Overview

Logistic regression models the relationship between a binary1 outcome (e.g., customer or non-customer, or nearly anything with a yes or no interpretation) and, typically, several explanatory variables.2 It can identify and estimate the effects of actions to increase or decrease the size or proportion of the group of paramount interest. It can also predict each case's probability of belonging to one group rather than another.

Many academic researchers consider it "the standard way to model binary outcomes" (Gelman & Hill, 2009, p. 79), possibly "dominating all other methods in both the social and biomedical sciences" (Allison, 2015). However, evidence indicates that market researchers do not use it extensively to analyze survey data, despite a client need across service lines (e.g., customer experience monitoring, brand health monitoring, concept testing, advertising testing, political polling) to understand how two groups differ, often a necessary step toward identifying effective actions for increasing or decreasing a key group's size or proportion. The evidence includes reviews of journal articles,3 conference papers, and presentations and personal communication with more than 125 current or former employees4 (mainly, marketing scientists, data scientists, and methodologists but also chief executive officers, salespeople, and others) from 11 of the 15 largest global market research agencies.5