Predictive Analytics & Modelling Malta

The specific data depends on the prediction target. Demand forecasting needs historical sales, pricing, and promotional data. Churn prediction needs customer interaction, transaction, and engagement data. Generally, you need at least 12-18 months of historical data with the outcome you want to predict. We assess your data availability during the scoping phase.

Accuracy varies by prediction type and data quality. Demand forecasting typically achieves 85-95% accuracy within confidence bands. Churn prediction models reach 70-85% AUC depending on available signals. Risk scoring models often outperform traditional scorecards by 10-20%. We set accuracy expectations during scoping and validate against business-relevant metrics.

Yes, model explainability is a core requirement for business adoption and regulatory compliance. We use SHAP values, feature importance analysis, and partial dependence plots to explain what factors drive individual predictions. For regulated industries, we provide documentation that satisfies explainability requirements.

Production models score new data automatically through API calls, batch scoring jobs, or embedded integration. A churn model scores every customer daily, updating risk flags in your CRM. A demand forecast refreshes weekly, feeding planning systems. We design deployment architecture based on your latency and integration requirements.

Model drift is inevitable as business conditions change. Our monitoring infrastructure tracks prediction accuracy continuously and alerts your team when performance degrades beyond configured thresholds. Retraining workflows refresh the model with recent data, and the updated model is validated before replacing the production version.

Simple predictive models like demand forecasting can be developed in 4-6 weeks. Complex models requiring extensive feature engineering, custom algorithms, or integration with production systems typically take 8-16 weeks. We deliver iteratively, providing early results for stakeholder feedback before finalising the production model.

Yes, our models are deployed as production services that your business users consume through dashboards, CRM integrations, and automated workflows without needing to understand the underlying data science. We handle the technical complexity while delivering predictions in formats your team can act on immediately.

Predictive analytics tells you what is likely to happen. Prescriptive analytics tells you what to do about it. A predictive model might forecast that a customer has 70% churn risk. Prescriptive analytics recommends which specific retention offer to send based on that customer's preferences and the expected ROI of different interventions.