ConversionOS
AI-Native Revenue Intelligence Framework
ConversionOS is an open-source framework for building ML-powered conversion optimization pipelines. It abstracts the patterns used in production systems that generated 34,000 qualified leads from 2.3 million prospects using XGBoost in BigQuery ML.
Built by an enterprise architect who has deployed these systems across telecom, crypto, fintech, healthcare, and hospitality.
What ConversionOS Does
ConversionOS provides a modular, production-tested architecture for:
| Capability | What It Solves |
|---|---|
| Propensity Scoring | Score every prospect/customer on conversion likelihood using gradient-boosted models |
| Audience Activation | Translate ML scores into 200+ CDP segments for targeted campaign execution |
| Conversion Optimization | Feed 4-tier conversion values ($1/$10/$50/$240) to ad platforms for value-based bidding |
| Churn Prevention | Predict at-risk customers 30-60 days before traditional signals appear |
| Multi-Touch Attribution | Distribute conversion credit across 7 channels using data-driven models |
Design Principles
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ML-first, not rule-first. Every scoring decision is powered by a trained model, not a business rule. Rules define thresholds; models define scores.
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BigQuery-native. Feature engineering, model training, and scoring happen inside BigQuery. No data movement, no latency, no infrastructure to manage.
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CDP as the activation layer. ML outputs don't sit in dashboards — they flow into a CDP (ActionIQ, Segment, etc.) where they become actionable audiences.
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Value-based optimization. Don't just count conversions — value them. The 4-tier conversion value framework ($1/$10/$50/$240) transforms how ad platforms allocate budget.
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Measurement closes the loop. Attribution and incrementality testing validate that ML-driven targeting actually produces incremental revenue.
Architecture at a Glance
Data Sources → Feature Engineering → ML Training → Scoring → CDP Activation → Measurement
↑ |
└──────────────────────── Feedback Loop ──────────────────────────────────────┘
See the full Architecture page for the system diagram.
Who This Is For
- Marketing Data Engineers building ML pipelines for acquisition and retention
- Marketing Technologists connecting ML outputs to CDPs and ad platforms
- Data Scientists looking for production-tested feature engineering patterns
- VPs/CTOs evaluating AI-native marketing architecture