Most GTM teams run experiments. Few turn them into systems.
I spent 15+ years building revenue systems, and I've learned one thing: the difference between a one-off win and scalable growth is systemization.
Here's the framework I used to turn experiments into a scalable pipeline across multiple opportunities—and how you can apply it.
The Results
The Framework: Experiment → Measure → Systemize → Scale
This isn't theory. It's the process I used to build a GTM engine from scratch at a Series A cybersecurity company. Here's how it works:
Phase 1: Experiment
Run small, focused experiments to test hypotheses.
Example: The "Reddit Air Strike"
I ran a "secret shopper" experiment on Reddit to test a hypothesis: Problem-aware buyers discuss their challenges publicly before they're solution-aware.
The Experiment:
- Monitored technical forums (Reddit, HN, Stack Overflow) for problem discussions
- Engaged as a "secret shopper" to validate messaging
- Measured response rates and lead quality
The Result: 3 high-value, problem-aware leads in 48 hours. Messaging validated. Hypothesis confirmed.
Key Insight: Don't scale what you haven't validated. Run experiments first, measure results, then decide what to systemize.
Phase 2: Measure
Track what actually works, not what feels good.
For the Reddit Air Strike, I measured:
- Lead Quality: Were they problem-aware? (Yes: 100%)
- Response Rate: How many engaged? (3 out of 5 = 60%)
- Time to Engagement: How fast? (48 hours)
- Conversion Potential: Did they fit ICP? (Yes: 3/3)
The Metric That Mattered: 60% response rate vs. 2-3% for generic cold email. That's a 20x improvement.
Phase 3: Systemize
Turn the experiment into a repeatable play.
Once I validated the Reddit Air Strike worked, I systemized it:
- Created Scripts: Tailored messaging for different problem types
- Built Process: Defined signal sources, engagement criteria, follow-up sequence
- Documented Play: Wrote playbook with step-by-step instructions
- Set Metrics: Defined success criteria (response rate, lead quality, conversion)
The System:
- Monitor forums for problem-aware discussions
- Score leads based on ICP fit and problem awareness
- Engage with tailored messaging
- Track results and iterate
Phase 4: Scale
Delegate to the team and automate what you can.
Once systemized, I scaled the Reddit Air Strike:
- Delegated to SDR: Trained them on the play, gave them scripts
- Automated Detection: Built Python scripts to monitor forums automatically
- Scaled Sources: Expanded from Reddit to HN, Stack Overflow, X/Twitter
- Measured at Scale: Tracked results across all channels
The Result: What started as a one-off experiment became a repeatable play generating 15+ leads per week.
Real Examples: How I Applied This Framework
Example 1: X/Twitter Drippi Campaign
Experiment: Automated DMs on X/Twitter to engage technical audiences
Measure: 15+ technical conversations, 1 lead to proposal stage, 1 key meeting booked
Systemize: Created DM templates, defined target personas, set engagement criteria
Scale: Automated detection, delegated to SDR, scaled across multiple accounts
Result: Ongoing pipeline generation from social channels
Example 2: SE Proactive Engagement
Experiment: Sales Engineer engaging in public forums to establish thought leadership
Measure: Inbound technical leads, brand recognition, meeting bookings
Systemize: Created engagement playbook, defined forums, set posting schedule
Scale: Expanded to multiple SEs, automated content suggestions
Result: Consistent inbound technical leads
Example 3: Closed-Lost Campaign
Experiment: Re-engage historical closed-lost opportunities with new asset (analyst report)
Measure: Re-engagement rate, meeting bookings, pipeline value
Systemize: Created asset library, defined re-engagement criteria, built sequence
Scale: Automated asset distribution, delegated to SDR, tracked results
Result: Re-activated $100K+ in pipeline
The Pipeline: How Systemization Built Scale
By systemizing experiments, I built a significant pipeline across 25+ opportunities:
- Multiple in Qualification: High-value opportunities in discovery
- Several in Negotiation: Deals moving toward close
- Multiple in PoC: Proof of concept stage
- Others in Various Stages: Pipeline across the funnel
Key Insight: Each systemized play contributed to the pipeline. No single play did it all—but together, they built a scalable engine.
How to Apply This Framework
Step 1: Run Experiments
Start small. Test one hypothesis at a time. Don't try to scale before you validate.
Questions to Ask:
- What hypothesis am I testing?
- How will I measure success?
- What's the minimum viable experiment?
Step 2: Measure Results
Track what actually works. Not what feels good. Not what you hope works. What actually works.
Metrics That Matter:
- Response rates (vs. baseline)
- Lead quality (ICP fit, problem awareness)
- Conversion rates (experiment → meeting → pipeline)
- Time to result (how fast does it work?)
Step 3: Systemize What Works
Turn successful experiments into repeatable plays. Document the process. Create scripts. Define metrics.
What to Systemize:
- Scripts and messaging
- Process and workflow
- Success criteria
- Tools and automation
Step 4: Scale Through Delegation and Automation
Once systemized, scale it. Delegate to the team. Automate what you can. Measure at scale.
How to Scale:
- Train team on the play
- Automate detection and enrichment
- Expand to new channels/sources
- Track results and iterate
The "Mad Scientist" Approach
I call this the "Mad Scientist" approach: run experiments, measure results, systemize what works, scale what scales.
It's not about being perfect. It's about being systematic. Every experiment teaches you something. Every systemized play builds your engine.
The Key Insight: GTM isn't about running one perfect campaign. It's about building a system that turns experiments into repeatable plays that scale.
What This Means for You
If you're running GTM experiments, you're already halfway there. The question is: are you systemizing what works?
Start Here:
- Look at your last 3 experiments
- Which one worked best? (highest response rate, best leads, fastest results)
- Systemize it: create scripts, document process, define metrics
- Scale it: delegate to team, automate what you can, measure at scale
That's how you build a GTM engine. Not with one perfect campaign. With a system that turns experiments into repeatable plays.
Want to Build Your Own GTM System?
I've built systems that replaced 10 SDRs ($424K savings) and grew pipeline 160%. If you're building GTM systems, let's connect.