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voice-of-customer-synthesizer

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Chinese

Voice of Customer Synthesizer

客户之声（VoC）合成工具

Turn scattered customer feedback into a single source of truth. Aggregates signals from every source you have, clusters them into themes, and produces a report that product, marketing, and CS teams can actually act on.
Built for: Startups where customer feedback lives in 6 different places and nobody has time to synthesize it. The founder says "what are customers saying?" and nobody has a clear answer. This skill produces that answer.
将分散的客户反馈整合为唯一可信数据源。聚合所有渠道的反馈信号，将其聚类为主题，并生成产品、营销和客户成功团队可实际执行的报告。
适用场景： 客户反馈分散在6个不同渠道，且无人有时间整合的初创公司。当创始人问“客户都在说什么？”时，没人能给出清晰答案。本工具就能提供这个答案。

When to Use

使用时机

  • "What are our customers saying?"
  • "Synthesize customer feedback from last quarter"
  • "Build a VoC report for the product team"
  • "What themes are coming up in customer feedback?"
  • "Aggregate feedback from all our channels"
  • “客户都在说什么？”
  • “整合上季度的客户反馈”
  • “为产品团队生成一份VoC报告”
  • “客户反馈中出现了哪些主题？”
  • “整合所有渠道的反馈”

Phase 0: Intake

阶段0：信息收集

Feedback Sources (provide all you have)

反馈渠道（请提供所有相关数据）

  1. Support tickets — Export from support tool (CSV: customer, date, subject, description, tags, resolution)
  2. NPS/CSAT survey responses — Scores + verbatim comments
  3. Slack messages — Customer channel messages, feedback channels
  4. G2/Capterra reviews — Will scrape if product is listed (provide product name or URL)
  5. Call/meeting transcripts — Customer call recordings or notes
  6. Churn exit survey responses — Why did customers leave?
  7. Feature request log — Internal tracker of what customers have asked for
  8. Social mentions — Twitter/LinkedIn/Reddit threads mentioning your product
  9. Email threads — Notable customer emails (praise or complaints)
  10. In-app feedback — Any in-product feedback submissions
  1. 支持工单 — 从支持工具导出（CSV格式：客户、日期、主题、描述、标签、解决方案）
  2. NPS/CSAT调查回复 — 评分 + 文字评论
  3. Slack消息 — 客户频道消息、反馈频道内容
  4. G2/Capterra评价 — 若产品已在平台上架，将自动爬取（请提供产品名称或URL）
  5. 通话/会议记录 — 客户通话录音或笔记
  6. 流失客户退出调查回复 — 客户为何流失？
  7. 功能需求日志 — 内部记录的客户需求
  8. 社交平台提及 — Twitter/LinkedIn/Reddit上提及产品的帖子
  9. 邮件对话 — 重要的客户邮件（表扬或投诉）
  10. 应用内反馈 — 任何产品内提交的反馈

Configuration

配置项

  1. Time period — What window to analyze? (Last 30 days, quarter, 6 months)
  2. Product name — For review scraping and context
  3. Report audience — Who's reading this? (Product team, exec team, CS team, all)
  4. Focus areas — Any specific themes to pay attention to? (e.g., "onboarding experience", "pricing feedback", "mobile app")
  1. 时间范围 — 分析哪个时间段的数据？（过去30天、季度、6个月）
  2. 产品名称 — 用于评价爬取和上下文识别
  3. 报告受众 — 报告的阅读对象是谁？（产品团队、高管团队、客户成功团队、全员）
  4. 重点领域 — 是否有需要重点关注的特定主题？（例如：“新手引导体验”、“定价反馈”、“移动应用”）

Phase 1: Data Collection

阶段1：数据收集

1A: Internal Data Processing

1A：内部数据处理

From the provided inputs, normalize all feedback into a standard format:
SOURCE | DATE | CUSTOMER | SEGMENT | FEEDBACK_TEXT | SENTIMENT | CATEGORY
Sentiment classification per item:
  • Positive — Praise, satisfaction, delight
  • Neutral — Feature request, question, observation
  • Negative — Complaint, frustration, disappointment
  • Critical — Churn threat, escalation, anger
将提供的所有反馈标准化为以下格式：
渠道 | 日期 | 客户 | 群体 | 反馈文本 | 情感倾向 | 分类
单条反馈的情感分类：
  • 正面 — 表扬、满意、愉悦
  • 中性 — 功能需求、问题、观察
  • 负面 — 投诉、沮丧、失望
  • 严重负面 — 流失威胁、升级投诉、愤怒

