Latent Class Segmentation/Regression

We’ll help you identify and target segments with distinct drivers of behavior

Cluster Analysis

We can identify and target segments that have different needs, attitudes and behaviors toward your brand

Our state-of-the-art techniques can help address problems with commonly used segmentation methods

Discriminant Function Analysis

We have the ability to:

–Overlay segmentation results on your own databases
–Track segmentation over time
–Identify the key drivers of segment membership

Logistic Regression

We have a process that examines the key drivers of a segmentation, and the characteristics most likely to be associated with each segment

The Praxis Brand Positioning Model

Unlike most research firms, we take full advantage of Latent Class Regression and Structural Equation Modeling to arrive at Importance Models that fit the data—a much more reliable system than typical ad-hoc methodologies

Customer Satisfaction Modeling

For accuracy, we use Structural Equation Modeling to arrive at models that fit the data and identify the true drivers of customer satisfaction

Conjoint Analysis

Discrete Choice Conjoint Analysis and Modeling

To maximize the number of features and levels that can be tested—without overtaxing your respondents—we use discrete choice

We pinpoint the optimal combinations of features and levels

We provide Excel models that clients can “game” themselves to experiment with near-optimal combinations

Traditional Conjoint Analysis

This method allows for more robust conjoint utilities based on purchase behavior questions, such as likelihood to purchase, rather than simple choice data