Why choice modeling?

Choice Modeling for Marketing in R

Elea McDonnell Feit

Assistant Professor of Marketing, Drexel University

Regression modeling relates predictors to numeric outcomes

A linear regression model is used to predict a number.

In marketing, we might use a linear regression to understand how how the sales at a store are related to the features of that store. Sales is a number.

Many events we want to understand and predict are choices

Selecting a dress for a special occasion from an online retailer
Choosing what to watch on a video streaming service
Buying a car

Choices require their own special type of regression

Multinomial logistic regression or the multinomial logit model is used to predict a choice from a set of alternatives. The prediction is based on the features of each alternative. For instance, we can predict the likelihood of choosing a particular car based on the features of the available cars.

Logistic regression or the logit model is a special case of multinomial logistic regression used to predict binary "yes/no" such as the uptake on a promotional offer.

Marketing applications for choice models

Designing new products

Understand how product features relate to what people will buy

Pricing

Determine how price is related to market share

Merchandising

Measure the effect of a "customer favorite" flag on which product a online shopper chooses

What choices are you interested in analyzing?