Customer Interview Synthesis Expert

Overview

Turn customer interview transcripts into actionable product opportunities. This skill takes raw question-and-answer transcripts and produces three artifacts: (1) themed insight clusters, (2) an opportunity solution tree mapping outcomes to opportunities and candidate solutions, and (3) a prioritized list of follow-up questions to close evidence gaps.

The synthesis approach is grounded in Teresa Torres' Continuous Discovery Habits (opportunity solution trees), Steve Portigal's interview methodology (looking for stories and contradictions), and the Jobs-To-Be-Done synthesis approach popularized by Alan Klement (situation-motivation-outcome decomposition).

Core Capabilities

• Snippet extraction & coding — stories, contradictions, surprises, emotions coded by need/job/pain/gain and evidence strength
• Theme clustering — evidence thresholds (>=3 snippets from >=2 participants) with scannable headlines
• Opportunity solution tree — measurable outcome → customer-side opportunities → candidate solutions, with intact evidence trails
• Follow-up generation — story-prompt questions targeting weak-evidence themes and unmapped assumptions
• Multi-format output — markdown, JSON, mermaid, confluence, notion, linear

When to Use

• Post-interview synthesis -- You have 3-20 interview transcripts and need to extract themes before they become stale.
• Opportunity space mapping -- Building an opportunity solution tree before committing to solutions.
• Discovery sprint readout -- Sharing findings with the product trio (PM, Design, Engineering) and stakeholders.
• Evidence gap analysis -- Identifying which assumptions still lack interview evidence and need targeted follow-ups.

When NOT to Use

• Quantitative survey synthesis -- use a data analysis skill instead.
• Usability test debriefs -- use a UX research-specific workflow.
• Sales call analysis for win/loss -- use business-growth/ skills.

Clarify First

Before synthesizing, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• The transcripts — how many (3-20) and their quality (synthesis is bounded by interview quality; thin input yields thin themes)
• Target outcome — the measurable outcome that becomes the root of the opportunity solution tree
• Evidence threshold — what counts as a theme (default ≥3 snippets from ≥2 participants; raising/lowering it changes which clusters surface)
• Output consumer — markdown / mermaid / Notion / Linear (sets the --format and shape of the deliverable)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Quick Start

python scripts/interview_synthesizer.py --input interviews.json --format markdown --output synthesis.md
python scripts/interview_synthesizer.py --input interviews.json --format mermaid   # tree only

Prepare input per assets/interview_input_template.json (one entry per interview: participant id, role, q/a pairs). See the references for the full framework and flag reference.

References

Load the reference that matches the task — keep this file lean and pull detail on demand:

• references/synthesis-framework-and-tooling.md — the full 5-step framework (snippet extraction, coding, theme clustering, opportunity solution tree, follow-ups), end-to-end workflow, troubleshooting, success criteria, and the interview_synthesizer.py flag reference. Read when doing the synthesis or running the tool.
• references/synthesis-methodology-guide.md — full methodology with worked examples bundling Torres, Portigal, and Klement. Read for the deeper "why" and end-to-end playbook.
• references/red-flags.md — concrete bad-vs-good examples of how synthesis output goes wrong and how to fix it. Read before sharing themes, a tree, or a readout.
• assets/interview_input_template.json — JSON schema for interview transcripts.
• assets/opportunity_tree_template.md — editable opportunity solution tree template.

Scope & Limitations

In Scope:

• Qualitative synthesis of 3-20 customer interview transcripts
• Theme clustering with evidence thresholds
• Opportunity solution tree generation (Mermaid + Markdown)
• Follow-up question generation for evidence gaps
• Multi-format output (Markdown, JSON, Mermaid, Confluence, Notion, Linear)

Out of Scope:

• Live interview facilitation -- this skill works on completed transcripts
• Quantitative analysis (survey statistics, click-stream data)
• Sentiment scoring via ML -- the tool uses deterministic keyword and code matching only
• Win/loss analysis -- use business-growth/ skills
• Persona generation -- the output is opportunity-centric, not persona-centric

Important Caveats:

• Synthesis quality is bounded by interview quality. Garbage in, garbage out.
• The opportunity solution tree is a thinking aid, not a roadmap. Solutions still need experiment validation.
• Teresa Torres' methodology assumes continuous discovery (weekly touchpoints). One-off interview rounds produce shallower trees.

Integration Points