Concept testing for various commercial model offerings of new AI feature
Industry: Large online recruitment and social networking site continually rolling out new product offerings
Goal: Identify the most palatable approach for new AI feature pricing while validating the new tool’s value proposition
Methods: Qualitative interviews explaining complex pricing models
Problem
Value Proposition Uncertainty: The client needed to determine if enterprise-level recruiters perceived their new AI-powered resourcing tools as "must-have" features or merely "nice-to-have" additions.
Pricing Complexity: There was significant ambiguity regarding which pricing structures (e.g., flat fees, per-seat, or usage-based) would align with enterprise budgets and how to justify an increased spend on the platform.
Impact
Strategic Pricing Recommendations: Delivered a comprehensive report providing clear direction on how to position the AI tools and which specific pricing formats would minimize friction during the sales process.
Optimized Revenue Model: Identified the high-value product elements that users were most willing to pay a premium for, allowing the client to align their product development and marketing efforts with maximum revenue potential.
my role
Monetization Deep-Dives: Conducted sessions with power users to "stress-test" various complex pricing models, walking them through specific scenarios to uncover the logic behind their willingness to pay.
Qualitative Parsing: Systematically analyzed user feedback to differentiate between features that drove high perceived value and those that were seen as secondary, identifying the specific "hooks" that incentivized budget expansion.