Abstarct: Today, primary Origin of success in Competitive advantage for companies is successful continuances developments of new product. By increasing diversity in products and orders volumes, product life curve will be decrease. so variable competitive rules in the business world shows introducing new product to the market process importantly. Today, most organizations find out that reliance only on traditional competitive levers like price increasing, cost reduction and Differentiation in products and services aren’t enough and concepts like speed and flexibility in the competition sound significant and trend toward introducing new product and services to the market is a reason for this attitude change. But introducing a new product to the market is expensive and risky.Accepting new products by customers is one of risky factors in new product development projects. In order to decreasing this risk, we have to recognize all customers’ needs and expectations from our products. customer knowledge management is a competitive advantage for companies by recognizing customer’s needs and it reduces new product failure risk. customer knowledge management by combining knowledge management and customer relationship management (CRM), more clearly extract knowledge ‘for’ customers, knowledge ‘about’ customers,and knowledge ‘from’ customers, so that a more beneficial product can be delivered to the right group of customers, to prevent product failure and to ensure commercial success. In this research, To develop new products baxsed on customer knowledge management frxamework, the customers’ knowledge should be elicited and converted to a pattern of consumers’ need towards the products attributes. The need patterns and consumers’ characteristics have to be segmented to be more meaningful for marketers to target and communicate. This reduces the customer fuzziness dimension in the NPD process and elevates the likelihood of success. Data collection is done with a survey of 580 university and private companies peoplein Tehran. The questionnaire consists of two sections: 1-measuring means-end chains with Association Pattern Technique (APT), 2-questions about buying behavior and demographic characteristics to better profile the segments. Data analysis of means-end chains with clustering algorithms, results in five segments of consumers, five groups of preferred product attributes and proper promotions for each segment. Data analysis of second part of the questionnaire, help to profile and describe the segments found. The main objectives of this research include finding and implementing systematic approaches to extract customer knowledge, and finding frxameworks for more involvement of customer and using customer knowledge in the idea generation phase of NPD process