Advanced Choice Modeling

Conjoint Analysis

Reveal customer preferences and trade-off decisions through sophisticated choice modeling that quantifies the relative value of product features, benefits, and pricing strategies.

Methodology Overview

Understanding Conjoint Analysis Types

Conjoint analysis is a family of advanced statistical techniques that decode how customers make decisions by analyzing their trade-offs between different product attributes, features, and price points. PROOF Insights leverages several types of conjoint analysis to precisely tailor our research to our clients' needs:

Traditional Choice-Based Conjoint (CBC)

Measures relative importance of attributes through systematic presentation of product profiles, revealing utility values for each feature level.

Adaptive Choice-Based Conjoint (ACBC)

Advanced methodology that adapts to individual preferences in real-time, providing more accurate and actionable insights through personalized questioning.

Menu-Based Conjoint (MBC)

Streamlined approach that presents respondents with menu-style options, making complex trade-off decisions more intuitive and engaging.

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Key Applications

Strategic Business Applications

Conjoint analysis provides actionable insights across multiple business scenarios, from product development to pricing optimization and market positioning.

Product Design

Optimize product configurations by understanding which features matter most to different customer segments and their willingness to pay.

Feature prioritization
Optimal configurations
Customer-driven design

Pricing Strategy

Determine optimal pricing by understanding price sensitivity and willingness to pay for different feature combinations.

Price elasticity modeling
Value-based pricing
Revenue optimization

Market Simulation

Predict market share and competitive dynamics through sophisticated choice modeling and scenario planning.

Market share prediction
Competitive scenarios
Launch forecasting

Segmentation

Identify customer segments based on preference patterns and develop targeted strategies for each group.

Preference-based segments
Targeted offerings
Persona development

Brand Positioning

Develop compelling brand positioning by understanding which attributes drive choice and differentiate from competitors.

Differentiation strategy
Value proposition
Competitive advantage

Portfolio Optimization

Optimize product portfolios by understanding complementary and cannibalization effects across different offerings.

Portfolio balance
Cannibalization analysis
Cross-selling opportunities
Our Process

Structured Approach to Choice Modeling

Our systematic methodology ensures accurate, actionable insights that translate directly into strategic business decisions and measurable outcomes.

1

Study Design

Define attributes, levels, and constraints based on business objectives and market realities to ensure meaningful and actionable results.

Attribute identification
Level optimization
Constraint definition
2

Data Collection

Execute sophisticated choice exercises using optimal experimental design and adaptive questioning to maximize information value.

Experimental design
Adaptive questioning
Quality assurance
3

Analysis & Insights

Apply advanced statistical modeling to generate utilities, importance scores, and market simulation tools for strategic decision-making.

Utility estimation
Market simulation
Custom-built simulator tool

Ready to Decode Customer Preferences?

Partner with PROOF Insights to leverage advanced conjoint analysis and ACBC methodologies that reveal customer choice drivers and optimize your product strategy.