# Customer research Human Guide ## What This Is For Provides expert guidance for customer researcher. It gives the agent a clearer input/output frame for competitive research: what context to ask for, what decisions to make, and what usable artifact to return. Use this as a human-readable version of the Customer research agent skill. It is meant for marketers, operators, founders, and other non-coders who want the workflow without reading agent-specific implementation instructions. ## When To Use This - Use this when you need a repeatable process for customer research. - Use this when the task needs judgment, examples, constraints, or a clear output format rather than a one-off prompt. - Use this when you want to hand an AI assistant enough context to produce a usable marketing artifact. ## When Not To Use This - Do not use this when you only need a quick factual answer. - Do not use this when the work depends on private data you cannot share with the assistant. - Do not use this as a replacement for legal, compliance, financial, or medical review. ## What You Need Before Starting - The goal or business outcome you want. - The audience, customer segment, or market context. - Any source material the assistant should respect, such as notes, briefs, examples, URLs, or brand guidance. - Constraints such as tone, length, channel, deadline, region, or approval requirements. - A clear definition of what a good final answer should look like. ## Step-By-Step Workflow 1. State the job clearly: "Use the Customer research guide to help me with..." 2. Add context: audience, goal, offer, channel, source material, and constraints. 3. Ask the assistant to identify missing inputs before producing the final output. 4. Have the assistant follow the skill-specific guidance below. 5. Review the result against the final checklist and ask for revisions where needed. ## Skill-Specific Guidance - Extract: pains, triggers, desired outcomes, language used, objections, alternatives considered - Look for: the moment they decided to look for a solution, what they tried before, what success looks like to them - Segment responses by customer tier, use case, or tenure before drawing conclusions - Flag: what open-ended answers say vs. what multiple-choice answers say (they often conflict) - Identify: the 20% of responses that contain the most useful signal - Mine for: recurring complaints, confusion points, feature requests, and "I wish it could…" language - Categorize tickets before analyzing — don't treat all tickets as equal signal - Separate bugs from confusion from missing features from expectation mismatches - Wins: what tipped the decision? What almost made them choose a competitor? - Losses and churn: was it price, features, fit, timing, or something else? - Segment by reason — don't average across different churn causes - Passives and detractors are higher signal than promoters for improvement work ## Decision Points And Nuance The original skill emphasizes: Before Starting, Two Modes of Research, Mode 1: Analyze Existing Assets, Mode 2: Go Find Research, Mode 1: Analyzing Existing Research Assets, Asset Types, Extraction Framework, Synthesis Steps, Research Quality Guardrails, Mode 2: Digital Watering Hole Research. Use these questions to steer the work: - What is the intended audience or buyer? - What source material must be preserved? - What should the assistant optimize for: clarity, persuasion, accuracy, speed, creativity, or conversion? - What examples represent the desired quality bar? - What should the assistant avoid? ## Common Mistakes - Categorize tickets before analyzing — don't treat all tickets as equal signal - Segment by reason — don't average across different churn causes - **Minimum viable sample**: Don't build personas or draw messaging conclusions from fewer than 5 independent data points per segment. - | Verbatim quote | Exact words — don't paraphrase | - Early-stage products (or new categories) lack first-party review data. Don't invent personas — walk outward through proxy sources, in order: - Personas should be built from research, not invented. Don't create a persona until you have at least 5-10 data points (interviews, reviews, or community posts) from a consistent segment. - [Competitor, DIY, do nothing, hire someone] - **Don't name them cutely** ("Marketing Mary") unless your team finds it helpful — it's often a distraction ## Copy-And-Paste Prompt ```text Use the Customer research human guide. My goal: [Describe the business outcome] Audience: [Describe who this is for] Context and source material: [Paste notes, examples, links, or existing copy] Constraints: [Tone, length, channel, timeline, must-include items, must-avoid items] Before producing the final output, ask me for any missing information that would materially improve the result. ``` ## Final Checklist - [ ] The output matches the original goal. - [ ] The audience and context are reflected in the answer. - [ ] Important constraints and source material were preserved. - [ ] The assistant made the relevant decisions explicit. - [ ] The final artifact is ready to use, review, or hand to the next person. ## Source This guide was generated from the coreyhaines31/marketingskills skill entry for `customer-research`. ## Source Skill Notes These notes preserve the nuance from the original skill. Use them as supporting reference when the workflow above feels too generic. # Customer Research You are an expert customer researcher. Your goal is to help uncover what customers actually think, feel, say, and struggle with — so that everything from positioning to product to copy is grounded in reality rather than assumption. ## Before Starting **Check for product marketing context first:** If `.agents/product-marketing.md` exists (or `.claude/product-marketing.md`, or the legacy `product-marketing-context.md` filename, in older setups), read it before asking questions. Use that context to skip questions already answered. --- ## Two Modes of Research ### Mode 1: Analyze Existing Assets You have raw research material (transcripts, surveys, reviews, tickets). Your job is to extract signal. ### Mode 2: Go Find Research You need to gather intel from online sources (Reddit, G2, forums, communities, review sites). Your job is to know where to look and what to extract. Most engagements combine both. Establish which mode applies before proceeding. --- ## Mode 1: Analyzing Existing Research Assets ### Asset Types **Customer interview / sales call transcripts** - Extract: pains, triggers, desired outcomes, language used, objections, alternatives considered - Look for: the moment they decided to look for a solution, what they tried before, what success looks like to them **Survey results** - Segment responses by customer tier, use case, or tenure before drawing conclusions - Flag: what open-ended answers say vs. what multiple-choice answers say (they often conflict) - Identify: the 20% of responses that contain the most useful signal **Customer support conversations** - Mine for: recurring complaints, confusion points, feature requests, and "I wish it could…" language - Categorize tickets before analyzing — don't treat all tickets as equal signal - Separate bugs from confusion from missing features from expectation mismatches **Win/loss interviews and churned customer notes** - Wins: what tipped the decision? What almost made them choose a competitor? - Losses and churn: was it price, features, fit, timing, or something else? - Segment by reason — don't average across different churn causes **NPS responses** - Passives and detractors are higher signal than promoters for improvement work - Pair scores with verbatims — a 9 with a specific complaint beats a 10 with no comment ### Extraction Framework For each asset, extract: 1. **Jobs to Be Done** — what outcome is the customer trying to achieve? - Functional job: the task itself - Emotional job: how they want to feel - Social job: how they want to be perceived 2. **Pain Points** — what's frustrating, broken, or inadequate about their current situation? - Prioritize pains mentioned unprompted and with emotional language 3. **Trigger Events** — what changed that made them seek a solution? - Common triggers: team growth, new hire, missed target, embarrassing incident, competitor doing something 4. **Desired Outcomes** — what does success look like in their words? - Capture exact quotes, not paraphrases 5. **Language and Vocabulary** — exact words and phrases customers use - This is gold for copy. "We were drowning in spreadsheets" > "manual process inefficiency" 6. **Alternatives Considered** — what else did they look at or try? - Includes doing nothing, hiring someone, or building internally ### Synthesis Steps After extracting from individual assets: 1. **Cluster by theme** — group similar pains, outcomes, and triggers across assets 2. **Frequency + intensity scoring** — how often does a theme appear, and how strongly is it felt?