Case Studies
Anonymized results from production ConversionOS deployments across three industries.
Telecom ISP: 34K Qualified Leads from ML Scoring
Industry: Broadband / Telecom | Duration: 12 months
Challenge
A major broadband ISP was spending $50M+ annually on customer acquisition with no ML-driven targeting. Campaigns relied on basic demographic selects. Lead quality was inconsistent and CPL was unsustainable.
ConversionOS Deployment
| Module | Implementation |
|---|---|
| Propensity Scoring | XGBoost on 120+ features (credit, address, competitive, behavioral) |
| Google Ads Engine | 4-tier conversion values ($1/$10/$50/$240) fed to Performance Max |
| CDP Audience Taxonomy | 200+ segments in ActionIQ with real-time suppression |
| Attribution Model | 7-channel multi-touch replacing last-click |
Results
| Metric | Before | After |
|---|---|---|
| Prospect targeting | Demographic-only selects | ML propensity scoring |
| Qualified leads identified | ~8K (manual) | 34K (ML-scored) |
| Lead precision yield | Under 0.3% of prospect universe | 1.5% of prospect universe |
| Cost per qualified lead | Baseline | -40% |
| ROAS (Performance Max) | Baseline | +2.8x |
| Active audience segments | ~15 | 200+ |
Crypto Exchange: 3x Conversion Rate via CDP Activation
Industry: Crypto / Blockchain | Duration: 8 months
Challenge
A leading crypto exchange was treating all prospects identically. No segmentation, no personalization, no ML scoring. High-intent users saw the same experience as casual browsers. First-deposit rates were stagnant at 1.2%.
ConversionOS Deployment
| Module | Implementation |
|---|---|
| Propensity Scoring | Deposit propensity model (behavioral + financial features) |
| CDP Audience Taxonomy | 80+ behavioral segments (deposit intent, trading frequency, asset interest) |
| Churn Prevention | Early-stage churn detection for new user onboarding |
Results
| Metric | Before | After |
|---|---|---|
| Segmentation | None (batch and blast) | 80+ behavioral segments |
| Conversion rate | 1.2% | 3.6% (+3x) |
| First-deposit rate | Baseline | +65% |
| Wasted ad spend | Baseline | -40% (real-time suppression) |
| Onboarding completion | One-size-fits-all | ML-personalized journeys |
Financial Services: 40% Churn Reduction
Industry: Fintech | Duration: 6 months
Challenge
A fintech platform was losing customers at 12% monthly churn with no early warning system. Retention efforts were reactive — reaching out only after customers had already left. High-LTV customers received the same (lack of) retention treatment as low-value accounts.
ConversionOS Deployment
| Module | Implementation |
|---|---|
| Churn Prevention | 30/60/90-day risk scores refreshed daily |
| Propensity Scoring | LTV model for retention investment prioritization |
| CDP Audience Taxonomy | Risk-tiered retention segments with automated triggers |
Results
| Metric | Before | After |
|---|---|---|
| Monthly churn rate | 12.0% | 7.2% (-40%) |
| Churn detection | Reactive (post-churn) | 30-day predictive window |
| At-risk outreach timing | After churn signal | Before traditional signals |
| Retention campaign ROI | 1.2x | 3.8x |
| Annual revenue preserved | — | $4.2M |
| High-LTV retention rate | 82% | 94% |