Uncover hidden patterns in your data. Latent Class Modeling is a powerful statistical technique used to identify unobserved (latent) subgroups within a population based on observed data.
We apply three key types of Latent Class models to solve a range of research and analytics challenges:
Groups individuals based on similarity in response patterns, rather than Euclidean distance as in the traditional K-Means approach.
Probability-based classification: assesses the probability that each respondent belongs to every cluster. In a model with a good fit, these probabilities are usually close to 100% for the cluster a user is most associated with and close to 0% for the other clusters
Handles variables of mixed scale types (nominal, ordinal or continuous), which allows the use of behavioral data in clustering
Statistical tests are available to assess the model fit and compare different models
More robust with missing data - respondents can still be classified even with incomplete information
Continuous or discrete factors can be used in a LC cluster model to deal with rating scale usage bias as a faster, more flexible alternative to case level standardization, and to capture obvious relationships in the data
Segmenting customers using both attitudinal and behavioral data
Creating stable, well-defined segments with interpretable patterns
Simplify complex data into meaningful dimensions.
This model identifies latent factors that explain correlations among observed variables. Unlike traditional factor analysis, LC Factor models generate discrete, ordinal factors such as Low / Mid / High, which can be more actionable in marketing and strategy.
Works with mixed data types
No need to rotate factors for interpretation
Since LC factors are discrete and ordinal, using LC Factor models rather than traditional factor / principal components analysis may be a better approach when creating affinity scores, because Low/Mid/High groups will be created by the model rather than by arbitrary cutoffs of values of a continuous factor
LC factors can be converted to segments. For example, if 2 factors are identified with 2 levels each (Low/High), then respondents can be grouped into 4 segments, which will represent each possible combination of values in the 2 factors: (Low, Low), (Low, High), (High, Low), (High, High)
Build predictive models that reflect real-world complexity.
The LC Regression model simultaneously classifies individuals into segments and builds a separate regression model for each segment. Each segment represents a homogeneous group of respondents. This approach is ideal when data reflects substantial heterogeneity.
Handles variables of mixed scale types (nominal, ordinal or continuous)
No assumptions of linearity, normality, or homogeneity
Separate models for each class improve predictive accuracy
Supports covariates and parameter constraints to avoid overfitting
Ideal for conjoint analysis - simultaneously identify segments in population and product features that appeal to each segment
Segment-specific product design strategies
Modeling outcomes like purchase intent across distinct respondent types
Latent Class models provide deeper insight than traditional techniques by recognizing that one-size-fits-all analysis often misses the mark, giving you the tools to uncover hidden structure in your data for more precise targeting and better strategic outcomes.