factors and the quality of goods and serving in perceptual factors were have more effective role than the rest. Dealers can apply these rules to predict customer behavior.