algo-price-conjointlisted

# Conjoint Analysis ## Overview Conjoint analysis estimates the relative value consumers place on product attributes by analyzing their choices among hypothetical product profiles. Choice-Based Conjoint (CBC) is the most common variant. Produces part-worth utilities per attribute level and derived willingness-to-pay estimates. ## When to Use **Trigger conditions:** - Determining which features drive purchase decisions and how much they're worth - Estimating willingness to pay for specific product features - Optimizing product configuration for a target segment **When NOT to use:** - When you only need an acceptable price range (use Van Westendorp — simpler) - When attributes can't be varied independently (natural constraints) ## Algorithm ``` IRON LAW: Conjoint Results Are Valid ONLY for Tested Attribute Levels Extrapolating beyond tested ranges is unreliable. If you tested prices $10-$50, you cannot predict preference at $100. The utility function is only defined within the experimental design space. ``` ### Phase 1: Input Validation Define: attributes (3-7), levels per attribute (2-5 each), design type (full factorial if small, fractional/D-optimal if large). Survey 200+ respondents minimum. **Gate:** Attributes independent, levels realistic, sample size sufficient. ### Phase 2: Core Algorithm 1. Generate choice sets using experimental design (D-optimal or balanced overlap) 2. Present respondents with sets of 3-4 product profiles, ask to choose preferred 3. Estimat