Use this skill when conducting customer research - designing surveys, writing interview guides, performing NPS deep-dive analysis, interpreting behavioral analytics (funnels, cohorts, retention), or building data-driven user personas. Triggers on "create a survey", "interview script", "NPS analysis", "user persona", "behavioral analytics", "customer segmentation", "voice of customer", "churn analysis", "jobs to be done", or "research plan".
Use this skill when conducting customer research - designing surveys, writing interview guides, performing NPS deep-dive analysis, interpreting behavioral analytics (funnels, cohorts, retention), or building data-driven user personas. Triggers on "create a survey", "interview script", "NPS analysis", "user persona", "behavioral analytics", "customer segmentation", "voice of customer", "churn analysis", "jobs to be done", or "research plan".
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Customer Research
Customer research is the systematic practice of understanding who your customers are,
what they need, and how they behave. It combines qualitative methods (interviews,
open-ended surveys) with quantitative methods (NPS, structured surveys, behavioral
analytics) and synthesis techniques (persona building, segmentation, journey mapping).
This skill equips an agent to design research instruments, analyze collected data, and
produce actionable artifacts like personas, insight reports, and research-backed
recommendations.
When to use this skill
Trigger this skill when the user:
Wants to design a customer survey or questionnaire
Needs an interview guide, script, or recruiting screener
Asks to analyze or interpret NPS (Net Promoter Score) data
Wants to set up or interpret behavioral analytics (funnels, cohorts, retention)
Needs to build, refine, or validate user personas
Asks about customer segmentation or Jobs To Be Done (JTBD) frameworks
Wants to synthesize qualitative data (affinity mapping, thematic analysis)
Needs a research plan or study design for a product initiative
Do NOT trigger this skill for:
Market sizing, competitive analysis, or pricing strategy (market research, not customer research)
A/B testing or experimentation design (product experimentation, not research)
Key principles
Research question first - Every research activity starts with a clear question.
"What do we want to learn?" comes before "What method should we use?" A survey
without a research question produces data without insight.
Triangulate methods - Never rely on a single source. Combine qualitative
(interviews, open-ended responses) with quantitative (surveys, analytics) to
validate findings. What people say they do and what they actually do often diverge.
Bias awareness - Every method introduces bias. Surveys have response bias and
question-order effects. Interviews have interviewer bias and social desirability.
Analytics miss intent and context. Name the bias, design around it, caveat findings.
Sample matters more than size - A well-recruited sample of 8 interview
participants produces better insight than a poorly targeted survey of 1,000.
Define the target population, screen rigorously, aim for representation over volume.
Actionability over thoroughness - Research that does not change a decision is
wasted effort. Every deliverable should answer: "What should we do differently
based on this?" If the answer is nothing, the research question was wrong.
Core concepts
Research methods spectrum - Methods range from qualitative (rich, small-n,
exploratory) to quantitative (structured, large-n, confirmatory). Qualitative
methods (interviews, diary studies, contextual inquiry) generate hypotheses.
Quantitative methods (surveys, analytics, NPS) test them. The best research
programs cycle between the two.
Voice of Customer (VoC) - The aggregate understanding of customer needs,
expectations, and pain points across all channels - support tickets, survey
verbatims, interview transcripts, reviews, social mentions. VoC is an ongoing
program, not a one-time project.
Jobs To Be Done (JTBD) - A framework that reframes needs as "jobs" customers
hire products to do. Format: "When [situation], I want to [motivation], so I can
[outcome]." This prevents feature-driven thinking and keeps research anchored to
outcomes.
Research operations (ResearchOps) - The infrastructure layer: participant
recruitment panels, consent and privacy workflows, data repositories, insight
libraries. Without ResearchOps, each study starts from scratch and insights
get lost between teams.
Common tasks
Design a customer survey
Start with the research question - what decision will this survey inform? Structure:
Screener questions (1-3) - Filter out non-target respondents early
Warm-up questions (1-2) - Easy, non-threatening questions to build engagement
Core questions (5-10) - The questions that answer the research question
Demographics (2-4) - At the end, not the beginning (reduces drop-off)
Key rules: one concept per question, avoid leading language, use 5-point Likert
scales for attitudes, randomize option order, limit open-ended questions to 2-3,
target 5-7 minutes completion time (12-15 questions max).
See references/surveys.md for question type catalog, scale design, and distribution.
Create an interview guide
Structure a 45-60 minute semi-structured interview in five blocks:
Core exploration (25 min) - Open-ended deep-dive on the research topic
Reactions (10 min) - Show prototypes or concepts if applicable
Wrap-up (5 min) - "Anything else?", next steps, thanks
Technique rules: ask "how" and "why" not "do you"; use "tell me about a time
when..." for behavioral recall; use the 5-second silence technique after answers;
never suggest answers or finish sentences; record verbatim quotes.
