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MKT training

Data Analysis for Marketing Intelligence

Goals

segment the customer base using the main statistical techniques.

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_ 

Tutorials:

There are exercises for each of the topics covered.

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.

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