What is a Logit Model?
- Content Type:
- Glossary
Logit Model Definition
A version of regression analysis using an S-shaped curve instead of a straight line. Used when responses are binary, e.g., yes/no, rather than continuous numbers.
A logit model in marketing research is a statistical technique used to predict the probability of a particular outcome or choice based on one or more independent variables. It is commonly applied in binary or multinomial choice modeling, such as predicting purchase decisions, brand selection or customer churn.
What are key characteristics of a logit model in marketing research?
- Predicts probabilities of categorical outcomes.
- Handles binary (yes/no) or multinomial choices.
- Uses a logistic function to model relationships.
- Coefficients indicate the influence of predictor variables.
- Does not assume a linear relationship between variables and outcome probabilities.
- Suitable for both behavioral and attitudinal data.
Why is a logit model important in market research?
A logit model is important because it allows researchers to understand the likelihood of specific consumer actions and identify the factors that most strongly influence decision-making, enabling targeted marketing strategies.
Who relies on a logit model in marketing research?
Data analysts, market researchers, pricing strategists, brand managers, CRM specialists and product development teams rely on logit models for predictive insights and decision support.
How do market researchers use logit models?
Market researchers use logit nodels to analyze consumer choice behavior, estimate the probability of purchase under different conditions, assess brand-switching patterns, evaluate the impact of price changes and forecast customer retention or attrition.