Liqiongyu Lenny Skills Plus Analyzing User Feedback

Analyzing User Feedback

Scope

Covers

• Aggregating and normalizing feedback from multiple channels (support, sales, research, reviews, surveys, usage signals)
• Turning raw feedback into themes with evidence and actionable recommendations
• Identifying friction / reasons users won’t use the product (not just validation)
• Producing a repeatable feedback loop (cadence, owners, and handoffs)

When to use

• “Synthesize our user feedback into themes and actions.”
• “Analyze support tickets / feature requests for the top issues.”
• “Create a voice-of-customer report for in the last .”
• “Summarize churn reasons / cancellation feedback.”
• “Cluster survey open-ends into insights and recommendations.”

When NOT to use

• You need to collect new feedback via interviews (use conducting-user-interviews) or surveys (use designing-surveys); this skill analyzes data you already have
• You need task-based usability evaluation of a specific flow or prototype (use usability-testing)
• You need backlog prioritization as the primary output (use prioritizing-roadmap)
• You need a PRD/spec for a chosen solution (use writing-prds / writing-specs-designs)
• You need retention/engagement metric analysis (quantitative cohort/funnel work) rather than qualitative feedback synthesis (use retention-engagement)
• You only need to respond to individual tickets (support workflow, not synthesis)

Inputs

Minimum required

• Product area / workflow to analyze (or “all product”)
• Time window + volume expectations (e.g., “last 90 days”, “~2k tickets”)
• Feedback sources available (tickets, interviews, sales notes, reviews, surveys, community, logs)
• The decision this analysis should inform (roadmap theme, launch readiness, onboarding fixes, messaging, quality)
• Any segmentation that matters (ICP, persona, plan tier, lifecycle stage)
• Constraints: privacy/PII rules, internal-only vs shareable, deadline/time box

Missing-info strategy

• Ask up to 5 questions from references/INTAKE.md.
• If data access is limited, proceed using a small representative sample and label confidence/limitations.
• Do not request secrets. If feedback contains PII, ask for redacted excerpts or aggregated fields only.

Outputs (deliverables)

Produce a User Feedback Analysis Pack in Markdown (in-chat; or as files if requested):

1. Context snapshot (scope, decision, time window, segments, constraints)
2. Source inventory + sampling plan (what’s included/excluded; why)
3. Taxonomy + codebook (tags, definitions, and coding rules)
4. Normalized feedback table (tagged items; links/IDs if available; no PII)
5. Themes & evidence report (top themes, representative quotes, frequency/severity, confidence)
6. Recommendations (actions, owners/time horizon if known, expected impact, open research questions)
7. Feedback loop plan (cadence, stakeholders, how engineering participates, how insights are stored)
8. Risks / Open questions / Next steps (always included)

Templates: references/TEMPLATES.md

Workflow (8 steps)

1) Intake + decision framing

• Inputs: User context; references/INTAKE.md.
• Actions: Confirm the decision, scope, time window, audience, and constraints. Define what “good” looks like.
• Outputs: Context snapshot.
• Checks: A stakeholder can answer: “What decision will this analysis change?”

2) Inventory sources + define the sampling plan

• Inputs: List of sources + access constraints.
• Actions: Create a source inventory, decide inclusions/exclusions, and pick a sample strategy (random, stratified, top-volume buckets).
• Outputs: Source inventory + sampling plan.
• Checks: Sampling plan covers the highest-volume and highest-risk segments (or explicitly explains why not).

3) First-pass read-through (open coding)

• Inputs: Sampled feedback items.
• Actions: Read/annotate items manually to surface what’s “wrong” and why users struggle or churn. Write raw notes before building categories.
• Outputs: Initial codes/notes + candidate themes list.
• Checks: Notes capture rejection reasons and friction, not just feature ideas.

