Custom signals from 50,000+ accounts. 8x lift on A-tier. Validated before deployment.

The Situation

The RevOps team knew they had whitespace - but so much that prioritization was a nightmare. With > 20k target accounts, and no idea which accounts they were missing, many target accounts with no logged meetings or opportunities in 2 years. But they couldn't just tell reps to "work better accounts" or "do more activities" — they needed to prove which signals actually predicted success.

"We had hypotheses on what makes someone a good customer, and how to prioritize. But the data told a different story."

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The Challenges

• Gap between hypothesis and reality: Some "perfect fit" accounts never converted. Some unexpected wins came from outside the traditional ICP.
• Signals that don't exist in enrichment: Keywords like "loan origination," "KYC," tech stack in job postings.
• No way to validate before deploying: How do you know scoring works before wasting quarters of rep time?
• Data scattered across 8+ tools: Salesforce, Looker, Clari, Adaptive Insights, Monday.com.

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The Solution

Custom Signal Scraping at Scale

Scraped completely custom signals from 50,000+ potential accounts — website keywords, job posting tech stack, regulatory compliance indicators - with zero engineering effort.

"We found a single website keyword that consistently identifies someone as 1.8x more likely to convert. You can't buy that from ZoomInfo."

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Propensity Scoring with Backtesting

Built a scorecard model validated against 2+ years of historical data before deployment.

"The backtesting was the unlock. We knew our scoring model worked before we deployed it to reps."

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See Around Corners

Integrated external market data to explain the "why" behind internal metrics.

"The 'see around corners' capability is real. We're integrating external signals into our workflows — not just reacting to what happened, but anticipating what's coming."

"I used to get blindsided in leadership meetings. 'Why is Financial vertical down?' Now I have the answer before anyone asks."

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What the VP of RevOps Says

"Every GTM tool tells you what happened. This tells you why — and what to do about it before your pipeline tells you it's too late."

"The propensity model doesn't just score accounts — it explains why. Reps trust it because they can see the logic."

"Reps aren't working harder - they're working the right accounts. That's how you maintain efficiency while scaling headcount."

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The Results

• 8x higher expected value on A-tier vs. average accounts
• +8.2k high-propensity accounts identified with no activity in 2 years
• 2x win rate on A-tier vs. C-tier
• 5x ACV on A-tier vs. C-tier
• $1.8M incremental revenue opportunity solely from re-allocation
• 50+ custom signals not available from any enrichment provider
• Eliminate manual RevOps reporting with AI revenue analyst.

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Key Takeaway

Don't guess. Use analytics to validate before you push it to the team.