See references/interviews.md for the full protocol and analysis framework.
Conduct NPS deep-dive analysis
NPS asks: "How likely are you to recommend [product]?" on a 0-10 scale.
Promoters (9-10), Passives (7-8), Detractors (0-6). NPS = %Promoters - %Detractors.
Go beyond the top-line score:
Segment by cohort - NPS by tenure, plan tier, use case, geography
Analyze the follow-up - The open-ended "why" is where the insight lives
Track trends - Monthly/quarterly trends matter more than any single score
Cross-reference behavior - Do Promoters refer? Do Detractors churn?
Close the loop - Contact Detractors within 48 hours; understand Passive blockers
See references/nps-analysis.md for scoring methodology, benchmarks, and coding.
Analyze behavioral analytics
Define key behavioral metrics for a product:
Activation - What action signals a user "gets it"? (e.g., created first project)
Engagement - What does healthy usage look like? (DAU/MAU ratio, session frequency)
Retention - Cohort retention curves: Day 1, Day 7, Day 30 benchmarks
Funnel analysis - Map the critical path and measure drop-off at each step
Feature adoption - Which features correlate with retention? (correlation, not causation)
Behavioral analytics answers "what" and "how much" but never "why." Always pair
with qualitative methods to interpret observed patterns.
See references/behavioral-analytics.md for metrics frameworks and cohort analysis.
Build user personas
Personas are archetypes synthesized from real data - not fictional characters from
a workshop. Process:
Gather data - Combine interview transcripts, survey responses, analytics segments
Deliverables - What artifacts will be produced (personas, report, recommendations)
Stakeholders - Who needs the findings and in what format
Anti-patterns / common mistakes
Mistake
Why it's wrong
What to do instead
Starting with the solution ("Do you want feature X?")
Confirmation bias - users agree to please you
Start with the problem space; let solutions emerge from patterns
Surveying without a research question
Produces data without insight; analysis becomes fishing
Define the decision the survey informs before writing questions
Using NPS as the only customer metric
NPS measures sentiment, not behavior; it is lagging and blunt
Combine NPS with behavioral metrics, CSAT, and qualitative feedback
Recruiting only power users
Survivor bias - misses churned and non-adopters
Recruit across segments including lapsed and churned users
Creating personas from assumptions
Personas without data reinforce existing biases
Ground every persona attribute in observed research data
Asking leading questions
"Don't you think X is frustrating?" always gets agreement
Use neutral, open-ended phrasing: "Tell me about your experience with X"
Ignoring small sample findings
5 interviews surfacing the same pain point is a strong signal
Qualitative validity comes from pattern saturation, not sample size
Gotchas
Recruiting only current, happy customers - If your interview panel is drawn from NPS promoters or customers who accepted a meeting invite, your research systematically misses churned users, non-adopters, and detractors. These are often the most informative participants. Explicitly recruit across churn status, tenure, and engagement level.
Survey question order creates priming effects - Asking "How satisfied are you with our support?" immediately before "How likely are you to recommend us?" artificially inflates NPS. Question order changes answers. Randomize sections where possible, and never put evaluative questions before attitude questions they could bias.
Treating qualitative saturation as a sample size problem - Researchers often keep interviewing because they feel "n=8 isn't enough." In qualitative research, you stop when new interviews stop producing new themes - typically 5-8 for a focused topic. More interviews after saturation waste time and produce diminishing returns.
Behavioral analytics without a prior hypothesis - Starting with "let's look at the data and see what's interesting" produces confirmation bias and analysis paralysis. Define a specific behavioral question before opening the analytics tool: "Do users who complete onboarding step 3 within 7 days retain better at Day 30?"
Personas with invented attributes - Personas built in a workshop from team assumptions rather than research data are archetypes of bias, not customers. Every persona attribute (goals, pain points, behaviors) must trace back to an observed data point. If you cannot cite the source, remove the attribute.
References
For detailed methodology on specific research techniques, read the relevant file
from references/:
references/surveys.md - Question types, scale design, sampling, distribution.
Load when designing or reviewing a survey.
references/interviews.md - Full interview protocol, recruiting, consent, thematic
analysis. Load when planning or analyzing interviews.
references/behavioral-analytics.md - Metrics frameworks (AARRR, North Star),
cohort analysis, funnel design. Load when setting up or interpreting analytics.
references/personas.md - Persona template, affinity mapping, validation checklist,
worked example. Load when building or refining personas.
Only load a references file if the current task requires it.
Companion check
On first activation of this skill in a conversation: check which companion skills are installed by running ls ~/.claude/skills/ ~/.agent/skills/ ~/.agents/skills/ .claude/skills/ .agent/skills/ .agents/skills/ 2>/dev/null. Compare the results against the recommended_skills field in this file's frontmatter. For any that are missing, mention them once and offer to install:
