Project Overview
We built a predictive analytics platform that forecasts demand, flags churn risk, and surfaces actionable insights from the client's data — turning historical reporting into forward-looking decisions.
The Challenge
The client had dashboards full of what already happened but no reliable way to anticipate demand or risk. Stockouts and overstock alternated, and churn was noticed only after customers left.
- Reporting was backward-looking only
- Demand swings caused stockouts and overstock
- Churn detected too late to intervene
- Insights buried across disconnected data sources
Our Strategic Approach
We unified the data sources, engineered predictive features, and trained forecasting and churn models, then exposed results through clear, decision-oriented dashboards and alerts.
The Solution We Delivered
The platform delivers demand forecasts, churn-risk scores, and automated insight alerts, with model monitoring to keep predictions trustworthy.
- Demand and sales forecasting
- Churn-risk scoring with drivers
- Automated anomaly and insight alerts
- Unified data pipeline across sources
- Decision-oriented dashboards
- Model monitoring and retraining
Technologies Used
- Python / scikit-learn — Forecasting and churn models
- Prophet / time-series models — Demand forecasting
- dbt + warehouse — Unified, modeled data pipeline
- FastAPI — Prediction-serving APIs
- React — Analytics dashboards
- Airflow — Scheduled training and refresh
Development Process
- Data unification — Consolidated sales, customer, and operations data.
- Feature engineering — Built predictive features for demand and churn.
- Model development — Trained, validated, and calibrated models.
- Dashboards & alerts — Designed decision-focused views and proactive alerts.
- MLOps — Automated retraining and drift monitoring.
Results & Impact
The business shifted from reacting to anticipating, improving inventory and retention outcomes.
- Forecast error reduced by 31%
- Stockout and overstock incidents down materially
- At-risk customers flagged in time to retain
- Decisions made on forward-looking data
🎯 Key Takeaway
A predictive analytics platform converted scattered historical data into reliable forecasts and early warnings that drive better decisions.
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