Voice of Customer

Mine real customer language from communities, reviews, and forums. Uses Perplexity for community research. Output feeds copywriting, email, SEO, and every execution skill.

Purpose

Voice of Customer answers the question “What words do our customers actually use?” It produces a Customer Language Bank with four categories:

• Pain Expressions — Complaints, frustrations, problem descriptions
• Desire Expressions — Wishes, aspirations, ideal outcomes
• Comparison Language — How customers evaluate and compare alternatives
• Trigger Phrases — What finally pushes them to buy, subscribe, or switch

The output — research-memory/customer-language.md — is the language foundation that copywriting (06), SEO (07), newsletter (08), email sequences (09), and content atomizer (10) reference to write in customers’ own words.

“Every customer photo is an ad. Every review is copy. Every unboxing video is content. You’re not creating marketing — you’re curating what customers already give you.”

Relationship to Audience Profiler: Audience Profiler maps who the customer is. Voice of Customer captures how the customer talks.

Modes

Auto-Load Protocol

On every invocation, BEFORE any research:

1. Check research-memory/ directory
2. If files exist → Read ALL .md files (except README.md)
3. Use loaded context to:
   • From customer-insight.md: Pull segment names, pain points, and community list → use as search keyword seeds (Step 1)
   • From competitive-intel.md: Pull competitor names → generate comparison search keywords (“[competitor] review”, “[competitor] alternative”)
   • Build on existing findings (deepen, not repeat)
4. Check brand-memory/ (read-only) → If exists, extract product name and category from voice-profile.md → use in search queries
5. If customer-language.md has data AND research-log.md shows a previous mining run → suggest Refresh mode

Input Gathering

Collect from the user conversationally. Do NOT dump a form — ask naturally.

If customer-insight.md exists, pre-fill category, segments, pain points, and communities — ask user to confirm or add.

If competitive-intel.md exists, extract competitor names automatically — show user and ask if any should be excluded.

If this is a Refresh, show current Language Bank summary (count per category) and ask: “Which category needs more expressions? Any new communities to mine?”

Research Intensity

사용자가 명시적으로 요청하면 리서치 깊이를 조절합니다. 지정하지 않으면 deep (기본값).

“가볍게”, “빠르게”, “간단히” → light / “보통으로”, “적당히” → standard / 별도 지정 없음 → deep

Process

Step 1: Load Context & Generate Search Keywords

Goal: Build a keyword matrix for targeted community mining.

Actions:

• Load all research-memory/ files
• Extract from customer-insight.md (if available):
  • Segment names → search audience context
  • Pain points → Pain Expression seed keywords
  • Community list → search scope
• Extract from competitive-intel.md (if available):
  • Competitor names → Comparison and Review keywords
• Generate the Search Keyword Matrix:

PAIN keywords:
  "[category] problems", "[category] frustrating", "[category] sucks"
  "[category] disappointed", "hate [category]", "[category] complaint"

DESIRE keywords:
  "best [category]", "[category] recommendation", "wish [category] could"
  "ideal [category]", "perfect [category] would", "[category] dream feature"

COMPARISON keywords:
  "[competitor A] vs [competitor B]", "[product] alternative"
  "switched from [competitor]", "[competitor] review honest"

TRIGGER keywords:
  "finally bought [category]", "worth it [category]"
  "what convinced me [category]", "just signed up for"

• If customer-language.md exists with data → switch to Refresh mode

Output: Keyword matrix ready for Steps 2-4.

Step 2: Mine Pain Expressions

Goal: Collect 10-15 real phrases expressing frustration, complaints, and problems.

Tool: perplexity_ask

Query pattern:

What are the most common complaints and frustrations people express about [category] in online communities (Reddit, review sites, forums, social media)?

For each expression: quote the actual language, note the context, identify the source platform, rate intensity (Mild/Strong/Extreme).

Find 10-15 distinct pain expressions from [primary segment] perspective.
Include complaints about: [competitor list]

Parameters: search_context_size: Research Intensity에 따라 결정 (light→"low" / standard→"medium" / deep→"high"), search_recency_filter: "year"

수집 목표: light=5-7 / standard=7-10 / deep=10-15 pain expressions

Step 3: Mine Desire Expressions & Comparison Language

Goal: Collect 8-10 desire expressions + 5-8 comparison expressions in one pass.

Why combined: In communities, wishes and comparisons naturally co-occur in the same threads (“I wish X could do what Y does”).

Tool: perplexity_ask

Query pattern:

What do people say when discussing what they WANT from [category] and how they COMPARE options?

DESIRE EXPRESSIONS: "I wish [product] could...", "The perfect [category] would...", ideal outcomes, feature requests.
COMPARISON LANGUAGE: "[A] vs [B]", "I switched from X to Y because...", evaluation criteria mentioned.

For each: quote actual language, note context + source, tag as Desire or Comparison.
For comparisons: note competitors mentioned and criteria used.

