From Opaque Match Lists to Scalable Lookalikes — How Verkada 3.9× Pipeline Efficiency

Verkada replaced a brittle list-buying workflow with transparent ICP targeting and scalable lookalikes. The result was a Meta program that could expand reach without sacrificing persona precision.

‍

The Overarching Challenge

Verkada’s product is persona-sensitive.

If the ad reaches the wrong role — marketing, HR, or unrelated teams — the message fails instantly.

Before Primer, their audience strategy relied on:

• ZoomInfo and Apollo exports uploaded to ad platforms as matched lists
• Salesforce contact lists
• Purchased third-party datasets from bespoke web scraping teams
• Clay enrichment workflows

Even after enrichment, match rates were low.

"We’d enrich the lists and maybe go from 20% to 25% match rate — but it was still guesswork.”

The deeper problem was opacity. Some vendors would sell datasets without even revealing who was inside them until after purchase.

“You pay $8,000 for a list and hope it works.”

If a campaign fatigued, the only solution was buying another list and repeating the process.

As Patrick puts it,

“Contact data becomes stale quickly, and maintaining an accurate, high-match-rate database is a full-time effort. Primer’s database has consistently proven to be accurate and up to date, especially for niche roles in the physical security space. That allows us to stay focused on our products, promotional campaigns, and engaged leads who are interested in our business, rather than spending time managing a ton of cold contacts who may never convert.”

Verkada’s Audience Scaling Framework

Verkada replaced their list-driven workflow with a four-step framework built around precision seeds, algorithmic scale, and faster experimentation.

Step 1 — Build a High-Integrity Seed Audience

Instead of importing large datasets, Verkada began defining audiences directly from ICP logic.

Using Primer’s demographic filters, they created audiences based on:

• job title contains logic
• Industry and headcount filters
• explicit role exclusions

Using Primer’s demographic selector, Verkada could define audiences using job titles and industries and preview who those audiences would include.

Instead of buying lists blindly, the team could see the composition of their targeting before launching campaigns — making the process far more transparent than traditional data vendors.

Patrick summarizes the strategy simply:

“Less is more. It’s better to have a small audience of really good titles — even if it’s 50K — than a big audience of mixed results.”

A smaller but higher-integrity seed produced stronger match lists and more reliable lookalike performance.

Audience design rules

• Keep seed audiences ~50k–100k users
• Avoid mixed roles (e.g., generic “Manager” titles)
• Exclude operational roles that inflate audience size

Step 2 — Scale Through Lookalike Expansion

Once the seed audience performs, Verkada scales reach through a structured lookalike ladder. Typical expansion sequence:

1. Launch 1% lookalike (highest similarity)
2. Once performance flattens, add 2% lookalike
3. Expand to 3% and 4% as frequency rises
4. When larger audiences fatigue, rotate back to 1%

This produces audiences of 1–2M users that maintain persona relevance. “That audience of two million doesn’t go stale fast, and by the time you're on the 4% lookalike, you can go back to the 1% again.”

This solved a core scaling challenge in B2B paid social: audience staleness. Instead of small audiences exhausting quickly, the lookalike system created millions of qualified prospects.

Because the seed audience was tightly aligned with Verkada’s ICP, the lookalikes consistently outperformed other audience foundations.

Step 3 — Feed Meta Higher-Value Signals

Verkada didn’t rely on targeting alone. They also improved the signals Meta’s algorithm learned from.

Instead of optimizing purely for form submissions, the team sends lead score values from Salesforce back to Meta, allowing the platform to prioritize higher-quality prospects.

“We’re always fine-tuning the lead score and the value we send back.”

For example, personas that historically drive stronger pipeline — such as IT Directors and physical security leaders — receive higher value weights.

This allows Meta to optimize not just for conversions, but for pipeline quality and expected deal value.

Over time, this feedback loop helps the algorithm find more buyers that resemble Verkada’s best customers.

Step 4 — Let Creative Do Its Job

With a large enough audience, Verkada could finally let Meta’s algorithm operate properly. Without lookalikes, audiences are often too small to support algorithmic learning.

“You can’t really make use of Meta’s algorithm when your audience is only five figures.”

Now campaigns run with a controlled but diverse creative set:

• 3–5 creatives per ad set
• different formats (static, video, GIF) - creative diversity helps
• regular creative swaps to avoid fatigue

Launching 20+ creatives at once proved unnecessary. “The algorithm will just find the best three or four anyway.” This balance allows Meta to learn while still keeping creative experimentation structured.

Verkada also uses a creative performance as a diagnostic signal. Patrick’s rule:

• Strong creative performs across multiple audiences
• Weak creative fails everywhere

This makes it easier to isolate whether performance changes are caused by targeting or creative. If a creative performs well across several audiences, the message is likely strong. If it fails everywhere, the problem is usually the creative itself — not the targeting.

Step 5 — Accelerate Mid-Funnel Experimentation

The biggest operational change was speed. Before Primer, testing a niche audience required:

• sourcing contacts
• enriching emails
• uploading match lists
• waiting for match results

That process made experimentation slow and expensive. With ICP targeting directly inside Primer, Verkada could launch audiences immediately.

This unlocked a new MOFU testing workflow:

1. Launch a content offer to a niche audience
2. Let Meta optimize engagement
3. Validate which segments respond
4. Retarget those audiences with demo offers

One example was a law-enforcement webinar campaign. The audience responded extremely well.

“Law-enforcement folks just ate it up.”

The campaign generated $20 CPL leads, which were later retargeted with demo offers to drive pipeline. This workflow allowed Verkada to test 4x times faster than before.

Key Principles from Verkada’s Playbook

The Outcome

Switching from purchased match lists to ICP-driven audiences fundamentally changed Verkada’s paid-social performance. Results across campaigns showed:

• 3.9× higher pipe-to-spend efficiency
• Faster audience testing cycles
• More reliable lookalike scaling
• Sustainable campaigns without repeated list purchases

What had once been a brittle, list-driven system became a scalable acquisition engine.

Verkada transformed Meta from a channel dependent on purchased contacts into a predictable pipeline machine.

Run This Play

This framework works best for:

• B2B companies with a large TAM and multiple buyer personas
• Teams relying on purchased contact lists
• Marketers facing audience fatigue on Meta
• Organizations that want scalable lookalike performance

Start with a precise ICP seed. Then let the algorithm scale from there.