opportunity-solution-tree

An opportunity solution tree maps desired business outcomes to customer problems, potential solutions, and experiments. Use it during product discovery to prevent the feature factory anti-pattern by systematically validating opportunities before committing to development.

Start by defining the underlying desired outcome, then generate multiple opportunity options representing customer problems. Brainstorm solutions for the most critical problem, and evaluate them based on feasibility, impact, and market fit to select a proof-of-concept.

Design experiments by defining clear hypotheses and creating experiment plans that test your selected proof-of-concept. This iterative learning approach validates whether your mapped solutions effectively address customer problems and drive your target outcomes.

Select a proof-of-concept by evaluating each potential solution against three criteria: feasibility, impact, and market fit. This structured evaluation ensures you test the solution most likely to validate your product strategy and solve the identified customer problem.

Yes, the opportunity solution tree supports iterative learning through defined experiments. You can continuously refine your product discovery by testing hypotheses, validating solutions, and adjusting your approach based on experimental outcomes and customer feedback.

The feature factory anti-pattern occurs when teams build features without validating underlying problems. Structured product discovery prevents this by mapping outcomes to opportunities, forcing teams to identify customer problems and test solutions before development.