Chat with us!We built a predictive analytics platform that forecasts demand, flags churn risk, and surfaces actionable insights from the client's data — turning histori...
We finally plan around what is coming instead of explaining what already went wrong.
Director of Analytics
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.
| Project Detail | Information |
|---|---|
| Project Type | Web |
| Industry | Retail |
| Technologies | Python / scikit-learn, Prophet / time-series models, dbt + warehouse, FastAPI, React, Airflow |
| Delivery Partner | mTouch Labs |
| Primary Outcome | The business shifted from reacting to anticipating, improving inventory and retention outcomes. |
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.
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 platform delivers demand forecasts, churn-risk scores, and automated insight alerts, with model monitoring to keep predictions trustworthy.
Forecasting and churn models
Demand forecasting
Unified, modeled data pipeline
Prediction-serving APIs
Analytics dashboards
Scheduled training and refresh
Consolidated sales, customer, and operations data.
Built predictive features for demand and churn.
Trained, validated, and calibrated models.
Designed decision-focused views and proactive alerts.
Automated retraining and drift monitoring.
The business shifted from reacting to anticipating, improving inventory and retention outcomes.
A predictive analytics platform converted scattered historical data into reliable forecasts and early warnings that drive better decisions.
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View case studyWhat businesses ask us most often about this project and how we built it.
It is a system that uses machine learning on your historical data to forecast future outcomes — like demand or churn — and surface them as actionable insights and alerts.
Common use cases include demand and sales forecasting, churn risk, anomaly detection, and other outcome predictions tailored to your data.
Accuracy depends on data quality, but in this project forecast error fell 31% versus the prior approach, with ongoing monitoring to maintain it.
Yes. We add MLOps with scheduled retraining and drift detection so predictions remain trustworthy.
Yes. We unify your sales, customer, and operations sources into a modeled pipeline that feeds the models and dashboards.
mTouch Labs combines AI-powered development with deep industry expertise to deliver solutions faster.
