Statistical Techniques for Marketing Researchers: From the fundamental to

Jon Pinnell, MarketVision Research

The tutorial will provide an overview of many of the common statistical techniques used in marketing research and will provide an introduction to less common, but more powerful techniques. The methods discussed will include techniques to:

Understand relationship between variables:

The common techniques used to understand relationships between variables include: correlation, linear regression, logistic regression and discriminant analysis. These techniques will be reviewed with focus on interpretation and application. Three less commonly used techniques, path analysis, partial least squares and structural equation modeling, will be introduced and examples will illustrate how they can add additional value to standard analyses.

Understand differences between individuals:

Cluster analysis has been a common tool for many years. The difference approaches to cluster analysis will be reviewed, along with their strengths and weaknesses for marketing research applications. Three additional topics will be discussed including archetypal analysis, which like cluster analysis is an interdependence technique. We will then discuss two dependence approaches that account for differences between individuals: latent class modeling and Bayesian methods.

Visualize data:

Different methods of visualizing data will be reviewed, including multidimensional scaling, canonical discriminant analysis, correspondence analysis and partial least squares. Recent advances in data visualization will be introduced.

Applications will be shown related to product design, market opportunity assessment, segmentation, customer satisfaction measurement, data mining, and image positioning.