Find 8-10 desire + 5-8 comparison expressions. Include: [competitor list]

Parameters: search_context_size: Research Intensity에 따라 결정 (light→"low" / standard→"medium" / deep→"high"), search_recency_filter: "year"

수집 목표: desire — light=3-5 / standard=5-7 / deep=8-10, comparison — light=2-3 / standard=3-5 / deep=5-8

Step 4: Mine Trigger Phrases

Goal: Collect 8-12 phrases describing the moment of purchase decision.

Tool: perplexity_ask

Query pattern:

What makes people finally decide to purchase or subscribe to [category]?

Search for phrases describing the MOMENT of decision: "I finally decided because...", "What convinced me was...", "I was on the fence until..."

Classify each by trigger type: Social Proof, Urgency, Value Realization, Risk Removal, Authority, or Scarcity.

For each: quote the expression, classify type, note source platform.
Find 8-12 trigger phrases across 3+ trigger types.

Parameters: search_context_size: Research Intensity에 따라 결정 (light→"low" / standard→"medium" / deep→"high"), search_recency_filter: "year"

수집 목표: light=3-5 / standard=5-8 / deep=8-12 trigger phrases

Step 5: Save & Log

Goal: Write all findings to research-memory/customer-language.md and log execution.

5a. Write customer-language.md

Language rule: 섹션 헤더와 테이블 컬럼명은 영어로 유지합니다. 본문, 셀 값, 설명, 분석 텍스트는 사용자가 지정한 언어로 작성합니다. 언어가 지정되지 않으면 English로 작성합니다.

Use the exact schema in references/customer-language-schema.md. Key rules:

• 5 sections: Pain Expressions, Desire Expressions, Comparison Language, Trigger Phrases, Community Sources
• Every expression in quotation marks with source attribution
• For Refresh: append new rows to existing tables, do NOT overwrite

5b. Update research-log.md

Append one row:

| [YYYY-MM-DD] | voice-of-customer | Full Mining / Refresh | [X pain + Y desire + Z comparison + W trigger collected, key sources] | Perplexity |

Perplexity MCP Tool Guide

This skill does NOT use perplexity_reason. The focus is collection, not classification or reasoning. perplexity_ask handles fact-based community mining best.

Common parameters:

• search_context_size: Research Intensity 레벨에 따라 결정 — 위 Research Intensity 테이블 참조
• search_recency_filter: "year" — recent expressions are most relevant for current copy

Query best practices:

• Always include the product category in every query
• Ask for ACTUAL phrases, not summaries — emphasize “quote the real language”
• Include competitor names to surface comparison discussions
• One category per query (pain, desire+comparison, triggers) — don’t combine all in one call
• Request source attribution for every expression
• Language: 사용자가 English 외 언어를 지정한 경우, 모든 query 끝에 “Respond in [language].”를 추가

Quality Checklist

Before saving, verify:

• Pain expressions 최소 5개 (light) ~ 15개 (deep) 수집 (with intensity ratings)
• Desire expressions 최소 3개 (light) ~ 10개 (deep) 수집
• Comparison expressions 최소 2개 (light) ~ 8개 (deep) 수집, competitor names and criteria 포함
• Trigger phrases 최소 3개 (light) ~ 12개 (deep) 수집, 3+ trigger types
• Every expression includes source platform/community
• Community Sources table lists all mined sources with URLs where available
• Expressions read like real human language (not AI-summarized)
• All Perplexity responses include source citations
• research-log.md updated with execution record
• If Refresh: existing expressions preserved, new ones appended

Example (Abbreviated)

Input: “AI marketing education / skill packs for solo marketers”

Pain Expressions:

• “I’ve installed 10 AI tools and ended up only using ChatGPT” (Reddit r/marketing, Strong)
• “Everything AI writes sounds the same — that generic LinkedIn tone” (Twitter, Strong)
• “Spent more time writing prompts than actually writing the copy” (HN, Mild)

Desire Expressions:

• “I just want copy-paste prompts that actually work for my niche” (Reddit)
• “Wish there was a system, not just random tips scattered across YouTube” (Twitter)

Comparison Language:

• “DigitalMarketer’s content was made pre-AI so it feels outdated now”
• “Demand Curve is great for growth but weak on content marketing side”

Trigger Phrases:

• “$199 is less than one month of agency fees and I keep it forever” (Value Realization)
• “Three people in my Twitter feed recommended it in one week” (Social Proof)

What This Skill Does NOT Do

• Customer profiling / segmentation → Use audience-profiler (maps who they are)
• Competitor identification → Use competitor-finder (identifies the players)
• Website scraping for competitor copy → Use competitor-analyzer (Firecrawl)
• Strategic recommendations → Use research-synthesizer (reads this output)
• Market sizing / trends → Use market-scanner

Voice of Customer stays focused on capturing real language — the exact words, phrases, and expressions customers use when they complain, wish, compare, and decide.