User Feedback Synthesiser

user-feedback-synthesiser

Agent: UX Researcher

L3 UX researcher (Nx) responsible for user feedback synthesis, session analysis, and feeding research findings back into the product and design cycle.

Department ethos: ideal-design.md

Skill Description

Synthesises qualitative user feedback from research sessions, surveys, and interviews into thematic clusters that reveal user needs, pain points, and opportunities for design improvement.

When to Use

• When a batch of user interviews, survey open-text responses, or feedback entries has been collected and needs to be distilled into actionable themes.
• When the design team needs a current-state summary of user sentiment around a specific product area before starting a design project.
• When multiple feedback sources (support, research, sales) are saying overlapping things and a unified synthesis would prevent duplicated effort.

Workflow

1. Data Preparation: Gather all qualitative feedback inputs, normalise formats, and de-identify where necessary. Remove duplicate entries and out-of-scope feedback. Deliverable: cleaned feedback corpus.
2. Open Coding: Read through all feedback entries and apply open codes (short descriptive labels) to each meaningful segment. Use in-vivo codes (participant's own words) where possible. Deliverable: coded feedback dataset.
3. Affinity Clustering: Group related codes into thematic clusters using affinity diagramming. Name each cluster with a descriptive theme label. Deliverable: affinity diagram with theme labels.
4. Theme Refinement: Review clusters for coherence. Split themes that are too broad, merge those that overlap, and ensure each theme is supported by evidence from multiple sources or participants. Deliverable: refined theme set with supporting evidence counts.
5. Synthesis Report: Write a synthesis report presenting each theme with a summary, representative verbatim quotes, frequency/intensity indicators, and design implications. Deliverable: feedback synthesis report.

Anti-Patterns

• Premature categorisation: Starting with predefined categories and forcing feedback into them instead of letting themes emerge from the data. Why: top-down categorisation suppresses unexpected themes that may be the most valuable findings.
• Quote stripping: Presenting themes without verbatim user quotes. Why: quotes are the evidence that makes themes credible and give stakeholders direct empathy with user experience.
• Frequency-only prioritisation: Ranking themes solely by how often they appear without considering intensity or business impact. Why: a low-frequency, high-severity issue (e.g., data loss) can be more critical than a high-frequency, low-severity annoyance.
• Single-source synthesis: Synthesising from only one feedback channel and presenting it as a complete picture. Why: each channel has selection bias (e.g., support hears from frustrated users); cross-channel synthesis provides a more balanced view.

Output

On success: Produces a feedback synthesis report containing refined themes, representative quotes, frequency and intensity indicators, and design implications per theme. Delivered as a shared document for the design and product teams.

On failure: Report which data sources could not be included (e.g., access issues, incompatible formats), what partial synthesis was completed, and recommend additional data collection or access requests to complete the picture.

Related Skills

• product-feedback-ingestion-uxr — Provides the structured feedback data this skill synthesises.
• jtbd-mapper — Synthesised themes can inform and validate JTBD identification.
• feedback-loop-formaliser-uxr — Synthesis outputs enter the formalised feedback loop for action tracking.