ElasticFlow
AI SkillSynthesize VoCMarketingby Gooseworks

Voice of Customer Synthesizer — every signal in one report

Runs on
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Aggregate customer feedback into a unified VoC report

  • Aggregates feedback from support, NPS, Slack, reviews, calls, surveys
  • Clusters themes across all sources
  • Performs sentiment analysis and trend detection
  • Generates actionable recommendations for product, marketing, CS
  • Quarterly digest format

Who this is for

Product Marketer

You own positioning, launches, and the buyer journey. These skills help you write copy that converts, price for growth, and launch products people actually want.

See skills for this role

What it does

Quarterly VoC review

Get the full picture of what every customer signal is saying in one structured report.

Pre-board-meeting prep

Walk into board meetings with a synthesized VoC report instead of anecdotes.

Cross-functional alignment

Give product, marketing, and CS the same view of what customers are saying.

How it works

1

Take feedback from multiple sources as input

2

Cluster themes across sources

3

Run sentiment and trend analysis

4

Generate cross-functional recommendations

5

Output unified VoC report

Metrics this improves

Content Quality
Better content quality by grounding messaging in real customer voice patterns
Marketing

Works with

G2
manual

Source for review-based VoC

Zendesk
manual

Source for support ticket signals

Slack
manual

Source for customer conversation signals

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Voice of Customer Synthesizer

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.

When to Use

  • "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"

Phase 0: Intake

Feedback Sources (provide all you have)

  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

Configuration

  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")

Phase 1: Data Collection

1A: Internal Data Processing

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

1B: External Review Scraping (if applicable)

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.

1C: Social Listening (if applicable)

Search: "[product name]" feedback OR review OR "switched to" OR "stopped using"
Search: "[product name]" site:reddit.com OR site:twitter.com

Phase 2: Theme Clustering

Group all feedback items into themes using a bottom-up approach:

Clustering Method

  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

Theme Template

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]

Phase 3: Analysis

3A: Sentiment Overview

Overall Sentiment Distribution:
  Positive:  [N] items ([X%])  ████████░░
  Neutral:   [N] items ([X%])  ████░░░░░░
  Negative:  [N] items ([X%])  ██░░░░░░░░
  Critical:  [N] items ([X%])  █░░░░░░░░░

3B: Source Comparison

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.

3C: Segment Analysis

Customer SegmentDominant SentimentTop RequestKey Pain
[New customers][Sentiment][Request][Pain]
[Power users][Sentiment][Request][Pain]
[Enterprise][Sentiment][Request][Pain]
[Churned][Sentiment][Request][Pain]

3D: Trend Detection

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]

Phase 4: Recommendations

For Product Team

PriorityThemeRecommendationEvidence Strength
P0[Theme][Specific action][N mentions, M sources, includes churn signals]
P1[Theme][Action][Evidence]
P2[Theme][Action][Evidence]

For CS/Support Team

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

For Marketing Team

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

Phase 5: Output Format

# Voice of Customer Report — [Period]
Sources analyzed: [list]
Total feedback items: [N]
Date range: [start] — [end]

---

## Executive Summary

[3-5 sentences: What are customers saying? What's the overall sentiment?
What's the single most important thing to act on?]

---

## Sentiment Overview

Positive: [X%] | Neutral: [X%] | Negative: [X%] | Critical: [X%]

Net Sentiment Score: [calculated — % positive minus % negative]
vs Prior Period: [+/- X points]

---

## Top Themes (Ranked by Impact)

### 1. [Theme Name] — [Sentiment] — [N mentions]
**Summary:** [2-3 sentences]
**Key quotes:**
> "[Quote]" — [Source]
> "[Quote]" — [Source]
**Recommended action:** [What to do]
**Owner:** [Product / CS / Marketing]

### 2. [Theme Name] — ...

### 3. [Theme Name] — ...

[Continue for top 5-8 themes]

---

## What Customers Love (Preserve These)

| Strength | Evidence | Marketing Opportunity |
|----------|---------|----------------------|
| [Feature/experience] | "[Quote]" — [N mentions] | [How to use in messaging] |

---

## What Customers Want (Feature Requests)

| Request | Frequency | Segments | Product Priority |
|---------|-----------|----------|-----------------|
| [Feature] | [N mentions] | [Who wants it] | [P0/P1/P2] |

---

## What Causes Pain (Fix These)

| Pain Point | Severity | Churn Risk | Recommended Fix |
|-----------|----------|------------|----------------|
| [Issue] | [High/Med/Low] | [Yes/No] | [Action] |

---

## Trends vs Prior Period

[What's getting better, what's getting worse, what's new]

---

## Team-Specific Action Items

### Product Team
1. [Action] — [Evidence]

### CS Team
1. [Action] — [Evidence]

### Marketing Team
1. [Action] — [Evidence]

---

## Appendix: All Themes Detail

[Full theme cards with all quotes and analysis]

Save to clients/<client-name>/customer-success/voc/voc-report-[YYYY-MM-DD].md.

Scheduling

Run monthly or quarterly:

0 8 1 */3 * python3 run_skill.py voice-of-customer-synthesizer --client <client-name>

Cost

ComponentCost
Review scraping (via review-scraper)~$0.50-1.00
Web search (social mentions)Free
All analysis and synthesisFree (LLM reasoning)
TotalFree — $1

Tools Required

  • 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

Trigger Phrases

  • "What are customers saying?"
  • "Build a VoC report"
  • "Synthesize our customer feedback"
  • "Run voice of customer analysis"
  • "Customer feedback summary for [period]"