Hyper personalization in sales outreach: go beyond “Hi {{firstname}}”

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Inboxes today are flooded with generic outreach that barely scratches the surface, no wonder prospects ignore most cold emails. If you want to break through, hyper-personalization is your competitive advantage. It’s about using AI and comprehensive web data to create messages that feel genuinely crafted for each individual recipient, not mass-distributed to hundreds.

This guide demonstrates exactly how to scale that level of personal touch, combining mass personalization, strategic enrichment, and AI sales outreach so your emails actually get read and generate responses.

Review these personalized email examples to see how effective multi-line AI icebreakers can be.

Tutorial: mass-personalized cold emails with multi-line AI icebreakers

This isn’t your typical spray-and-pray cold email approach. It’s a systematic methodology from Nick Saraev, founder of Maker School, designed to deliver genuine hyper-personalization at scale.

Masterclass

I deep-personalized 1000+ cold emails using this AI system – Video originally published by Nick Saraev

1. Source and collect your leads

Start by building a clean, targeted list.

Use a lead database like Apollo.io to identify your ideal audience.
Then extract that data efficiently with a third-party scraper like Apify.
Export essential details: first name, last name, email, website, LinkedIn URL, headline, location, everything you’ll later use for AI-powered personalization.

2. Prepare your enrichment workflow

Set up a streamlined Google Sheet to manage everything:

One tab for raw search URLs
Another for enriched leads. Feed your scraped data here so you can easily track progress and build towards automation.

3. Filter for qualified, reachable leads

Before investing time in personalization:

Keep only leads with valid emails and functional website URLs.
Expect to filter aggressively, sometimes 70% of scraped data doesn’t meet quality standards.

4. Scrape and analyze company websites

For each qualified lead:

Once you’ve filtered for qualified leads with working websites, it’s time to extract meaningful context.

Start by sending an HTTP GET request to each company’s homepage to verify the URL is live and responsive.

You can use tools like Python’s requests library, cURL, or Apify.
Ensure you follow redirects (301, 302) to reach the final landing page.
Then parse the HTML to extract all internal links (about, team, blog, etc.)

This provides high-value pages you can analyze later for relevant context and personalization insights.

Filter for relative links to stay within their site, and remove duplicates to avoid redundancy.

5. Crawl and summarize key website pages with AI

Next, extract content from these pages.

Convert HTML to markdown, it’s more cost-effective for LLM processing.
Send to GPT (or your preferred LLM) with a clear prompt: “Summarize this page in two paragraphs, straightforward, no sales language. If empty, respond ‘no content.‘”
Limit inputs to around 5,000 characters to keep your data manageable.

6. Aggregate company intelligence

Consolidate all page summaries into one Google Sheet for each company.

This comprehensive context enables your AI to create genuine personalization, far beyond the standard ‘saw you’re hiring!’ outreach.

7. Generate multi-line, authentically custom icebreakers with AI

Now provide the complete company context to your LLM and request a cold opener that demonstrates genuine research.

Prompt example:

“Write a concise, multi-line opener for a cold email referencing specific, non-obvious details from this company. Sound like a real person who conducted thorough research.“

Guidelines for your AI-driven personalization:

Use shorter company names (“Love Mayo” instead of “Love Mayo Inc.”).
Reference specifics that aren’t immediately obvious, skip the “great website!” generic comments.
Example output:

“Hey Katie, noticed how your team highlights local artists on the blog. Also impressed by your instant property updates feature, really smooth execution. Wanted to share an idea with you…”

8. Push enriched leads and icebreakers back to your campaign sheet

Transfer the icebreakers to your Google Sheet so every lead now includes a multi-line, hyper-personalized opener, the foundation of your mass personalization campaign.

9. Integrate with La Growth Machine for outreach

Import your enriched leads into La Growth Machine.

Use these icebreakers as your opening lines in personalized cold emails, making each message feel individually crafted even though it’s powered by intelligent AI sales outreach.

Expect significant improvements in response rates: 4–10%+ versus the typical 1% for generic emails.

10. Optimize and iterate your personalization strategy

Continuously refine your scraping and AI prompts to handle new site structures or messaging adjustments.
Test different icebreaker styles, or reference multiple contacts for even deeper AI-powered personalization.
Track everything: responses, meetings, pipeline, so your campaigns continuously improve.

The actual work that goes into building out a cold email campaign, if you really want it to perform, is more complex than just scraping leads from Apollo and sending. You need to make your content genuinely customized.

Nick Saraev – Founder @ Maker School

Takeaways

Ultimately, hyper-personalization is how you demonstrate you’re worth a prospect’s time. By using AI to go beyond surface-level details and truly understand each company, you’ll craft cold emails that stand out in crowded inboxes. That’s how you move from ignored messages to meaningful conversations that drive pipeline, revenue, and long-term business relationships.