Goodbye and good luck!

Keuzemodellering voor marketing in R

Elea McDonnell Feit

Assistant Professor of Marketing, Drexel University

Choices in building models

  • Which attributes to include
  • Treating numeric attributes as factors
  • Interactions between attributes
  • Interactions between attributes and decision-maker characteristics
  • Hierarchical models
  • Correlations between coefficients
Keuzemodellering voor marketing in R

Other choice model features

  • Distributions of random coefficients
  • Probit models
  • Nested logit
  • Bayesian choice models (using the bayesm package or Stan)
Keuzemodellering voor marketing in R

Advice for building models

  • Always start by computing choice counts to summarize the data
  • Build up from simple models to more complex
  • If estimated parameters have very large standard errors, then you've probably added too much model complexity. Back up to a simpler model.
  • For models describing human behavior, heterogeneity is usually a good idea
Keuzemodellering voor marketing in R

Go fit some choice models!

Keuzemodellering voor marketing in R

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