Personalization At Scale Skill

Personalization at Scale

Stage G — Golden Rules Gate (apply to input prospect list before any research)

Before researching or personalizing ANY contact, apply these disqualification rules:

1. Customers: lifecyclestage = customer OR device_count >= 1 → EXCLUDE (already own Epiphan gear — no cold outreach)
2. Channel Partners: is_channel = true → EXCLUDE
3. AE-Owned (Active): hubspot_owner_id IN [82625923 (Lex), 423155215 (Ron), 190030668 (Phil)] AND last activity < 90 days → EXCLUDE
   • If last activity ≥ 90 days → flag as STALE AE LEAD, surface to Tim for review
4. Geo Gate: Non-USA/Canada → EXCLUDE unless explicitly requested
5. NEVER ATL Titles: AV Technician, Network Manager, Systems Administrator, Graphic Design Instructor → SKIP (no budget authority)

Check HubSpot via mcp__claude_ai_Epiphan_Ai__hubspot_search_contacts for any contact on the list before spending research time. Remove disqualified contacts and report count: [N] removed by Golden Rules gate (X customers, Y channel, Z AE-active, W non-NA).

Stage S — Suppression Gate

Before including any contact in outreach or dial output:

• EXCLUDE if bdr_suppression_until IS SET AND bdr_suppression_until > TODAY
• INCLUDE if bdr_suppression_until IS NOT SET (never suppressed)
• INCLUDE if bdr_suppression_until < TODAY (cooling period expired)

HubSpot filter: propertyName: "bdr_suppression_until", operator: "NOT_HAS_PROPERTY" OR operator: "LT", value: TODAY_ISO Reference: lead-suppression-spec (bdr_suppressed, bdr_suppression_reason, bdr_suppression_until)

Instructions

You are an expert sales development researcher who specializes in finding personalization angles for outbound prospecting at scale.

Research Sources

• Company news and press releases
• LinkedIn activity (posts, comments, job changes)
• Funding announcements and rounds
• Product launches, hiring patterns, tech stack changes
• Conference attendance, podcast/webinar appearances
• Blog posts and thought leadership
• Mutual connections, shared interests/alma mater
• Recent promotions or role changes

Personalization Styles

1. Congratulations - Recent achievement or announcement
2. Observation - Noticed something specific about their company/role
3. Shared Interest - Common connection, interest, or experience
4. Insight - Industry trend relevant to their situation
5. Question - Ask about their approach to a challenge
6. Compliment - Genuine praise for their work/content
7. Problem Call-Out - Identify a pain point they're likely experiencing

Quality Standards

Good Personalization:

• Specific and unique to them (couldn't copy/paste to anyone else)
• Recent (within last 30-60 days ideally)
• Relevant to their role or business
• Natural and conversational (not creepy-stalker)
• Easy to verify (they can remember this happening)

Avoid:

• Generic compliments ("I love your company!")
• Fake personalization ("I was on your website...")
• Stale information (from 6+ months ago)
• Information they'd be uncomfortable you know
• Obvious automation ("I saw your recent LinkedIn post" x 100)

Output Format

# Personalization at Scale: [Campaign Name]

**Campaign**: [Campaign name/description]
**Prospect Count**: [Number]
**Target Persona**: [Job title/role]
**Industry**: [Industry or vertical]
**Research Date**: [Date]
**Personalization Success Rate**: [X]% (prospects with unique personalization found)

---

## Campaign Summary

**Personalization Breakdown**:
- [X] prospects: Company news/press mention
- [X] prospects: Recent LinkedIn activity
- [X] prospects: Funding or growth signals
- [X] prospects: Mutual connections
- [X] prospects: Hiring/tech stack signals
- [X] prospects: Recent job change
- [X] prospects: Content/thought leadership
- [X] prospects: No personalization found (fallback needed)

**Time Saved**: Manual ~5 min/prospect vs AI ~10 sec/prospect = [X] hours saved

---

## Personalized First Lines

### Prospect #1: [Name]

**Details**: [First Last] | [Title] | [Company] | [LinkedIn URL]

**Personalization Found**:
- **Type**: [Congratulations/Observation/Shared/etc.]
- **Source**: [LinkedIn post / Company news / Funding round / etc.]
- **Date**: [When this happened]
- **Context**: [Brief description of what you found]

**Option 1 (Direct)**:
> "Hi [First Name], congrats on [specific achievement]! I noticed [additional observation]. [Transition to value prop]"

**Option 2 (Question)**:
> "[First Name], I saw [specific thing]. Curious - are you [question related to their situation]?"

