ML Revenue Intelligence Platform

Discover the true sources of your revenue. Our specialized ML models analyze payment patterns to uncover hidden customer journeys and attribute revenue with unprecedented accuracy.

Revenue Intelligence

ML-Powered Attribution Without Tracking

Our specialized ML models analyze 40+ behavioral signals from your payment data to accurately attribute revenue sources—no tracking pixels required.

Attribution Accuracy

+12%
94.2%

Revenue source identification without tracking pixels

Optimize top-performing channels

$45K revenue properly attributed

Hidden Revenue Found

+28%
$38,500

Previously untracked revenue from dark social & direct

Increase LinkedIn investment by 40%

2.3x higher LTV from LinkedIn traffic

Churn Prediction

+15%
89% accurate

ML models predict cancellations 30 days early

Engage 12 at-risk accounts

Save $8.4K MRR this month

How ML Attribution Works

No tracking pixels. Just intelligent pattern recognition.

Payment Data

Stripe, PayPal, etc.

Pattern Analysis

40+ behavioral signals

ML Classification

4 specialized models

Attribution

94%+ accuracy

Result: Know exactly which campaigns drive revenue, even from dark social and word-of-mouth.

Revenue Discovery

Uncover Hidden Revenue Sources

Our ML models reveal the true sources of your revenue by analyzing customer behavior patterns—finding revenue you didn't know you had.

ML-Discovered Revenue Segments

High-Intent Converters
LTV: $4,200+45%
15% of revenueLinkedIn + Webinar
Product-Led Growth
LTV: $1,800+30%
35% of revenueFree Trial + Docs
Word-of-Mouth
LTV: $2,400+25%
25% of revenueDark Social
Paid Acquisition
LTV: $900+10%
25% of revenueGoogle Ads

Real-Time ML Insights

Revenue Attribution Discovery
High Impact

Discovery: $125K misattributed revenue

LinkedIn driving 3x more enterprise deals than tracked
Behavioral Pattern Alert
High Impact

Discovery: 40% of revenue from untracked sources

Implement enhanced attribution for dark social
LTV Prediction Update
Medium Impact

Discovery: Customer segments redefined

Focus on multi-touch journey customers
6 Enterprise ML Models

Purpose-Built ML Models

Six production-grade ML models working in harmony to deliver predictive insights with industry-leading 98% combined accuracy.

XGBoost

94%
Accuracy

Gradient boosting for churn prediction and customer segmentation

Churn Prediction
Extreme performance
Customer segmentation
Risk scoring

Random Forest

92%
Accuracy

Ensemble learning for customer lifetime value prediction

LTV Prediction
Robust predictions
Feature importance
Cohort analysis

LSTM Network

96%
Accuracy

Deep learning for revenue forecasting and time-series analysis

Revenue Forecasting
Memory cells
Sequential patterns
30-90 day forecasts

Markov Chain

98%
Accuracy

Multi-touch attribution modeling for complex customer journeys

Attribution
Journey mapping
Channel analysis
Dark social detection

Logistic Regression

89%
Accuracy

Binary classification for conversion probability scoring

Conversion Scoring
Propensity scoring
Lead qualification
Fast inference

Isolation Forest

91%
Accuracy

Anomaly detection for fraud prevention and pattern identification

Anomaly Detection
Fraud detection
Outlier identification
Pattern alerts