Primer

What problem does Primer solve, and who benefits?

Primer helps B2B marketers reach their true buying audience across channels like Meta, Google, LinkedIn, and Reddit - where traditional targeting doesn't work. Most ad platforms are built for B2C, meaning they rely on cookies, emails, or interest-based signals that miss B2B buyers. Primer solves this by making B2B identity resolution and audience targeting work reliably across those same consumer-grade ad platforms. Our best-fit customers are growth-stage B2B SaaS and services companies running $100-$500K/month in paid media who care deeply about efficiency, attribution, and proving channel ROI.

What makes Primer stand out in the B2B marketing space?

Primer sits between a company's CRM and their ad platforms - turning complex customer and firmographic data into high-match, high-performing audiences. Where other tools focus on lead scoring or account intent, we focus on activation - actually getting those segments live across platforms with measurable lift. We're also one of the only B2B ad platforms that provides end-to-end feedback loops, showing which audiences, companies, and segments are driving real conversions.

How does PredictLeads add value?

PredictLeads gives us a deeper and more current understanding of company activity — what technologies they use, which markets they're entering, and what signals they're showing publicly. That data makes our audiences sharper and more dynamic. For example, we can build "companies using HubSpot but not Salesforce" or "companies adopting Snowflake in the past six months." As a result, our customers see higher match rates, tighter targeting, and reduced wasted spend - particularly when layering tech adoption signals with intent or firmographic filters.

Can you share examples of how Technologies data is used?

Absolutely. Common use cases include:

• Building segments like “companies using Drift or Intercom” to target marketers in conversational AI.
• Excluding companies already using a competitor's technology (e.g., targeting Shopify stores not using Klaviyo).
• Enriching website traffic reveal data - e.g., “Visitors using HubSpot and Cloudflare.”
• Powering dynamic suppression lists when a company adopts a client's own product.

Are there any specific filters that are most commonly used together with Technologies filter?

Yes. The most common combinations are:

• Industry + Employee Size + Technologies
• Job Titles + Technologies
• Revenue + Region + Technologies

This gives marketers balance between precision and reach - using tech stack as a high-intent signal layered with core firmographics.

Which industries benefit the most from using the Technologies filter?

Software, IT services, and SaaS dominate - especially those selling into marketing, sales, and data teams. We also see strong adoption among cybersecurity, cloud infrastructure, and analytics vendors who rely heavily on tech-stack-based qualification.

Are you able to estimate how the Technologies filter affects targeting accuracy, audience size, or wasted ad spend?

In our internal testing, adding Technologies filters typically:

• Improves targeting accuracy by 20-30%, measured by qualified lead rates and CRM matchbacks.
• Reduces wasted ad spend by 15-25%, especially when excluding incompatible or irrelevant stacks.
• Shrinks audience size slightly (by ~10-15%) - but those smaller audiences tend to deliver higher conversion rates.

Why did you choose PredictLeads over other data providers?

Coverage, freshness, and precision. PredictLeads updates faster than most tech-tracking providers and includes historical adoption timelines, which let us model "recent adopters" versus "long-time users." Their APIs are also lightweight and easy to integrate with our audience builder, which reduces latency in syncing data to ad platforms.

In short: better signal quality and operational simplicity.

What are the biggest challenges ahead for B2B marketing, and how can PredictLeads help solve them?

B2B marketing is shifting from static account lists to real-time, signal-based targeting. The biggest challenge is unifying identity, intent, and engagement data into something actionable across walled gardens. PredictLeads helps bridge that gap by keeping our view of the market current - ensuring our customers' audiences evolve as companies grow, switch tools, or show buying signals.

Is there any feature or dataset you'd like PredictLeads to add that would help you even more?

We'd love to see:

• Historical deprecation tracking - when companies stop using a tool, not just when they adopt it.
• More global coverage - especially EMEA and APAC.