Data Science
Predictive & Customer Intelligence
4–12 weeksRisk scoringDemo
HealthyWatchAt risk · prioritize
Customers at risk
6
prioritized to act
Model accuracy
0.91
on new data (AUC)
Hits vs. random
3.2×
in the riskiest 10% (lift)
Turn historical data into foresight: which customers will leave and when, which credits carry the most risk, which leads to prioritize, and which behavioral groups your portfolio actually splits into.
We combine three capabilities into one intelligence system over your customers and operations — predictive scoring (who, and how likely), survival analysis (when and for how long, CLV), and behavioral segmentation (which actionable groups). Technically: supervised classification and regression (gradient boosting, random forests, neural networks) with out-of-time validation, calibration, and SHAP; Kaplan-Meier and Cox models for time-to-event and competing risks; clustering (K-Means, HDBSCAN) with stability analysis and UMAP visualization.
Use cases
- —You want to anticipate which customers are most likely to churn — and when
- —You need to prioritize leads or accounts by conversion likelihood, or assess credit risk
- —You want to detect fraud or unusual behavior before it escalates
- —You want to understand which behavioral groups your portfolio splits into and serve them differently
- —You need to estimate useful life and CLV of customers, contracts, or assets with time rigor
Deliverables
- —Trained models with out-of-time validation and baseline comparison
- —SHAP interpretability report (global importance and individual explanations)
- —Survival curves (Kaplan-Meier) and a risk model with interpretable factors
- —Segmentation model with profiles, stability analysis, and UMAP visualizations for leadership
- —CLV modeling by segment with confidence intervals
- —Inference API or integration, with data-drift and performance monitoring