Consumer Intelligence

Jobs to be Done (JTBD)

Jobs to be Done (JTBD)

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Conveo automates video interviews to speed up decision-making.

Definition:

Jobs to be Done (JTBD) is a consumer intelligence framework that explains purchasing and usage behavior through the lens of the functional, emotional, and social progress a person is trying to achieve in a given situation. Rather than asking who the customer is, JTBD asks what they are trying to get done and why existing solutions fall short. Developed by Clayton Christensen and refined by researchers like Bob Moesta, the framework is widely applied in brand positioning, product development, concept testing, and messaging research. In qualitative research, JTBD interviews are designed to surface the specific circumstances, motivations, and trade-offs that drive real consumer decisions, making it a powerful complement to behavioral and attitudinal data.

How Conveo Does It

Conveo supports Jobs to be Done research through AI-moderated video interviews that probe the situational triggers, desired outcomes, and competing alternatives behind real consumer decisions. Teams can launch a JTBD study in under 30 minutes and receive structured findings within days, not weeks. Because every session involves a real participant speaking on camera, not a synthetic respondent, the emotional and contextual signals that JTBD methodology depends on are preserved and analyzable at enterprise scale across hundreds of simultaneous interviews.

Frequently asked questions.
Jobs to be Done is a research framework that explains why people buy or use products by focusing on the progress they are trying to make in a specific situation. Instead of segmenting by demographics or product features, JTBD research uncovers the functional, emotional, and social goals driving a decision. It treats the consumer as someone hiring a product to solve a problem, which produces more actionable insight for product, brand, and messaging teams.
JTBD matters in qualitative research because it shifts the focus from what customers do to why they do it. Standard surveys can capture stated preferences, but they rarely surface the situational context, competing alternatives, or emotional trade-offs that actually drive a purchase. Qualitative JTBD interviews, when conducted well, reveal the specific circumstances and unmet needs that quantitative data misses entirely. That depth is what makes findings credible enough to influence product strategy, positioning, and innovation decisions.
Traditional personas describe who a customer is, typically using demographic and psychographic attributes. Jobs to be Done describes what a customer is trying to accomplish in a specific situation, regardless of who they are. A persona might tell you your buyer is a 35-year-old parent. JTBD tells you they are trying to reduce decision fatigue on a weeknight. The two approaches are complementary, but JTBD tends to produce more actionable insight for product and messaging decisions because it is grounded in behavior and context rather than profile.
AI is making JTBD research faster and more scalable without sacrificing the conversational depth the framework requires. AI-moderated interviews can probe situational triggers, switching moments, and desired outcomes in real time, adapting follow-up questions based on what a participant actually says. This removes the scheduling bottleneck that traditionally limited JTBD studies to small sample sizes. Teams can now run hundreds of JTBD interviews in parallel, analyze patterns across the full dataset automatically, and surface findings in days rather than weeks.
Enterprise teams typically apply JTBD research at key decision points: before a product launch, when repositioning a brand, or when trying to understand why a category is growing or declining. The research usually involves in-depth interviews designed to surface the specific situation that triggered a purchase, the alternatives considered, and the emotional and functional criteria used to decide. Findings are then used to sharpen messaging, identify unmet needs, and prioritize features or product lines that map directly to what consumers are actually trying to accomplish.
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