Customer Interview Synthesis Pack
Raw interview notes into evidence-led themes, opportunities, and next research questions.
$12
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Claude Code
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Grok Bot+18 agentsCustomer Interview Synthesis Pack
Example session with this skill installed
Analyze these 3 transcripts. Research question: Why do designers stop using our component library after two weeks? Participants: P01 (Senior), P02 (Junior), P03 (Freelance). Lens: Friction, Trust.
- Read your context and instructions
- Compiled the customer interview synthesis
- Generated the document
I have synthesized the three transcripts provided. The analysis identifies a significant tension between initial setup speed and long-term maintenance trust.
Customer interview synthesis: Designer retention for component library
Research question: Why do designers stop using our component library after two weeks?
Study scope: 3 qualitative interviews with varied seniority levels.
Decision context: Identifying barriers to long-term adoption.
Participants: P01, P02, P03
Generated: 2024-05-22 · Evidence synthesis for human verification — not market proof
1. Executive readout
What the interviews suggest: The library is easy to start with but lacks the flexibility needed for edge cases, leading designers to "break" components and lose sync.
- What they do not establish: Whether this leads to total churn or just selective use of specific atoms.
Most important implication: The "all or nothing" nature of the library causes trust decay when a single component fails a specific use case.
- Evidence confidence: Medium; consistent across 3 varied roles but needs a larger sample to confirm prevalence.
4. Evidence table
| Evidence ID | Participant | Location | Observation / paraphrase | Direct quote | Code(s) |
|---|---|---|---|---|---|
| E1 | P01 | p.2 | Overriding props feels "hacky" and brittle. | “I felt like I was fighting the library instead of using it.” | Friction |
| E2 | P02 | p.4 | Documentation is clear for basics but missing complex states. | “I couldn't find the hover state for the nested dropdown.” | Friction |
| E3 | P03 | p.1 | Fear of library updates breaking custom workarounds. | “I’m scared to pull the latest version because I’ve detached so much.” | Trust |
5. Themes and patterns
Theme 1: The "Detachment Trap"
Pattern: Users start by using components as intended but detach them as soon as a non-standard requirement appears.
- Evidence: E1, E3
- Variation: P02 (Junior) detaches less frequently but expresses more frustration when stuck.
- Interpretation: High initial velocity is offset by a "wall" encountered at the 14-day mark of a project.
8. Opportunity hypotheses
| Priority | Hypothesis | Evidence basis | Smallest validation step | Signal to watch |
|---|---|---|---|---|
| 1 | If we provide a "slot" system for custom sub-components, then designers will detach 50% less often because they won't feel they are "fighting" the library. | E1, E3 | Beta test a "flexible card" component with 5 designers. | Detachment rate in Figma analytics. |
Next steps
- Review the "Detachment Trap" findings with the design systems engineering team.
- Audit the current library for components with the highest detachment rates.
- Schedule follow-up interviews focusing specifically on the handoff between Figma and React.
customer-interview-synthesis-pack.pdf
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What you get
About this skill
Stop making product decisions based on gut feelings or hallucinated customer needs. Raw interview transcripts are often messy, disorganized, and full of noise, making it difficult to extract actionable evidence without bias. This skill provides a rigorous framework for transforming raw qualitative data into structured research synthesis.
What it does
- Evidence extraction identifies specific behaviors, triggers, and workarounds directly from transcripts.
- Systematic coding applies a consistent lens to categorize data into jobs, pains, and objections.
- Pattern matching clusters evidence into themes while explicitly highlighting contradictions and negative cases.
- Opportunity mapping generates testable hypotheses linked to specific participant IDs and quotes.
- Integrity checking ensures every claim is backed by supplied text without inventing market-wide outcomes.
How it works
- Define the frame by providing your research question, study scope, and raw interview text or notes.
- Review the inventory as the agent maps the corpus, identifying completeness and sample caveats.
- Analyze the synthesis through structured tables covering coding, evidence, and tension points.
- Action the hypotheses by using the generated validation steps and next research questions to guide your sprint.
Frameworks & tools
This skill is optimized for qualitative research methodologies including Jobs-to-be-Done (JTBD), thematic analysis, and evidence-led product discovery.
Why this beats prompting it yourself
Standard prompts often produce generic summaries that lose the nuance of participant quotes or ignore evidence that contradicts the main narrative. This skill enforces a strict separation between direct evidence and interpretation, preventing the AI from claiming product-market fit or inventing ROI metrics from a small sample.
Use cases
- Product discovery to identify why users are abandoning specific workflows or features.
- Messaging optimization by extracting verbatim triggers and objections from prospect calls.
- Service design to map friction points and current workarounds in existing user journeys.
- Churn analysis to synthesize exit interviews into prioritized opportunity hypotheses.
Known limitations
Does not support audio files directly; requires text transcripts or notes. Accuracy depends on the quality and honesty of the supplied source material.
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