Uncover Segments, Branch By Branch
CFR Solutions runs CHAID (Chi-Square Automatic Interaction Detection) analysis to split your data into statistically significant, decision-tree segments — ranking predictors, testing every split, and profiling the groups that matter most to your target variable.
Every Split Tested, Every Segment Validated
Each candidate split is tested against a chi-square significance threshold before it's allowed into the tree, predictors are ranked by their contribution to the target variable, and every terminal segment is profiled and checked for a usable, real-world sample size.
Segmentation Formats Matched to Your Objective
From a first-pass predictor scan to a fully exhaustive tree, we size the CHAID model around the target variable and dataset you bring us.
Decision Tree Segmentation
Data split into statistically distinct segments using chi-square tests at every node of the tree.
Learn more →Target Variable Profiling
Segments profiled against your key target variable to surface which groups over- or under-index.
Learn more →Predictor Variable Ranking
Every candidate predictor ranked by its statistical contribution to explaining the target variable.
Learn more →Multi-Level Tree Splits
Exhaustive CHAID modelling that keeps splitting each branch until no further significant split remains.
Learn more →Chi-Square Significance Testing
Bonferroni-adjusted chi-square tests applied at every candidate split before it's accepted into the tree.
Learn more →Segment Profiling & Reporting
Final segments delivered as a client-ready report with tree diagrams, sample sizes, and profile summaries.
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.
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