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• Multiple regression (driver analysis) describes the relationship of each variable in a set (and the set of variables as a whole) to a single variable, helping to determine key individual "drivers" of satisfaction, loyalty, purchasing or other behaviors.

• Cluster analysis identifies homogeneous sub-groups within a much larger group of respondents. It identifies customer profiles, market segments or potential customers who fit into similar groups and, for example, make decisions and perceive products and services similarly.

• Factor analysis reduces a complicated data matrix into its more basic structural essentials. This helps, for example, in uncovering the basic dimensions that your employees might use to evaluate how satisfied they are in working for your organization.

• Perceptual mapping (multidimensional scaling) extracts multiple dimensions from a variable set and graphically positions key concepts. Perceptual mapping can, for example, help your company visualize how customers mentally organize your competitive set in your product or service category, and your brand's position relative to your competitors.

• Structural equation modeling tests how well observed data conforms to a theoretical model. This can help describe the process by which customer loyalty is built for your particular product or service category. It can be used for predicting future behaviors and sales.

• Data mining detects useful and sometimes unexpected patterns among variables in a data set. Executed properly, data mining can reveal cross-selling opportunities for your products and services at key points in a customer relationship.