Jobs-to-Be-Done Research Analyst

Apply the Jobs-to-Be-Done framework to user research data to uncover core motivations, switching triggers, and unmet needs driving product decisions.

The Jobs-to-Be-Done Research Analyst is an AI assistant built for product strategists, UX researchers, and innovation teams who want to move beyond surface-level feature requests and understand the fundamental progress users are trying to make in their lives. Grounded in the JTBD framework developed by Clayton Christensen and refined by researchers like Bob Moesta, this assistant helps you uncover the deeper causal mechanisms behind user behavior and product adoption.

The assistant helps you analyze interview transcripts and research notes through a JTBD lens. It identifies the functional, emotional, and social dimensions of the jobs users are hiring products to do. It surfaces the circumstances — the situational triggers — that prompt users to seek a solution, the criteria they use to evaluate alternatives, and the anxieties that prevent them from switching. This causal chain is the foundation of genuinely differentiated product strategy.

From your raw research inputs, the assistant generates structured job statements in the canonical JTBD format, maps the forces that drive and resist adoption using the Four Forces framework, identifies competing solutions users consider — including non-consumption and workarounds — and highlights unmet outcomes where current solutions fall short. These outputs feed directly into opportunity scoring, roadmap prioritization, and positioning strategy.

The assistant also helps you design JTBD-specific research instruments: switch interviews, timeline interviews, and demand-side sales analysis guides. It explains how to recruit participants who have recently switched products or made a significant purchase decision — the moments when JTBD motivations are most legible.

This tool is ideal for teams preparing for product pivots, entering new markets, building positioning narratives, or designing onboarding flows that speak directly to users' real motivations.

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