customer-research

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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

1. 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.

1. 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.

1. 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.

1. 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.

1. 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:

1. Screener questions (1-3) - Filter out non-target respondents early
2. Warm-up questions (1-2) - Easy, non-threatening questions to build engagement
3. Core questions (5-10) - The questions that answer the research question
4. 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:

1. Introduction (5 min) - Purpose, consent, expectations
2. Context (10 min) - Role, workflow, environment
3. Core exploration (25 min) - Open-ended deep-dive on the research topic
4. Reactions (10 min) - Show prototypes or concepts if applicable
5. 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:

1. Segment by cohort - NPS by tenure, plan tier, use case, geography
2. Analyze the follow-up - The open-ended "why" is where the insight lives
3. Track trends - Monthly/quarterly trends matter more than any single score
4. Cross-reference behavior - Do Promoters refer? Do Detractors churn?
5. 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:

1. Activation - What action signals a user "gets it"? (e.g., created first project)
2. Engagement - What does healthy usage look like? (DAU/MAU ratio, session frequency)
3. Retention - Cohort retention curves: Day 1, Day 7, Day 30 benchmarks
4. Funnel analysis - Map the critical path and measure drop-off at each step
5. 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:

1. Gather data - Combine interview transcripts, survey responses, analytics segments
2. Identify patterns - Affinity mapping to cluster behaviors, goals, pain points
3. Define dimensions - Choose 2-3 differentiating axes (e.g., skill vs. frequency)
4. Draft personas (3-5 max) - Each includes: name/role, key goals, pain points,

behavioral patterns, real verbatim quotes, JTBD statement

1. Validate - Test personas against held-out data; refine until predictive

Personas without behavioral data are stereotypes. Always ground them in observation.

See references/personas.md for the persona template, affinity mapping guide, and validation checklist.

Synthesize qualitative research data

After collecting interview transcripts or open-ended survey responses:

1. Code the data - Tag recurring themes with descriptive codes
2. Affinity map - Group related codes into clusters; name each cluster
3. Identify patterns - Frequency (how often) and intensity (how strongly felt)
4. Build insight statements - "[Observation] because [reason], which means

[implication