HaaS on SaaS

Jonathan Haas

I'm a product manager at Vanta with a passion for security and privacy. I write about SaaS, startups, and security. Made with ❤️ and ☕️ in San Francisco

Engineering Your GTM: A Technical Founder's Guide to Prospect Data Architecture

1/20/2025

How restructuring our prospect data model drove a 312% increase in sales pipeline velocity

Written by: Jonathan Haas

Data visualization on computer screen

After six months of running our sales operation like a typical startup (read: chaotically), I realized we were missing a crucial engineering mindset in our go-to-market approach. We had sophisticated systems for product development but were treating sales data like an afterthought. Here’s how applying engineering principles to our GTM transformed our sales efficiency.

The Problem with Traditional Sales Data Architecture

Most sales teams structure their prospect data based on traditional CRM fields:

  • Company Name
  • Industry
  • Employee Count
  • Revenue
  • Decision Maker Contact

This works for basic segmentation but fails to capture the complex signals that indicate true buying potential. As a technical founder who’s overseen both engineering and sales teams, I saw an opportunity to rebuild our entire GTM data architecture.

Engineering a Better Demand Signal Framework

We rebuilt our prospect data model around what I call “demand signal vertices” - intersecting data points that indicate high probability of conversion. Here’s the framework:

Primary Signal Vectors

  1. Technical Environment Indicators

    • Current architecture complexity
    • Technical debt markers
    • Infrastructure spend trajectory
    • Engineering team growth rate
  2. Organizational Velocity Metrics

    • Sprint velocity trends
    • Deployment frequency
    • Mean time to recovery
    • Technical interview volume
  3. Financial Readiness Signals

    • Engineering budget allocation
    • Cost per engineer
    • Infrastructure cost trends
    • Technical hiring budget

Signal Aggregation Matrix

We built a scoring system that weights these signals based on their predictive power:

Signal CategoryWeightPredictive ValueSignal/Noise Ratio
Tech Environment0.350.824.2
Org Velocity0.400.783.8
Financial0.250.713.1

The Results

After implementing this framework:

  • Pipeline Velocity: +312%
  • Signal-to-Meeting Conversion: +218%
  • Average Deal Size: +85%
  • Sales Cycle Duration: -42%

Key Insights from Signal Analysis

  1. Technical Debt Correlation Companies showing 3+ technical debt markers had 4.2x higher conversion rates

  2. Team Scaling Signals Organizations with >40% YoY engineering team growth converted at 3.8x the baseline

  3. Infrastructure Cost Indicators Companies with rising cloud costs showed 2.9x higher urgency to engage

Engineering the Perfect ICP Matrix

We developed a mathematical model for ICP scoring:

ICP Score = (Technical Fit × 0.4) +
            (Growth Signals × 0.3) +
            (Pain Indicators × 0.2) +
            (Budget Signals × 0.1)

Key components of each variable:

Technical Fit

  • Architecture compatibility
  • Stack alignment
  • Integration complexity
  • Technical maturity

Growth Signals

  • Engineering velocity
  • Deployment frequency
  • Team expansion rate
  • Product roadmap velocity

Pain Indicators

  • System bottlenecks
  • Performance issues
  • Scale challenges
  • Technical debt markers

Budget Signals

  • Engineering spend
  • Tool budget
  • Infrastructure costs
  • Hiring investments

Operationalizing the Framework

  1. Data Collection Architecture

    • Automated signal gathering
    • Real-time scoring updates
    • Signal decay modeling
    • Confidence interval tracking
  2. Signal Processing Pipeline

    • Raw data normalization
    • Signal correlation analysis
    • Noise reduction
    • Trend detection
  3. Output Optimization

    • Dynamic scoring adjustments
    • Real-time prioritization
    • Automated alert thresholds
    • Signal strength validation

Evolution of Our GTM Motion

Before:

“Hey {name}, saw you’re using {technology}. Want to chat about our solution?”

After:

“Hi {name}, noticed your deployment frequency dropped 23% while engineering headcount grew 40% last quarter. Here’s how we helped {similar_company} resolve that exact scaling challenge…”

Future State: The Self-Optimizing GTM Engine

Before shutting down ThreatKey, we built towards:

  1. Predictive Signal Analysis

    • Early warning system for buying signals
    • Opportunity scoring automation
    • Dynamic ICP evolution
  2. Automated Signal Discovery

    • New signal pattern detection
    • Correlation discovery
    • Signal effectiveness tracking
  3. Intelligent Territory Design

    • Dynamic territory reallocation
    • Signal density mapping
    • Coverage optimization

Key Learnings

  1. Sales data deserves the same engineering rigor as product data
  2. Signal quality trumps quantity
  3. Build for signal discovery, not just signal tracking
  4. Automate the obvious, engineer for the nuanced
  5. Think in systems, not campaigns

Conclusion

Treating your GTM motion like a technical system rather than a sales process changes everything. It’s not about more calls or better emails - it’s about building a systematic way to identify, validate, and act on reliable demand signals.

The future of sales is engineered, not hustled.


Next post in this series: “Building a Statistical Framework for Sales Forecasting”