Socratic Analytics

A division of Socratic Technologies, Inc.

Overview of Services

Socratic Analytics specializes in utilizing advanced statistical consulting and applied mathematical techniques to provide our clients with the most insightful and actionable research findings. The Socratic Analytics team is comprised of mathematicians, PhD statisticians, and market researchers - all with years of broad-based, diverse experience. The result is our ability to interpret your business objectives, recommend the appropriate statistical and / or applied mathematical techniques and provide data-driven results from that modeling in an actionable and understandable format.

Socratic Analytics services and deliverables include:

Professional recommendations for the proper statistical / applied mathematical techniques required to:
- Meet research objectives; and
- Incorporate proper structure for multivariate techniques when appropriate
Data analysis that translates results from advanced multivariate techniques into actionable, understandable findings - in a variety of software formats (PowerPoint, Word, etc.)
Raw data expertly structured in SPSS, SAS, Excel or other formats as needed

Examples of techniques employed:

Regression (Linear, Logistic, Latent Class, Log linear, etc.)
Discrete Choice / Conjoint
TURF/NCURA
Perceptual / Correspondence Mapping
Structural Equations
CHAID
Configurator Analysis^TM
Associative Networks
Optimization Techniques (Differential Calculus, Linear Algebra, etc.)
Many more

Case Study

Recently, a well-known international company came to us with the results from a study they had conducted. Although the majority of the findings were worthwhile and very well received, certain segmentation data appeared to contain contradictory and confusing results. Through the use of advanced multivariate techniques, we were able to determine in a short period of time that some of the key drivers influencing the category had not been identified and were missing from the analysis. By studying the results from focus groups that had already been conducted by the company, we found evidence regarding the nature of the missing variables. With a very short follow-up survey, which included the new variables, the explanation of variance in the model was dramatically increased and the segmentation model became far more understandable and useful-providing management with a clear opportunity for differentiation and unique messaging.