Predictive Analytics & ML Solutions
Machine learning models that turn your historical data into forward-looking decisions — demand forecasting, customer churn prediction, fraud detection, price optimization, and recommendation engines. We handle the entire pipeline from raw data to production dashboards integrated with your BI tools.
Tech Stack
Key Features
- Custom ML models with 85–95% accuracy for your KPIs
- Time-series forecasting for demand, revenue, and inventory
- Customer churn and lifetime-value prediction models
- Fraud detection with real-time scoring APIs
- Explainability reports (SHAP values, feature importance)
- Automated retraining pipelines with performance drift alerts
Service Level
Frequently Asked Questions
How much historical data is needed to build a predictive ML model?
For most business forecasting use cases (demand, churn, revenue), a minimum of 12 months of historical data is required to capture seasonality. 24–36 months is preferred for stable model performance. For fraud detection or anomaly models, the critical factor is the ratio of positive examples (fraud events) to total records — we need at least 500–1,000 fraud instances, regardless of the total dataset size. We assess your data quality and volume before scoping the project.
What accuracy can we expect from a predictive model?
Accuracy depends heavily on data quality, feature richness, and the signal-to-noise ratio in your historical data. For well-scoped use cases with clean, complete historical data, we typically achieve 85–95% accuracy on held-out test sets. We always provide a baseline comparison (what accuracy you'd get with a naive model like 'predict last year') so you can evaluate the marginal value of the ML model.
How often does the model need to be retrained?
We implement automated retraining pipelines that monitor model performance on live data and trigger retraining when accuracy degrades beyond a defined threshold (data drift detection). For most business models, a monthly retraining schedule is sufficient. For high-velocity environments (e-commerce, fraud), weekly or real-time retraining may be required. All retraining pipelines include A/B testing so the new model is validated against the incumbent before replacing it.
How do you explain the model's predictions to non-technical stakeholders?
Every model we deliver includes SHAP (SHapley Additive exPlanations) value reports that show, for any individual prediction, which features drove the outcome and by how much. For a churn prediction, this means you can see: 'This customer was flagged at 78% churn risk because: login frequency dropped 60% (high weight), support ticket volume increased 3× (high weight), last payment was late (medium weight).' These reports are integrated into your BI dashboard or exported as Excel-friendly summaries for business teams.
How does the model integrate with our existing BI tools and systems?
ML model outputs are delivered as a real-time scoring API (FastAPI endpoint returning predictions in JSON) that your existing systems can call, or as a batch pipeline that writes predictions to a database table your BI tools (Power BI, Tableau, Looker) already connect to. We do not require you to change your BI stack — the model plugs into your existing data infrastructure. For CRM integration, we can push churn scores or lead quality scores directly into Salesforce, HubSpot, or Pipedrive field values.
Ready to Get Started?
Let's discuss your project requirements and how we can help you achieve your goals with our predictive analytics & ml solutions expertise.