Decision Trees (CART and CHAID) and Interactions
Decision trees original use in marketing was to look for interactions among variables in survey research. They are a group of related techniques that find which variables and which levels of those variables do the best job splitting objects into distinct groups based on the dependent variable. In market research objects are generally customers or survey respondents and the two most used decision tree techniques are CART and CHAID.
The case below models which demographic variables do the best job distinguishing between those people who frequently visit fast food restaurants & those that do not.
It is difficult to test for all possible interactions in a multiple regression model and this group of analyses does a good job filling that void. Decision trees also have the advantage that they are robust methods that are relatively insensitive to outliers or the distribution of data.
Marketing questions answered with decision trees:
Univariate approach: Graphically displaying the results of a cross-tabulation or nested cross-tabulation
Decision trees compared to other predictive models:
*Decision trees look at variables hierarchically rather than simultaneously.
*Decision trees are easier for audiences to interpret.
*Decision tree modeling is usually faster.
*Decision trees usually produce a handful of segments (=terminal nodes) each with a given score rather than a list of *customers with individually assigned scores. In other words there are less distinct modeled values when using decision trees.
*Decision trees do not assume that the dependent variable follows any given distribution (they are non-parametric models).
What is the M Squared Group seasoning?
Questions to discuss with M Squared Group