The “Voice of the Customer” Audit

What It Is

The Voice of Customer Audit finds and analyzes a prospect's public speaking—podcasts, webinars, conference talks, articles—to extract their actual words, priorities, and communication style. Instead of guessing what matters to them, you know exactly what they've publicly stated and can mirror their language.

Why It Matters

People trust people who 'speak their language.' By referencing their own words ('On the RevGenius podcast, you mentioned…') and using their preferred terminology, you demonstrate genuine research and create instant credibility. This transforms cold outreach into warm, relevant conversation.

Who It's For

• Enterprise sales reps targeting executive buyers
• SDRs personalizing outreach at scale
• Account Executives preparing for strategic meetings
• ABM teams creating highly personalized content

Required Tools

• ListenNotes API (for podcast discovery)
• OpenAI Whisper (for audio transcription)
• GPT-4 (for analysis and summary)
• Clay or similar for enrichment
• LinkedIn for written content

Required Fields/Properties

• Prospect name and current company
• Industry context for filtering
• Analysis prompt templates
• Output format specifications

Definitions Required

• Minimum seniority for VoC audit (typically VP+)
• Content sources to search
• Key themes to extract
• How to present findings to sales

Voice of Customer Profile Template

🎙️ VOICE OF CUSTOMER PROFILE
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

👤 {{prospect_name}}, {{prospect_title}} at {{company}}
📅 Last Updated: {{analysis_date}}
📋 Sources Analyzed: {{source_count}} (Podcasts, Articles, LinkedIn)

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

🎯 TOP PRIORITIES (What They Care About)
{{#each priorities}}
• {{priority}}
{{/each}}

😤 PAIN POINTS (Frustrations They've Mentioned)
{{#each pain_points}}
• {{pain_point}}
{{/each}}

💬 VOCABULARY (Terms They Use)
• Uses: "{{preferred_term}}" not "{{alternative_term}}"
• Frequently says: "{{repeated_phrase}}"
• Avoids: {{terms_to_avoid}}

📢 COMMUNICATION STYLE
{{communication_style_summary}}

✨ QUOTABLE MOMENTS (For Outreach)
{{#each quotes}}
"{{quote_text}}"
— Source: {{quote_source}} ({{quote_date}})
{{/each}}

💡 SUGGESTED OPENERS
• "On {{source_name}}, you mentioned {{topic}}..."
• "I noticed your point about {{insight}}..."

🔗 Sources:
{{#each sources}}
• {{source_name}}: {{source_url}}
{{/each}}

Personalized Email Using VoC

Subject: Your point on {{podcast_name}} resonated

Hi {{first_name}},

I caught your interview on {{podcast_name}} where you said: "{{direct_quote}}"

That stuck with me because we're seeing the same pattern with {{similar_role}} at [company A] and [company B]. The {{pain_point}} you described is almost universal right now.

We helped {{similar_company}} address exactly that—{{brief_outcome}}.

Would it be useful to compare notes? Happy to share what worked for them.

Best,
{{sender_name}}

P.S. Loved the story about {{personal_anecdote}} too.

Analysis Prompt for GPT-4

You are analyzing content from {{prospect_name}}, {{prospect_title}} at {{company}}.

I will provide transcripts from their podcast appearances, LinkedIn posts, and articles.

Your task:
1. Identify their top 3-5 professional priorities based on what they discuss most
2. Extract specific pain points or challenges they've mentioned
3. Note their communication style (formal/casual, data-driven/narrative, direct/diplomatic)
4. Pull 2-3 direct quotes that would resonate if referenced in sales outreach
5. Identify any personal interests, values, or preferences they've revealed
6. Note any terms or phrases they use consistently

Format your response as structured JSON for easy parsing.

Content to analyze:
{{content}}

Source Priority Matrix

| Source Type | Signal Strength | Candor Level | Effort to Find |
|-------------|-----------------|--------------|----------------|
| Podcast Guest Appearance | High | Very High | Medium |
| Webinar/Conference Talk | High | Medium | Low |
| LinkedIn Posts | Medium | Medium | Very Low |
| LinkedIn Articles | High | Medium | Low |
| Company Blog | Medium | Low | Low |
| Press Quotes | Low | Low | Low |
| Twitter/X Posts | Medium | High | Low |

Test Cases Checklist

• Prospect with multiple podcasts → comprehensive VoC profile generated
• Prospect with only LinkedIn presence → partial profile with written content
• Prospect with no public content → graceful 'limited data' response
• Audio quality issues → transcription error handling
• Quote used in outreach → positive response validates approach

Common Failure Modes

• Wrong person identified: Common names return wrong podcast appearances. Always verify company and title match before transcribing.
• Outdated content analyzed: Podcast from 3 years ago may reflect different role/priorities. Weight recent content higher and note dates.
• Creepy factor: Referencing obscure personal details feels stalkerish. Focus on professional content and publicly stated priorities.
• Misquoting or paraphrasing poorly: If you attribute a quote that's inaccurate, you lose trust instantly. Verify quotes against source before using.

Troubleshooting Tips

• If no podcasts found: Try alternate name spellings, previous companies, or industry podcasts they might guest on
• If transcription quality is poor: Try different Whisper model size or consider paid transcription service
• If analysis misses key themes: Tune the GPT prompt with more specific instructions
• If reps aren't using profiles: Review placement and format—may need to simplify or relocate

KPIs to Track

• Profiles Generated: VoC profile created for 100% of enterprise prospects
• Outreach Using VoC: >50% of personalized emails reference VoC insights
• Response Rate Lift: VoC-personalized emails outperform generic by 2x
• Meeting Conversion: Track meeting book rate for VoC-referenced outreach
• Rep Adoption: >80% of AEs actively using VoC profiles

Suggested Dashboard Widgets

• Profiles by Source Type: Breakdown of profiles by content sources available
• Outreach Performance: Response rates for VoC-personalized vs. generic outreach
• Content Discovery Rate: Percentage of prospects with at least one audio source found
• Quote Usage Tracking: Which quotes are being used and their performance