RevOps

Built on the karpathy/autoresearch loop pattern, this playbook applies autonomous feedback loops to GTM assets — emails, ads, landing pages, nurture flows — tested against revenue-linked metrics. Replace manual A/B testing with agent-driven loops that compound ICP-specific learnings across channels.

Build a unified revenue intelligence system that merges billing, CRM, product analytics, and enrichment data into a single account-level view — with AI-powered ICP scoring, churn prediction, and expansion signals. Replace fragmented dashboards with one system that tells you which accounts to save, grow, and acquire.

Train ad platform algorithms to find retained users — not bouncers — by optimizing CAPI for Synthetic Conversion Events correlated with Day 7 retention. Composite signals triggered when users hit their 'Product Aha Moment,' not just signups. CAPI + Pixel recovers up to 19% more attributed conversions and reduces CPA by up to 13%.

Most lead scoring is theatre. Sales ignores the scores because they do not trust them. This playbook builds a system that separates fit (who they are) from intent (what they are doing), validates against LTV, and routes leads with context. Top scoring leads convert at 5-6x the rate of bottom scoring leads.

Centralize and clean lead, product usage, and enrichment data into a single source of truth (Data Warehouse) to power precise targeting.

Turn incomplete lead records into complete prospect profiles automatically at lead creation. This playbook covers six enrichment patterns across hygiene, triggers, outbound waterfall, live enrichment, intent layering, and reverse ETL.

ICE (Impact x Confidence x Ease) gives GTM teams a weekly operating cadence to prioritize experiments, ship the top 3-5, and calibrate scores against pipeline outcomes.

Automate competitor monitoring and battle-card updates so reps always get current, actionable intelligence in their workflow before competitive calls.

Merge GA4, CRM, and Ads data into one integrated Looker/Data Studio dashboard without requiring a full data warehouse implementation.

Deploy AI agents to crawl websites and extract specific data fields that are not available via standard enrichment APIs.

Convert anonymous, high-intent pricing page visitors into qualified leads and route them to sales for immediate follow-up.

Build ICP fit scoring from realized customer value (LTV/NRR), not assumptions, so GTM teams prioritize accounts that actually become high-value customers.

Sequence enrichment providers by segment and field economics to improve match rates while cutting wasted credits and redundant lookups.

Use job postings as explicit buying-intent triggers, map roles to use cases, and launch personalized outreach while urgency and budget signals are fresh.

Build a signal-led ABM program that selects named accounts from verified purchasing behavior — import records, hiring patterns, permit filings, tech stack changes — instead of firmographic guesswork. Coordinate personalized, multi-channel outreach across the buying committee over 90 days with account-level measurement that tracks meetings and pipeline, not impressions. Replace $50K-$350K/year ABM platforms with a $3K/month stack that delivers 15-25% account-to-meeting conversion and 40-60% higher win rates.