Analytics

analytics

Often researchers look at the surface of data. They run cross-tabs, filter the data for certain sub-groups, generate means, create summary tables, produce charts and so on. While these are obviously very useful ways to interpret survey results, sometimes we need to dig deeper to truly understand the hidden information in the data.

Henry Ford famously said, “If I had asked people what they wanted, they would have said faster horses.”   Although this may seem like a simplification of why analytics is important, it demonstrates how as researchers we may need to go beyond the obvious to truly uncover what the data is trying to tell us.

Panalytics applies advanced analytical techniques to help clients mitigate their challenges and identify new opportunities.  These techniques include:

  • Factor Analysis –i.e., data reduction into bigger ideas or concepts
  • Cluster Analysis –i.e., consumer segmentation
  • Correspondence Analysis / Perceptual Mapping
  • Conjoint Analysis / Discrete Choice Modeling / Inertia Model –i.e., consumer choices analyses
  • 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

pas·sion·ate

ADJECTIVE

Showing a strong belief.