1B: External Review Scraping (if applicable)

1B：外部评价爬取（如适用）

If product is on review platforms:
Chain: review-scraper for G2, Capterra, Trustpilot
Filter: reviews from the target time period
Extract: rating, review text, reviewer role/company size, date, pros, cons.
若产品在评价平台上架：
Chain: review-scraper 用于G2、Capterra、Trustpilot
过滤条件：目标时间范围内的评价
提取内容：评分、评价文本、评价者角色/公司规模、日期、优点、缺点。

1C: Social Listening (if applicable)

1C：社交平台监听（如适用）

Search: "[product name]" feedback OR review OR "switched to" OR "stopped using"
Search: "[product name]" site:reddit.com OR site:twitter.com
搜索关键词："[产品名称]" feedback OR review OR "switched to" OR "stopped using"
搜索范围："[产品名称]" site:reddit.com OR site:twitter.com

Phase 2: Theme Clustering

阶段2：主题聚类

Group all feedback items into themes using a bottom-up approach:
采用自下而上的方法将所有反馈项分组为主题：

Clustering Method

聚类方法

  1. Read all feedback items
  2. Identify recurring topics (mentioned by 3+ customers or in 3+ sources)
  3. Group into theme clusters
  4. Rank by frequency AND severity
  1. 读取所有反馈项
  2. 识别重复出现的主题（被3个以上客户提及或出现在3个以上渠道）
  3. 分组为主题集群
  4. 按提及频次和严重程度排序

Theme Template

主题模板

THEME: [Name — e.g., "Onboarding Complexity"]
FREQUENCY: [N mentions across M sources]
SENTIMENT: [Predominantly positive/neutral/negative]
TREND: [↑ Growing / → Stable / ↓ Declining vs prior period]

REPRESENTATIVE QUOTES:
- "[Exact quote]" — [Source, Customer segment, Date]
- "[Exact quote]" — [Source, Customer segment, Date]
- "[Exact quote]" — [Source, Customer segment, Date]

CUSTOMER SEGMENTS AFFECTED:
- [Segment 1: e.g., "New customers in first 30 days"]
- [Segment 2: e.g., "Enterprise accounts"]

ROOT CAUSE HYPOTHESIS:
[1-2 sentences: Why is this coming up? What's the underlying issue?]

IMPACT:
- On retention: [High/Medium/Low]
- On expansion: [High/Medium/Low]
- On acquisition: [High/Medium/Low]
主题：[名称 — 示例："Onboarding Complexity"]
提及频次：[在M个渠道中被提及N次]
情感倾向：[以正面/中性/负面为主]
趋势：[与上期相比 ↑上升 / →平稳 / ↓下降]

代表性引用：
- "[具体引用内容]" — [渠道, 客户群体, 日期]
- "[具体引用内容]" — [渠道, 客户群体, 日期]
- "[具体引用内容]" — [渠道, 客户群体, 日期]

受影响的客户群体：
- [群体1：示例："New customers in first 30 days"]
- [群体2：示例："Enterprise accounts"]

根本原因假设：
[1-2句话：为何出现该问题？底层原因是什么？]

影响：
- 客户留存：[高/中/低]
- 业务拓展：[高/中/低]
- 客户获取：[高/中/低]

Phase 3: Analysis

阶段3：分析

3A: Sentiment Overview

3A：情感概览

Overall Sentiment Distribution:
  Positive:  [N] items ([X%])  ████████░░
  Neutral:   [N] items ([X%])  ████░░░░░░
  Negative:  [N] items ([X%])  ██░░░░░░░░
  Critical:  [N] items ([X%])  █░░░░░░░░░
整体情感分布：
  正面:  [N]条 ([X%])  ████████░░
  中性:   [N]条 ([X%])  ██░░░░░░░░
  负面:  [N]条 ([X%])  ██░░░░░░░░
  严重负面:  [N]条 ([X%])  █░░░░░░░░░

