Analytics

Often researchers look at the surface of data. They run cross-tabs, filter the data for certain sub-groups, run means, summary tables, charts and so on. While these are obviously very useful ways to interpret survey results, they are sometimes not sufficient to fully address the research objectives. Panalytics applies advanced analytical techniques to help our clients meet their research objectives. These techniques include:

Factor Analysis
Cluster Analysis
Correspondence Analysis / Perceptual Mapping
Conjoint Analysis / Discrete Choice Modeling / Inertia Model
Trend / Forecasting / Time Series Analysis
Penalty-Reward
TURF Analysis
Discriminant Analysis
All Subsets Regression Analysis
Logistic Regression Analysis
Structural Equation Modeling
RFM Analysis
Decision Tree Models

Analytics

Please contact us for more information about these and other analytical tools.