Services · Data
Statistical Analysis & Modeling
The number of statistical analysis and visualization tools has exploded, yet a core set of capabilities can help explain consumer behavior and trends in many situations.
Commonly used tools
For consumer and B2B/industrial marketing research.
Analysis of covariance
Adjusts data for the effects of other variables, so that analysis of variance can be used effectively.
Analysis of variance
Decomposes the total variance of an experiment into variance attributable to specific variables.
Cluster analysis
Forms homogeneous groups of objects, such as people, brands, or markets.
Canonical correlations
Measures in precise quantitative terms the relationship between two sets of variables.
Conjoint analysis
Measures the relative importance of benefits and product attributes, overall and for individual respondents.
Discriminant analysis
Classifies people or objects into pre-defined groups.
Factor analysis
Reduces a set of inter-correlated items to a smaller set of independent items, giving insight into the underlying structure.
Perceptual mapping
Gives a simplified picture of customers’ perceptions of brands and brand images. Includes correspondence analysis.
Regression analysis
Measures the relationship between one dependent variable and one or more independent variables. Includes logistic regression, where the dependent variable is either true or false.
Design wisely.
The usefulness of any statistical method hinges on data quality and underlying assumptions. Discuss your project with us before zeroing in on a specific technique, which may or may not be appropriate.