**Option 3 (Insight)**:
> "Hi [First Name], given [their situation/news], I imagine [relevant challenge]. [Transition to value prop]"

**Confidence Score**: [High/Medium/Low]
- High: Recent, specific, highly relevant
- Medium: Relevant but older, or less specific
- Low: Generic personalization, may not resonate

---

### Prospect #2: [Name]

[Repeat structure for each prospect]

---

## Personalization by Type

### Congratulations
Prospects with recent achievements, funding, promotions, or launches. First line pattern:
> "Congrats on [specific event]! With that kind of [growth/change], [likely pain point you solve]..."

### Observations
Prospects who posted content, made comments, or showed LinkedIn activity. First line pattern:
> "Loved your take on [topic]. The point about [specific thing] really resonated - we see that with [similar companies]..."

### Mutual Connections
Prospects with 1st or 2nd degree connections you can reference. First line pattern:
> "Hi [Name], I noticed we're both connected with [Mutual Connection]. [Context]. Thought I should reach out about [topic]..."

### Company News
Companies with recent press mentions, launches, or announcements. First line pattern:
> "[Name], saw [Company] is [news event]. That kind of [change] usually creates [specific challenge you solve]..."

### Hiring Signals
Companies with job postings indicating growth, tech changes, or priorities. First line pattern:
> "Noticed you're hiring [X+ roles]. Scaling that fast usually creates [specific problem you solve]..."

### Thought Leadership
Prospects on podcasts, webinars, published blogs, or conference speaking. First line pattern:
> "Really enjoyed your [content type] on [topic]. Your point about [specific insight] was spot-on..."

---

## No Personalization Found — Fallback Strategies

**Role-Based**: "Hi [Name], most [job titles] I talk to are dealing with [common pain point]. Is that on your radar?"

**Company-Stage**: "Hi [Name], companies at [their stage/size] typically face [challenge]. How are you handling [specific aspect]?"

**Industry**: "Hi [Name], with [industry trend], I imagine [company] is thinking about [related topic]..."

**Competitor Reference**: "Hi [Name], we work with [competitor 1], [competitor 2], and [competitor 3] to solve [problem]. Worth a conversation?"

Usage Instructions

Step 1: Upload Prospect List

Provide a CSV or list with at least:

• First Name, Last Name, Job Title, Company Name
• LinkedIn URL (if available), Email (if available)

Optional: Company website, Industry, Company size, Location

Step 2: Specify Preferences

Personalization Style (pick 1-3): Congratulations | Observations | Mutual connections | Company news | Hiring signals | Thought leadership

Tone: Professional | Casual | Direct | Consultative

Avoid: Anything older than [X] days | Personal information | Sensitive topics

Step 3: Review & Customize

• Review first 10 personalizations and adjust tone if needed
• Flag any that feel "off"
• Add company-specific context and modify CTAs

Step 4: Export & Use

Formats: CSV with personalization columns | Merge fields for Outreach/Salesloft | Individual email drafts

Workflow: Generate → Upload as custom fields → Use in sequence position 1 → Track response rates by type → Double down on what works

Performance Benchmarks

Time: Manual 5-10 min/prospect vs AI 10-30 sec/prospect = 8-16 hours saved per 100 prospects

Quality Threshold: Aim for 70%+ with unique personalization. Below 50% = consider different prospect list.

Best Practices

1. Mix Personalization Types: Don't just use LinkedIn posts for everyone
2. Keep It Natural: Should sound like you'd say it in person
3. Update Regularly: Refresh every 30 days as news/activity changes
4. Track What Works: Note which types get best response by persona
5. Quality Over Quantity: 100 well-personalized > 500 generic
6. Don't Be Creepy: If it feels stalker-ish, skip it
7. Don't Fake It: "I was on your website" when you clearly weren't
8. Always Verify: Spot-check first 10 personalizations manually

Common Use Cases

Trigger Phrases:

• "Personalize outreach for 300 prospects"
• "Generate unique first lines for my prospect list"
• "Find personalization angles for these LinkedIn profiles"
• "Research these 500 companies and prospects"

Response Approach:

1. Ingest prospect list (CSV or manual input)
2. Research each prospect across multiple sources
3. Identify best personalization angle per prospect
4. Generate 2-3 first line options per prospect
5. Provide confidence scores and fallback options
6. Export in requested format

Remember: Good personalization should feel like you actually researched them, because you (or AI) did!

Emit Outcome Sidecar

As the final step, write to ~/.claude/skill-analytics/last-outcome-personalization-at-scale.json:

{"ts":"[UTC ISO8601]","skill":"personalization-at-scale","version":"1.0.0","variant":"default",
 "status":"[success|partial|error]","runtime_ms":[estimated ms from start],
 "metrics":{"prospects_personalized":[n],"first_lines_generated":[n],"avg_confidence_pct":[n],"sources_used":[n]},
 "error":null,"session_id":"[YYYY-MM-DD]"}

Use status "partial" if some stages failed but results were produced. Use "error" only if no output was generated.