jtbd

Use the format: When [situation], I want to [motivation], so I can [expected outcome]. Derive each element from past user behavior observed in interviews rather than hypothetical preferences, and cover functional, emotional, and social dimensions.

Compute an opportunity score by subtracting satisfaction from importance for each outcome. High importance with low satisfaction signals an underserved outcome, and users with the job but no solution indicate non-consumption opportunities.

Hiring describes why users start using a solution, detected through phrases like "I switched because". Firing describes why they stop, detected through phrases like "I was frustrated by". Both reveal the causal forces behind product choice.

JTBD mapping derives from user research such as interviews, observations, or support data. Without real research inputs, job statements become hypothetical and lose the past-behavior grounding that makes the framework reliable.

Discovered jobs, hiring and firing criteria, and underserved outcomes feed downstream assumption testing, opportunity solution trees, and service checks. The opportunity scores help teams prioritize which jobs to address first.