Custom signals from 30,000+ accounts. 8x lift on top 10%. Backtested & live with < 10 hours RevOps time.

The Situation

Company: Enterprise Cybersecurity Company
Contact: VP of Revenue Operations
Stage: Scaling sales team +50%, maintaining revenue per headcount
Challenge: 41% of rep activity on low-value accounts; no way to validate quality at scale.

The RevOps team knew they had whitespace. Many "good fit" accounts had no activity in 2 years - deduplicated and cross-referenced across systems. But they couldn't just tell reps to "work better accounts" - they needed to explain why signals actually predicted success.

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The Core Challenges:

• Reactive to risks: Often didn't see risks until it was an emergency, then it was a fire drill. The team wanted to be proactive & attack revenue leakage before it's too late.
• Hypothesis vs. Reality: Intuitions about ICP didn't match historical wins. Some "perfect fit" accounts never converted, while unexpected wins came from outside the traditional ICP.
• Signals Missing from Enrichment: The signals that actually predict wins aren't available in standard enrichment tools - requiring custom signal discovery from websites, job postings, and public data.
• No Validation Before Deployment: Without backtesting, deploying a scoring model meant risking quarters of rep time on unproven "vibes-based" hypotheses.
• Data Scattered Across 8+ Tools: Revenue signals lived across CRM, analytics, and project management tools - making it impossible to get a single view of what actually drove wins.

The Solution

Custom Signal Discovery at Scale

Deepline extracted unstructured data from project management tools, public signals, and news sources to populate CRM and analytics models. This approach identified custom signals from 50,000+ potential accounts to surface ICP indicators that aren't available via standard enrichment. These industry-specific signals - from website content, job postings, public filings, and project management data - revealed patterns that standard enrichment vendors couldn't capture.

Propensity Scoring with Backtesting

Built a scorecard model validated against 2+ years of historical data before deployment. The backtesting revealed an 8x expected value lift on A-tier accounts vs. average, with 2x higher win rates and 5x higher ACV compared to C-tier accounts.

See Around Corners with External Signals

Integrated external market data to explain the "why" behind internal metrics — answering questions like why deal velocity changes, why key verticals shift, and what market trends are driving pipeline changes.

Unified Analytics Across 8+ Tools

Connected CRM, analytics, and project management tools into a single intelligence layer. Extracted unstructured data from project management systems, public signals, and news sources to automatically populate CRM fields and analytics models, eliminating the need to stitch spreadsheets together.

The Results

• 8x higher expected value on A-tier vs. average accounts
• 3.2k net-new high propensity accounts identified with no opps/activity in 2 years (deduplicated & cross-referenced)
• 500+ hours of meetings tied directly to low ACV, low converting accounts
• 5x ACV & 2x win rate on A-tier vs. C-tier
• $1.8M expected revenue increase from re-allocating effort from low to high propensity
• 25+ custom signals not available from enrichment vendors
• < 10 hours customer resources for onboarding AI analytics platform

Before → After

Before

• 41% of rep activity on low-value accounts
• Quick questions took hours + context switching, real insights took months
• Unclear prioritization

After

• Propensity-driven prioritization
• Model backtested against 2 years of data
• Whitespace identified, deduplicated, and prioritizedValidated model in weeks

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The ROI Case

Data Engineering Savings: over $360K/year with AI analytics on validated semantic layer

Platform + Propensity: $1.8M expected revenue increase from rep reallocation

Efficiency: 6 hr/week saved per rep, 10+ hours for analytics roles (finance, RevOps, SalesOps), faster ramp, consistent playbook

Why Deepline?

Speed and accuracy matter.

Validated signals instead of vibes-based GTM.