Turn Raw Data Into Clear Segments
CFR Solutions applies statistical clustering to group respondents by behavior, attitude, and need, so you're not staring at a spreadsheet of individual answers but a handful of segments you can actually build a strategy around.
Segments That Hold Up Outside The Spreadsheet
We run multiple clustering algorithms against your data, test different segment counts, and validate the split with stability and cross-validation checks, so the segments we hand back are ones your teams can actually act on, not an artifact of one arbitrary cut.
Clustering Matched To What You're Trying To Learn
From a first-pass K-means run to a full needs-based segmentation, we scale the method to the decision it needs to inform.
K-Means & Hierarchical Clustering
Distance-based clustering that groups respondents into distinct, non-overlapping segments.
Learn more →Latent Class Segmentation
Model-based clustering that uncovers hidden subgroups driving different response patterns.
Learn more →Needs-Based & Attitudinal Segmentation
Segments built on underlying needs and attitudes rather than surface demographics alone.
Learn more →Segment Profiling & Persona Development
Each segment translated into a clear profile and persona your teams can brief against.
Learn more →Cluster Validation & Stability Testing
Split-sample and bootstrap checks confirm segments hold up beyond the original dataset.
Learn more →Segment Sizing & Market Sizing
Each segment sized against the addressable market so priority can be set with confidence.
Learn more →Explore Our Other Essential Services
Every technique is built on the same rigorous, quota-clean data — pick the analysis that matches the question your study needs to answer.
Brand Mapping
Perceptual maps showing how brands sit relative to each other on key attributes.
Learn more →CHAID Analysis
Decision-tree segmentation that splits respondents by the strongest predictors.
Learn more →Cluster Analysis
Groups respondents into natural segments based on shared attitudes or behavior.
Learn more →Conjoint Analysis
Measures the trade-offs respondents make between product features and price.
Learn more →Correlation Analysis
Measures how strongly two or more variables move together.
Learn more →Discriminant Analysis
Classifies respondents into known groups using their strongest predictors.
Learn more →Factor Analysis
Reduces a long list of variables down to the underlying factors driving them.
Learn more →Multidimensional Scaling
Visual maps of how respondents perceive similarity between items or brands.
Learn more →Regression Analysis
Quantifies how much each predictor moves the outcome you care about.
Learn more →