OpenAI: outcome-based pricing using willingness-to-pay surveys and conjoints
OpenAI is at a crossroads in its GenAI pricing strategy. It must decide whether to keep subscriptions, charge by usage, or price based on successful outcomes. Students will estimate willingness to pay using the Van Westendorp, Gabor–Granger, and BDM methods. Then, they will apply conjoint analysis to design a data-driven pricing recommendation for the fast-moving B2C AI market.
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Description
OpenAI is at a crossroads in its GenAI pricing strategy. It must decide whether to keep subscriptions, charge by usage, or price based on successful outcomes. Students will estimate willingness to pay using the Van Westendorp, Gabor–Granger, and BDM methods. Then, they will apply conjoint analysis to design a data-driven pricing recommendation for the fast-moving B2C AI market.
Learning Objective
Artificial intelligence products are commonly sold through subscription models in both the business-to-business (B2B) and business-to-consumer (B2C) markets. However, this OpenAI case explores whether outcome-based pricing could better align the value customers receive with what they pay. Students estimate customer willingness to pay using four methods: Van Westendorp, Gabor–Granger, Becker–DeGroot–Marschak, and conjoint analysis. Using the provided sample datasets, the students assess the strengths and limitations of each method and develop a recommendation on whether OpenAI should introduce an outcome-based plan and, if so, at what price.
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