Data Analysis for Marketing Intelligence

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Goals: 

segment the customer base using the main statistical techniques.

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Techniques presented:

multiple regression, logistic regression, multivariate statistical analysis for classification (discriminant analysis, decision trees) and for perceptual mapping and preference analysis (factor analysis, conjoint analysis, multidimensional scaling)._cc781905 -5cde-3194-bb3b-136bad5cf58d_ 

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Tutorials:

There are exercises for each of the topics covered.

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Prerequisites: 

attendance of the INTRO course or having an intermediate knowledge of Statistics for Data Analysis is a prerequisite.

 

Subjects:

Predictive and interpretative models

• Forecasting a quantitative phenomenon

  • Simple linear regression

  • The assumptions of the regression model

  • Multiple linear regression

  • Analyze diagnostics

  • Introduction to the generalized linear model

  • Introduction to nonlinear models

• Prediction of a qualitative phenomenon

  • Logistic regression

  • Introduction to loglinear models

 

Segmentation models

• The interdependence between the variables

  • The distances

• The cluster analysis

  • Hierarchical, k-means and two-step methods

• Neural networks (outline)

  • Self Organizing Map (Kohonen)

 

Classification models

• The discriminant analysis

• Decision trees

  • Chaid method and C&RT method

  • Stopping rules

  • Risk estimation

  • Model validation

  • Profits, misclassification costs, prior probabilities.