Overview

What it is: A cold outreach infrastructure + deliverability optimization lab for testing outbound campaigns safely at scale.

Goal: Replace “spray and pray” with a controlled experimental system that tests variables, predicts risk, monitors inbox health, and adapts based on results.

Key outputs: Variant-level performance insights + guardrails that auto-pause when deliverability degrades.

Architecture (5-layer outbound system)

1. Campaign structure + variant engine
   • Campaigns are defined as modular components:
     • ICP (industry, role, size)
     • Offer angle
     • Personalization depth/type
     • Subject line variants
     • Sender domain/inbox
     • Sending windows
   • Generates a structured A/B/n matrix so variables can be evaluated independently.
   • Example tests:
     • Subject A vs B vs C
     • Short vs consultative long
     • Direct ask vs soft question
     • Personalized opener vs generic
2. Deliverability infrastructure layer
   • Protects domain reputation and inbox health.
   • Setup:
     • Multiple sending domains
     • Separate tracking subdomains
     • SPF/DKIM/DMARC
     • Warm-up automation
   • Controls:
     • Daily volume caps
     • Randomized send intervals
     • Throttled ramp schedule
     • Quiet hours enforcement
   • Risk detection + auto-pausing:
     • Bounce rate, spam complaints, open anomalies, blacklist checks
3. Performance tracking + attribution
   • Tracks: delivery, open, click, reply, positive reply, meetings booked, bounce, complaints.
   • Segments by: subject line, ICP, sender domain, send time, copy variant.
   • Enables insights like: “short emails outperform long ones by 18% for SaaS founders.”
4. Simulation + pre-test engine
   • Before scaling, simulates response behavior using AI persona testing.
   • Scores:
     • engagement likelihood
     • spam-risk probability
     • tone alignment (pushy vs helpful)
     • CTA clarity
   • Flags spam-trigger phrases and weak positioning early.
5. Optimization feedback loop
   • Post-send analysis powers automated decisions:
     • Pause poor variants
     • Scale winners
     • Rotate subject lines and angles
     • Adjust volumes
   • Example rules:
     • Reply rate < threshold after 200 sends → pause
     • Bounce rate > 5% → suspend domain
     • Spam complaints > 0.3% → investigate template
   • Feeds winning variables into future campaign templates.

How it works (step-by-step)

1. Campaign initialization
   • Define ICP, offer angle, personalization depth, and variants.
   • System generates a test matrix.
2. Pre-launch validation
   • DNS/auth checks, spam keyword scan, subject scoring, volume schedule.
3. Controlled deployment
   • Sends across multiple inboxes with randomized intervals.
   • Every email gets a unique message ID and is logged per domain.
4. Engagement monitoring
   • Opens tracked.
   • Replies classified (positive/neutral/negative).
   • Bounce/complaint handling triggers in real time.
5. Post-campaign analysis
   • Computes:
     • Reply rate = Replies / Delivered
     • Positive rate = Positive replies / Replies
     • Meeting rate = Meetings / Delivered
   • Produces best: subject line, message length, send window, sending domain.

Key components (conceptual)

• outbound_lab/campaign_builder.py
• outbound_lab/variant_matrix_generator.py
• outbound_lab/domain_health_monitor.py