What problem does it solve?
Conjoint analysis helps uncover how customers trade off product attributes, but building end-to-end workflows from data collection to actionable decisions is complex. This Skill provides a reusable architecture to infer multi-attribute preferences from observed market behavior and translate them into product, pricing, and strategy insights.
Core Features & Use Cases
- End-to-end conjoint workflow covering data acquisition, attribute engineering, experimental design, model estimation, and insight translation (importance, WTP, probability, ROI).
- Supports single full-model or split-submodel estimation to handle correlated attributes and small samples.
- Generates standard deliverables: coefficient tables, attribute importance, WTP, predicted best card, and ROI recommendations.
- Includes ready-made references, scripts to build stacked data, fit models, and compute insights, plus an example Case Study (Safety Glasses) for reproducibility.
Quick Start
Run the workflow on your product data to build stacked observations, fit a split or full logistic conjoint model, and generate the four key insights for decision-making.