3B: Source Comparison

3B：渠道对比

SourceVolumeAvg SentimentTop Theme
Support tickets[N][Pos/Neg score][Theme]
NPS comments[N][Score][Theme]
G2 reviews[N][Score][Theme]
Slack[N][Score][Theme]
Calls[N][Score][Theme]
Insight: Different sources often reveal different stories. Support tickets skew negative (problems). Reviews skew bipolar (love/hate). Calls reveal nuance. Note where themes appear across sources for highest confidence.
渠道数量平均情感核心主题
支持工单[N][正面/负面评分][主题]
NPS评论[N][评分][主题]
G2评价[N][评分][主题]
Slack[N][评分][主题]
通话记录[N][评分][主题]
洞察： 不同渠道往往反映不同的情况。支持工单偏向负面（问题反馈），评价平台呈现两极分化（爱/恨），通话记录则能体现细节。若同一主题出现在多个渠道，可信度最高。

3C: Segment Analysis

3C：客户群体分析

Customer SegmentDominant SentimentTop RequestKey Pain
[New customers][Sentiment][Request][Pain]
[Power users][Sentiment][Request][Pain]
[Enterprise][Sentiment][Request][Pain]
[Churned][Sentiment][Request][Pain]
客户群体主导情感核心需求主要痛点
[新客户][情感倾向][需求][痛点]
[核心用户][情感倾向][需求][痛点]
[企业客户][情感倾向][需求][痛点]
[流失客户][情感倾向][需求][痛点]

3D: Trend Detection

3D：趋势检测

Compare against prior period (if available):
ThemePrior PeriodThis PeriodTrendAlert
[Theme 1][N mentions][N mentions][↑X%][New/Growing/Stable/Declining]
[Theme 2]............
New themes this period: [Themes that weren't present before] Resolved themes: [Themes that decreased significantly — things you fixed]
与上期数据对比（如可用）：
主题上期本期趋势提醒
[主题1][提及次数][提及次数][↑X%][新增/上升/平稳/下降]
[主题2]............
本期新增主题： [之前未出现的主题] 已解决主题： [提及次数大幅下降的主题——已修复的问题]

Phase 4: Recommendations

阶段4：建议

For Product Team

给产品团队的建议

PriorityThemeRecommendationEvidence Strength
P0[Theme][Specific action][N mentions, M sources, includes churn signals]
P1[Theme][Action][Evidence]
P2[Theme][Action][Evidence]
优先级主题建议证据强度
P0[主题][具体行动][N次提及，M个渠道，包含流失信号]
P1[主题][行动][证据]
P2[主题][行动][证据]

For CS/Support Team

给客户成功/支持团队的建议

ActionThemeExpected Impact
[Create help article for X][Theme]Deflect ~[N] tickets/month
[Add onboarding step for Y][Theme]Reduce confusion for new users
[Proactive outreach to segment Z][Theme]Prevent churn in at-risk segment
行动主题预期影响
[创建X的帮助文档][主题]每月减少约[N]个工单
[添加Y的新手引导步骤][主题]减少新用户的困惑
[主动联系Z群体][主题]预防高风险群体流失

For Marketing Team

给营销团队的建议

ActionThemeOpportunity
[Use this proof point in messaging][Positive theme]"[Customer quote ready for marketing]"
[Address this objection on website][Negative theme]Counter common concern pre-sale
[Build case study around X][Positive theme][N] customers mentioned this win
行动主题机会点
[在营销话术中使用该证明点][正面主题]"[可直接用于营销的客户引用]"
[在官网回应该异议][负面主题]提前打消潜在客户的常见顾虑
[围绕X打造案例研究][正面主题][N]个客户提及该优势

Phase 5: Output Format

阶段5：输出格式

markdown
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markdown
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Voice of Customer Report — [Period]

客户之声（VoC）报告 — [时间段]

Sources analyzed: [list] Total feedback items: [N] Date range: [start] — [end]
分析渠道：[列表] 总反馈条数：[N] 日期范围：[开始日期] — [结束日期]

Executive Summary

执行摘要

[3-5 sentences: What are customers saying? What's the overall sentiment? What's the single most important thing to act on?]
[3-5句话：客户都在说什么？整体情感倾向如何？最需要优先处理的事项是什么？]

Sentiment Overview

情感概览

Positive: [X%] | Neutral: [X%] | Negative: [X%] | Critical: [X%]
Net Sentiment Score: [calculated — % positive minus % negative] vs Prior Period: [+/- X points]
正面: [X%] | 中性: [X%] | 负面: [X%] | 严重负面: [X%]
净情感得分：[计算值 — 正面占比减去负面占比] 与上期对比：[±X分]

