Deep dive into ACBC, HB, and other advanced conjoint methods with real-world case studies and implementation guidelines from 15+ years of conjoint expertise.
Conjoint analysis has evolved dramatically over the past decade, with advanced methodologies like ACBC (Adaptive Choice-Based Conjoint) and Hierarchical Bayes estimation delivering unprecedented accuracy in preference modeling and market simulation.
This white paper synthesizes insights from 200+ conjoint studies, providing practical guidance for selecting, implementing, and optimizing advanced conjoint methodologies for maximum business impact.
Adaptive Choice-Based Conjoint represents the gold standard for complex preference modeling. Unlike traditional CBC, ACBC adapts to each respondent's preferences, creating personalized choice scenarios that mirror real-world decision-making processes.
Hierarchical Bayes (HB) estimation has revolutionized conjoint analysis by enabling individual-level utility estimation with remarkable stability and predictive accuracy. This approach is particularly valuable for:
Client: Global smartphone manufacturer launching flagship device
Challenge: Optimize 18 key features across multiple price points while predicting market share against 12 competitor devices
Methodology: Advanced ACBC with HB estimation, incorporating realistic competitive scenarios and brand interaction effects
Key Findings:
Business Impact: Launch achieved 19.2% market share (96% prediction accuracy), generating $2.1B revenue in first year
Integrate conjoint utilities with elasticity curves for real-time pricing optimization across product portfolios
Cross-category cannibalization modeling for comprehensive portfolio impact assessment
Combine conjoint utilities with behavioral economics principles for enhanced prediction accuracy
Get the full 32-page analysis including detailed methodology comparisons, implementation checklists, and statistical validation frameworks.