Develop buyer hypotheses from your product inputs, then test them with real customers.
Last updated: September 2026
A broad persona can hide important differences in needs, buying roles and constraints. Start by writing down what you know and what you are assuming.
BuyerIQ helps organize questions about potential customers, pricing and alternatives. Only customer research and observed outcomes can establish whether those hypotheses hold.
Generated 0–100 compatibility scores help organize hypotheses. They are not purchase probabilities, survey responses or measured conversion rates.
Assumption-based annual market scenarios with geography, units and arithmetic to review. Verify inputs and acquisition capacity before using them in a pitch.
Potential competitors and positioning questions. Unsupported facts, dates and prices need verification; model knowledge is not live research.
A proposed starting audience based on needs and available context. For B2B, examine roles, company size, buying committees and procurement constraints.
Illustrative pricing scenarios, assumptions and suggested experiments. Generated prices do not establish demand or willingness to pay.
Suggested channels, messages and validation steps to test before committing a larger budget. Report depth depends on your plan.
Provide product details, pricing and audience assumptions. Separate user-reported observations and dated sources from guesses.
A language model generates structured hypotheses from your inputs. It does not survey respondents or simulate measured purchases. Generation time varies.
Review evidence labels and assumptions, test in the field, and add observations to inform a new immutable company version. Prior versions remain a record, not proof of accuracy.
Founders and teams planning customer discovery for a new product or category. Use the report to decide what to investigate, not as a substitute for evidence that people will buy.
Review generated hypotheses and a field-validation plan. There is no published product-specific predictive accuracy benchmark; calibration using real, consented outcomes remains future work.
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