This paper presents a methodology for setting up a Decision Support system for User Interface Design (DSUID). We first motivate the role and contributions of DSUID and then demonstrate its implementation in the case of usability diagnosis of web pages, based on time analysis of clickstream data. The resulting DSUID diagnostic reports enable website managers to learn about possible sources of usability barriers. The proposed DSUID analytic method is based on the integration of stochastic Bayesian and Markov models with models for estimating and analyzing the visitors' mental activities during their interaction with a website. Based on this approach, a seven-layer model for data analysis is suggested and an example of a log analyzer that implements this model is presented. We demonstrate the approach with an example of a Bayesian network applied to clickstream data and conclude with general observations on the generic role of DSUID and the implementation framework we propose.