customer value analysis

In customer value analysis (CVA), a company conducts sample surveys of its customers and of its competitors' customers to determine the relative performance of the company on many attributes ranging from product quality and technology to pricing and sales support.
We mean the full specification of information that allows the computation of posterior distributions of the data --- sharp specifications such as independent errors with normal distributions and diffuse specifications such as probability distributions on parameters arising from sharp specifications. The model includes the following:
• Survey respondent effects are modeled by random location and scale effects, a t-distribution for the location.
• Company effects for each attribute through time are modeled by integrated sum-difference processes.
• Error effects are modeled by a normal distribution whose variance depends on the attribute; in the model, the errors are multiplied by the respondent scale effects.
Building the model and using it to form conclusions about CVA stimulated work on statistical theory, models, and methods:
• Data that provides an overall guide for methods used to explore data for the purpose of making decisions about model specifications;
• an approach to modeling random location and scale effects in the presence of explanatory variables;
• a reformulation of integrated moving-average processes into integrated sum-difference models , which enhances interpretation, model building, and computation of posterior distributions;
• post-posterior modeling to combine certain specific exogenous information - information from sources outside of the data - with the information in a posterior distribution that does not incorporate the exogenous information;
• Trellis display, a framework for the display of multivariable data.