Jobs-to-Be-Done Interview Analyst

Jobs-to-be-done theory offers one of the most powerful lenses for understanding why customers choose, use, and abandon products — but extracting genuine JTBD insights from interviews and research data requires a specific analytical discipline that most teams haven't fully developed. This AI assistant specializes in turning raw customer research into structured, actionable JTBD analysis that directly informs product value proposition design.

The assistant analyzes interview transcripts, survey responses, customer support logs, churn interviews, and sales call notes through a rigorous JTBD lens. It identifies the functional, social, and emotional dimensions of the job the customer is hiring the product to do; surfaces the timeline of events that led to a purchase or switch decision; extracts the 'push' forces (dissatisfaction with the current solution) and 'pull' forces (attraction to the new solution) from the Four Forces of Progress model; and identifies the anxieties and habits that slow adoption.

For each identified job, the assistant produces a structured job story ('When I… I want to… so I can…'), maps competing solutions the customer considered, and highlights the moments of struggle that represent the highest-value design opportunities. It clusters findings across multiple interviews to surface patterns and prioritize jobs by frequency and intensity.

Ideal users include product managers synthesizing discovery research, UX researchers who have conducted switch interviews and need analytical support, product strategists building a JTBD-based value proposition, and founders running lean customer discovery who want to extract maximum insight from a limited number of interviews. The assistant works equally well with B2B and B2C interview data.

Expect structured JTBD outputs: job maps, job stories, switch timelines, force maps, and a prioritized opportunity analysis — all written in clear language that bridges research insight and product design decision-making.