Top Themes (Ranked by Impact)

核心主题（按影响排序）

1. [Theme Name] — [Sentiment] — [N mentions]

1. [主题名称] — [情感倾向] — [提及次数]

Summary: [2-3 sentences] Key quotes:
"[Quote]" — [Source] "[Quote]" — [Source] Recommended action: [What to do] Owner: [Product / CS / Marketing]
摘要： [2-3句话] 核心引用：
"[引用内容]" — [渠道] "[引用内容]" — [渠道] 建议行动： [具体措施] 负责团队： [产品 / 客户成功 / 营销]

2. [Theme Name] — ...

2. [主题名称] — ...

3. [Theme Name] — ...

3. [主题名称] — ...

[Continue for top 5-8 themes]
[继续列出前5-8个主题]

What Customers Love (Preserve These)

客户认可的优势（需保持）

StrengthEvidenceMarketing Opportunity
[Feature/experience]"[Quote]" — [N mentions][How to use in messaging]
优势证据营销机会
[功能/体验]"[引用内容]" — [N次提及][如何用于营销话术]

What Customers Want (Feature Requests)

客户需求（功能请求）

RequestFrequencySegmentsProduct Priority
[Feature][N mentions][Who wants it][P0/P1/P2]
需求提及频次目标群体产品优先级
[功能][提及次数][需求群体][P0/P1/P2]

What Causes Pain (Fix These)

客户痛点（需修复）

Pain PointSeverityChurn RiskRecommended Fix
[Issue][High/Med/Low][Yes/No][Action]
痛点严重程度流失风险建议修复方案
[问题][高/中/低][是/否][行动]

Trends vs Prior Period

与上期对比的趋势

[What's getting better, what's getting worse, what's new]
[哪些情况在变好，哪些在变差，哪些是新增的]

Team-Specific Action Items

各团队具体行动项

Product Team

产品团队

  1. [Action] — [Evidence]
  1. [行动] — [证据]

CS Team

客户成功团队

  1. [Action] — [Evidence]
  1. [行动] — [证据]

Marketing Team

营销团队

  1. [Action] — [Evidence]
  1. [行动] — [证据]

Appendix: All Themes Detail

附录：所有主题详情

[Full theme cards with all quotes and analysis]

Save to `clients/<client-name>/customer-success/voc/voc-report-[YYYY-MM-DD].md`.
[包含所有引用和分析的完整主题卡片]

保存至 `clients/<client-name>/customer-success/voc/voc-report-[YYYY-MM-DD].md`。

Scheduling

调度配置

Run monthly or quarterly:
bash
0 8 1 */3 * python3 run_skill.py voice-of-customer-synthesizer --client <client-name>
每月或每季度运行：
bash
0 8 1 */3 * python3 run_skill.py voice-of-customer-synthesizer --client <client-name>

Cost

成本

ComponentCost
Review scraping (via review-scraper)~$0.50-1.00
Web search (social mentions)Free
All analysis and synthesisFree (LLM reasoning)
TotalFree — $1
组件成本
评价爬取（通过review-scraper）~$0.50-1.00
网页搜索（社交平台提及）免费
所有分析与合成免费（LLM推理）
总计免费 — $1

Tools Required

所需工具

  • Optional: 
    review-scraper
    for G2/Capterra/Trustpilot reviews
  • Optional: 
    twitter-scraper
    for social mentions
  • Optional: 
    reddit-scraper
    for community feedback
  • All analysis is pure LLM reasoning on provided data
  • 可选： 
    review-scraper
    用于爬取G2/Capterra/Trustpilot评价
  • 可选： 
    twitter-scraper
    用于社交平台提及爬取
  • 可选： 
    reddit-scraper
    用于社区反馈爬取
  • 所有分析均基于提供的数据进行纯LLM推理

Trigger Phrases

触发短语

  • "What are customers saying?"
  • "Build a VoC report"
  • "Synthesize our customer feedback"
  • "Run voice of customer analysis"
  • "Customer feedback summary for [period]"
  • "客户都在说什么？"
  • "生成VoC报告"
  • "整合我们的客户反馈"
  • "执行客户之声分析"
  • "[时间段]的客户反馈摘要" ",