Predictive Analytics Services | ML Models

Demand forecasting models for inventory replenishment, staffing scheduling, and capacity planning -- the decisions where being wrong by 20% has direct cost consequences. Trained on your historical transaction data combined with seasonality patterns (day-of-week, month, holiday effects), promotional calendar, and relevant external signals (weather, economic indicators, competitor pricing where available). Time-series models (Prophet, SARIMA, LSTM) selected based on your data characteristics: dataset length, seasonality complexity, and whether the future is structurally similar to the past. Weekly, daily, or hourly forecast granularity depending on lead time requirements and operational decision frequency. Forecast output delivered to your inventory system, workforce scheduling tool, or operations dashboard via API or data pipeline. Confidence intervals accompany every forecast so your operations team knows when to trust the model and when external judgment should override it.

Churn risk scoring for every customer or account, updated on a defined cadence (weekly for SaaS, monthly for enterprise accounts), so your retention team is working from a current prioritized list rather than a gut-feel about which accounts look shaky. Models trained on your historical churn data and the behavioral signals that predict it: login frequency trends, feature usage breadth (accounts using fewer features than average tend to churn earlier), support ticket volume and sentiment, payment delays, and contract renewal engagement. Gradient boosting models (XGBoost, LightGBM) handle the mix of numerical, categorical, and behavioral features typical in subscription churn datasets. Risk scores delivered to your CRM (Salesforce, HubSpot, Gainsight) so your customer success managers see a risk score on every account dashboard without leaving their workflow. Segment-level analysis identifies which customer profiles churn earliest -- enabling targeted onboarding changes for high-risk cohorts before they reach the retention-intervention stage.

Pipeline-to-close probability models trained on your historical CRM data and win/loss outcomes -- not the standard opportunity stage probability percentages your CRM assigns by default, but a model that learns the specific factors that predict closes in your sales motion: deal age at each stage, engagement signals (email response rate, demo attendance, proposal opens), competitive displacement patterns, and rep-specific conversion rates by product and segment. Lead scoring models trained on your historical conversion data separate high-intent inbound leads from noise, enabling your SDRs to prioritize contacts more likely to convert. