From Generic to Personal: How AI Writes Custom Cold Emails at Scale

The Challenge: Personalization Doesn't Scale (Or Does It?)

Cold email is a numbers game, but only if your emails don't sound like a numbers game. Every sales professional knows the truth: personalized emails get 5-8X higher response rates than generic templates (source: HubSpot, Campaign Monitor).

But here's the problem we faced at RSL Media Hub: personalizing each cold email properly takes time. A lot of time.

The Manual Personalization Process

Before automation, sending a single personalized cold email required:

• Visit their LinkedIn profile (1-2 minutes) - Check their headline, recent posts, company info
• Research their business (2-3 minutes) - What do they do? What problems might they have?
• Craft a custom icebreaker (3-5 minutes) - Write something genuine that references their specific situation
• Total time per email: 8 minutes

The Math That Didn't Add Up

When you're running outreach campaigns at scale, this becomes unsustainable:

• 50 emails per week (typical B2B outreach volume)
• 8 minutes per email = 400 minutes (6.7 hours/week)
• Annual time cost: 350 hours per year
• Financial cost: $46,725/year (at $133.50/hour agency rate)

The alternative? Generic templates that get ignored. We needed a third option: personalization at scale.

The RSL Solution: AI-Powered LinkedIn-Enriched Icebreakers

We built a fully automated cold email personalization system that uses LinkedIn enrichment data and OpenAI to generate custom icebreakers for every prospect. The result: emails that feel hand-written, at a fraction of the time.

How It Works

The complete Make.com workflow: Google Sheets → OpenAI → Update Sheet (click to expand)

Phase 1: LinkedIn Enrichment Data Collection

We start with a Google Sheet containing enriched lead data from LinkedIn:

• Basic info: First name, last name, email, company name
• LinkedIn data: Job title, headline, profile URL
• Business context: Industry, company size, recent activity
• Enrichment data: Technologies used, funding status, growth signals

This data is typically gathered using tools like Apollo.io, LinkedIn Sales Navigator, or Clay.com. The richer the context, the better the personalization.

Phase 2: AI-Powered Icebreaker Generation

Make.com triggers daily and processes leads that don't have icebreakers yet. For each lead, we:

• Pull all enrichment data from the Google Sheet row
• Send to OpenAI GPT-4 with a custom prompt that includes:
  • The prospect's LinkedIn headline and job title
  • Their company name and industry
  • Any recent LinkedIn activity or posts
  • Context about RSL Media Hub's services (AI automation, CRM, marketing)
• Generate a 2-3 sentence custom icebreaker that:
  • References something specific about their role or company
  • Shows we've done our research
  • Creates a natural bridge to our offering
  • Sounds conversational, not salesy

Phase 3: Automated Sheet Update

The AI-generated icebreaker is automatically written back to the Google Sheet in a dedicated column. Now the sales team has:

• A fully researched, personalized opening line for each prospect
• Context on why that icebreaker was chosen
• Ready-to-use content that can be copy-pasted into email tools
• Consistent quality across all outreach

The Results: 325 Hours Recovered Annually

The impact was immediate and transformative for our outreach process:

Time Savings

• Before: 8 minutes per personalized email (research + writing)
• After: 30 seconds per email (review AI-generated icebreaker)
• Time saved: 7.5 minutes per email (94% reduction)
• Weekly time saved: 6.25 hours (based on 50 emails/week)
• Annual time saved: 325 hours (that's over 8 full work weeks!)

Financial Impact

• Weekly savings: $834 (6.25 hours × $133.50/hour)
• Annual savings: $43,387
• Build time investment: 3 hours ($400)
• Break-even: After just 29 emails (less than 1 week)
• First-year ROI: 10,746%

Quality & Scalability Wins

• Consistent quality: Every icebreaker is thoughtful and context-aware
• No writer's block: AI generates fresh angles every time
• Scales infinitely: 10 leads or 1,000 leads, same effort
• Better response rates: Personalized emails get 5-8X higher replies (industry benchmark)
• Team leverage: Junior sales reps can sound like senior researchers

Real Example

Who This Is For: B2B Sales Teams & Agencies

This automation is perfect for anyone running cold email campaigns who wants to maintain quality while scaling volume:

Ideal Use Cases

• B2B Sales Teams: Sending 50-500 cold emails per week to decision-makers
• Marketing Agencies: Running outbound campaigns for multiple clients
• Consultants & Freelancers: Building new business pipelines with limited time
• SaaS Companies: Reaching out to prospects with personalized product messaging
• Recruiters: Personalizing outreach to passive candidates at scale

Requirements

• Access to LinkedIn enrichment data ( Apollo, Clay, Sales Navigator, etc.)
• Google Sheets for lead management
• Make.com account (free tier works for smaller volumes)
• OpenAI API access (costs ~$0.01-0.03 per personalized icebreaker)

The "We Use What We Sell" Philosophy

This automation started as an internal tool because we needed it ourselves. We were running cold outreach campaigns for RSL Media Hub and hitting the same scaling wall every sales team hits: personalization takes too long.

After seeing the results internally, we started building similar systems for clients. Now it's one of our most requested automations because the ROI is undeniable and the setup is simple.

The best part? This same system can be customized for any industry or outreach strategy. We've deployed variations for:

• Real estate agents reaching out to property owners
• Recruiters personalizing candidate outreach
• E-commerce brands doing B2B wholesale outreach
• Consultants building thought leadership connections