The Research On Customer Demand Recognition And Pricing Strategy Of Mass Customization Product

Facing the fierce market challenges and the customer demand trend in customization and personalization nowadays, companies is in urgent need of satisfying the customer damand and provide mass customization products with limited resources. Mass customization is delivering mass production product, maximizing the extension in satisfying customized demand, to achieve economies of scale in production and scope economy.In the early design period of mass customization product, the primary goal is to consider low cost and high efficiency, but also take into account the individual needs of customers. The variable cost arised by customization demand changes in product design period has accounted for up to 60% of the total cost in the whole product life cycle. In order to effectively capture and refine the customization needs, the design processes gradually extend from the traditional manufacturing design to integrated extension to the market research, sales and service departments. In this case, obtaining effective treatment over the uncertain, unstable, inconsistent, and dynamic changes in customized product needs, and delivers the market research result into product development process is extremely critical. Providing correct pricing to customers based on their customized need is also an important part in product design.Firstly, we delivered a comprehensive demand survey among the mass customization customer and used XLSTAT for reliability analysis and factor analysis to identify the key factors impacting the choice of customer needs:custom prices, personalized level and delivery. By fully understanding the importance of these factors and their impact on customer demand, we establish the recognition model for mass customization customer demand. Secondly, through the analysis of industry and the business background of empirical study, we conducted clear and thorough k-Means clustering method to group fuzzy customer needs effectively and use Gray Relational Method to establish correlation analysis between each demand. After that, an algorithm was developed in R to realize Logical Regression Model to accurately forecast the trend of customer needs on new product features. After effectively recognize the customization demand and deliver accurate forecast of the future trend, as mentioned in the above study, we can adopt differential pricing for each segments to achieve revenue maximization. Finally, given the demand relationship function, the static pricing models were established for both adjustable capability and adjustable capability situation.Based on the foregoing content, a comprehensive example of customized software product is using to circumstantiate proposed approaches.At last, the whole-lenth research is summarized and the writer picks up with her opinions and suggestions for the futher work.