Advanced Analytics

We have a large analytics toolbox which we apply on a customized basis to meet your needs. We know the options. Our teams are educated on basic and advanced analytics that will sharpen the value of the solutions we provide. Our Chief Data Scientist works side-by-side with our Researchers and Strategists to ensure optimal use of statistics, modeling, and other advanced analytics tools as we process your data and begin shaping strategies to move your organization forward.

〉Complex Causal Relationship Mapping

To uncover relationships between human values and choices, we map interconnected relationships so you can better understand how your customers or stakeholders make choices. These maps also measure the degree to which contributing elements impact one another, and your desired outcome.

Choice Modeling
PATH Modeling
Structural Equation Models (SEM)
Mix Models

〉Strategic Pricing Solutions

Our advanced research methodologies reveal critical insights about price sensitivity, value perception, and purchase behavior—but we don’t stop there. We transform these insights into clear, actionable pricing strategies that drive revenue growth and strengthen market position. Through proven approaches like choice-based conjoint analysis, Gabor-Granger modeling, and other sophisticated methodologies, we help clients bridge the gap between market intelligence and profitable pricing decisions.

Gabor-Granger Analysis
Van Westendorp Price Sensitivity Meter
Maximum Difference Scaling (MaxDiff) with Pricing Analysis
Choice-Based Conjoint Analysis

〉Audience Segmentation

Segmentation is a means to divide the market into different meaningful groups that share similarities within the group but show differentiating characteristics when compared to other groups. A variety of techniques have been developed to accomplish segmentation models, including statistical methods (like latent class analysis) as well as computational methods (like neural networks and random forests).

Cluster Analysis

–Latent Class

–Two-step Cluster

–K-Means, K-Mode, K-Prototype

–Additional Partition, Tree, or Centroid-Based Machine Learning Models

Classification Analysis

–Naïve Bayes

–Multinomial Logit

–Discriminant Analysis

–ML Models (Random Forest, KNN and SVM)

〉Marketing Science Techniques

Correlation + ANOVA

Multivariate Regression

Driver Analysis

Predictive Modeling

Factor Analysis

Correspondence Map

Expectancy Analysis

MaxDiff Analysis

TURF Analysis

CHAID / CART

Price Elasticity

Market Sizing

Volumetric Forecasting