Conjoint Analysis

An indirect research method where respondents choose between product configurations with different attributes and prices. Choices are assumed to reveal preferences, allowing statistical estimation of willingness-to-pay for each attribute. The dominant form used in pricing research today is discrete-choice conjoint. The method was developed in consumer-goods market research for buyers who can hold and compare physical products with concrete reference points (price, brand, package size). SPP does not use conjoint analysis in any form. Discrete-choice conjoint is an indirect, hypothetical method — the respondent never pays, so the "realism" is in task design, not consequences. Hypothetical bias is well-documented in the peer-reviewed pricing-research literature, and peer-reviewed field experiments have also documented buyers deliberately selecting suboptimal configurations to avoid revealing high willingness-to-pay. The limits compound in B2B software: stable parameter estimates require hundreds to thousands of respondents, while B2B software buyer panels are typically tens to low hundreds; B2B purchases are multi-stakeholder consensus events, not individual choices on a card; software attributes are intangible until experienced, so stated preference for unfamiliar abstract attributes is unstable; actual prices are negotiated, with no slot in the conjoint task for the negotiation overlay; and conjoint doesn't model salesperson willingness to discount — buyers approach the choice task knowing the displayed price won't be the price they actually pay, which skews their preferences toward higher-priced configurations in ways the model treats as random noise rather than as the systematic anticipation it actually is. SPP works from transaction data, won/lost patterns, customer-group analysis, and structured commercial dialogue — direct/observed methods rather than indirect/hypothetical ones.