4) Build the taxonomy + codebook

• Inputs: Initial codes; product context.
• Actions: Define a tagging schema (topic, lifecycle stage, severity, user segment, root cause, sentiment). Write clear tag definitions and rules.
• Outputs: Taxonomy + codebook.
• Checks: Two people could tag the same item similarly using the codebook.

5) Normalize and tag the feedback table

• Inputs: Raw items; taxonomy/codebook.
• Actions: Create a normalized table, tag each item, and capture evidence fields (source, date, segment, verbatim excerpt, link/ID).
• Outputs: Normalized feedback table (tagged).
• Checks: No PII; every row has at least 1 primary theme tag + a severity/impact signal.

6) Synthesize themes + quantify carefully

• Inputs: Tagged table.
• Actions: Summarize top themes, quantify frequency by segment/source, identify severity and “why it happens”, and call out unknowns/bias.
• Outputs: Themes & evidence report with confidence levels.
• Checks: Each theme includes representative evidence (quotes/examples) and is not purely speculative.

7) Translate into actions + learning plan

• Inputs: Themes report; constraints.
• Actions: Convert themes into actions (bugs, UX fixes, comms, product bets) and open questions (what to research next). Tie each action to evidence and expected impact.
• Outputs: Recommendations + learning plan.
• Checks: Recommendations are concrete enough to execute next sprint/quarter (clear owner/time horizon if known).

8) Share out + establish the feedback loop + quality gate

• Inputs: Draft pack.
• Actions: Propose the share-out format (doc + review). Define cadence, owners, and storage (where insights live). Run references/CHECKLISTS.md and score with references/RUBRIC.md. Add Risks/Open questions/Next steps.
• Outputs: Final User Feedback Analysis Pack.
• Checks: Pack is shareable as-is; limitations are explicit; follow-up actions are scheduled.

Quality gate (required)

• Use references/CHECKLISTS.md and references/RUBRIC.md.
• Always include: Risks, Open questions, Next steps.

Anti-patterns (common failure modes)

1. Theme inflation — Creating dozens of micro-themes that obscure the signal. Limit top-level themes to 5-10 and nest sub-themes underneath; if you have 20+ themes, you haven't synthesized yet.
2. Source bias blindness — Treating support tickets and NPS comments as equivalent without accounting for who writes each (complainers vs promoters, power users vs new users). Always note source bias in confidence levels.
3. Counting without context — Reporting “42% of tickets mention onboarding” without severity, segment, or lifecycle context. Frequency alone is misleading; pair counts with severity/impact and segment breakdowns.
4. Copy-paste taxonomy — Reusing a generic tag set (bug/feature/UX/other) instead of building a codebook grounded in the product's actual problem space. The taxonomy should reflect how the team makes decisions.
5. One-and-done report — Delivering a synthesis with no feedback loop plan. Without cadence, owners, and storage, the analysis becomes shelfware within a sprint.

Examples

Example 1 (support tickets): “Analyze the last 60 days of onboarding-related tickets. Output a User Feedback Analysis Pack and top 10 recommended fixes.” Expected: source inventory + sampling, taxonomy, tagged table, themes with quotes, and ranked actions.

Example 2 (survey + reviews): “Synthesize survey open-ends and app store reviews for our new pricing change. What are the biggest friction points and why?” Expected: themes split by source/segment, severity signals, and recommendations (incl. messaging/UX changes).

Boundary example (redirect to conducting-user-interviews): “We have no feedback data yet but want to understand why users churn.” Response: redirect to conducting-user-interviews to collect first-person stories first; return here once you have data to analyze.

Boundary example (redirect to designing-surveys): “We want to collect structured feedback from our user base about the new pricing.” Response: redirect to designing-surveys to design and launch the instrument; return here to analyze the results.

Boundary example (scope guard): “Read all our feedback and tell us what to build next.” Response: ask for scope/time window/decision + a sample dataset; otherwise produce a sampling plan + a minimal first-pass synthesis with explicit limitations.

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