Multinomial Logit Tool

Understand Consumer Choice Behavior

Model how customers choose between competing alternatives. Quantify the impact of price, features, and brand on purchase decisions to optimize your product portfolio and pricing strategy.

Coefficient Estimates Price sensitivity & feature effects

Choice Probabilities Predict market share by scenario

Model Fit Statistics AIC, Log-Likelihood

Utility Equations Ready for optimization tools

P(choice = j) = exp(V_j) / Sum(exp(V_k))

Multinomial Logit Choice Probability

Where:
V_j = Utility of alternative j
= Intercept + B₁(Price) + B₂(Feature) + ...

$ Key Benefits

Price Optimization: Quantify price sensitivity to find revenue-maximizing prices
Feature Prioritization: Identify which product features drive choice most
Competitive Analysis: Understand how your brand compares to alternatives
Market Share Simulation: Predict share under different scenarios
Segment Targeting: Identify customer segments with distinct preferences

1% Price Improvement = 11% Profit Increase

*Average across industries (McKinsey)

: Common Applications

Consumer Packaged Goods

Analyze scanner panel data to understand brand switching and price promotion effects

Conjoint Analysis

Estimate part-worth utilities from choice-based conjoint experiments

Transportation & Logistics

Model mode choice decisions between car, transit, bike, and walking

Subscription & SaaS

Optimize pricing tiers and feature bundles to maximize conversions

# Tool Features

Long & Wide Format Support Dynamic Variable Builder Alternative-Invariant Coefficients Alternative-Specific Coefficients Reference Level Selection Intercept Options Data Filtering Exportable Equations R Script Generation Powered by mlogit Package

Ready to Model Consumer Choice?

Dr. Koray Cosguner

Founder & Principal Consultant
Kelley School of Business

miaow.consulting@gmail.com

koraycosguner.github.io/miaow-consulting