Predictive Intelligence Systems for Global Businesses

Machine learning demand forecasting trains a model on historical sales data - typically 12-24 months of daily or weekly sales by product and location - plus additional signals: seasonality patterns (day of week, week of month, month of year), promotional events, price changes, and external factors (weather, economic indicators). The model learns the relationship between these inputs and observed demand, then applies that learned relationship to future input values to generate demand forecasts. ML models outperform traditional methods (moving average, exponential smoothing) by capturing non-linear relationships, multiple interacting seasonal patterns, and promotional effects simultaneously. The output is a demand forecast per SKU per location per time period, with confidence intervals showing the range of expected demand.

A churn prediction model requires: (1) Historical customer data covering 12-24 months with clear churn event labels (which customers cancelled, and when). (2) Behavioural data capturing customer interactions over time - logins, feature usage, API calls, content consumption, or purchase activity. (3) Account attributes - plan type, company size, contract length, account age, and geographic data. (4) Support and communication data - support ticket volume, email open rates, and NPS scores if available. The minimum viable dataset is approximately 500-1,000 historical churns to train a statistically reliable model. More historical data (2,000+ churns) allows the model to learn more subtle patterns and improve accuracy.

SHAP (SHapley Additive exPlanations) is a framework for explaining machine learning model predictions - showing which input features drove each individual prediction and by how much. For a customer churn score of 78%, SHAP might show: 'Login frequency dropped 65% in the last 30 days (+0.32 impact on score), 3 support tickets this week (+0.18 impact), plan renewal due in 42 days (+0.12 impact), account is 3 years old (-0.08 impact - stabilising factor).' SHAP makes predictions actionable (the account manager knows to address the engagement drop and the upcoming renewal), builds user trust (users understand why the model flagged this customer), and enables model debugging (if the model consistently uses unexpected features, something may be wrong with the data).

Predictive model accuracy is measured differently for different problem types. Classification models (churn prediction, lead scoring, fraud detection) are measured by AUC-ROC (a perfect model scores 1.0; random guessing scores 0.5; production models typically achieve 0.75-0.90) and precision-recall at the operating threshold. Regression and forecasting models (demand forecasting, sales forecasting) are measured by MAPE (Mean Absolute Percentage Error - an 8% MAPE means the forecast is off by 8% on average) and RMSE. All accuracy numbers must be measured on a held-out test set - data the model never saw during training. Evolution Infosystem targets 80%+ AUC-ROC for classification models and 85%+ forecast accuracy for demand forecasting on held-out test sets before deployment.

Data leakage in machine learning is when information about the target outcome inadvertently appears in the training features - making the model appear more accurate than it actually is in production, where that information is not available. A classic example: building a churn prediction model and accidentally including the customer's cancellation date as a feature. The model learns to predict churn perfectly (because the cancellation date is the direct label), but this feature does not exist in production where you are trying to predict future churn. In production, the model performs no better than chance. Leakage is particularly common in time-series problems where future data is accidentally included in features for past prediction points. Evolution Infosystem conducts systematic leakage audits on every feature before model training.

A single prediction model (e.g., churn prediction or lead scoring) takes 6-10 weeks from data assessment to production deployment. Timeline breakdown: data assessment and preparation (2 weeks), feature engineering pipeline (2 weeks), model training and selection (1-2 weeks), explainability and calibration (1 week), API and dashboard development (2 weeks), integration testing (1 week). A multi-model predictive intelligence platform with 3-4 prediction systems takes 4-6 months. Timeline drivers: data quality (clean, labeled data accelerates all phases), integration complexity (connecting predictions to CRM/ERP/operational systems), and number of models.

Demand forecasting, customer churn prediction, lead scoring, predictive maintenance, customer lifetime value prediction, credit and risk scoring, inventory optimisation ML, and sales forecasting AI.

XGBoost and LightGBM for tabular prediction (churn, lead scoring, credit risk); Prophet and skforecast for time-series forecasting; LSTM (PyTorch) for sequential sensor data; BG/NBD + Gamma-Gamma for CLV; Isolation Forest for anomaly detection.

Yes. Every prediction and classification model includes SHAP (SHapley Additive exPlanations) values showing which input features drove each individual prediction and by how much - making predictions interpretable for business users and regulators.

85%+ forecast accuracy (MAPE under 12%) on held-out test data for demand forecasting models; 80%+ AUC-ROC on held-out test data for classification models (churn, lead scoring, credit risk).

Yes. Every deployed model includes monitoring using Evidently AI for data and prediction drift detection, scheduled accuracy evaluation as new outcome labels become available, and automated retraining alerts when performance falls below configured thresholds.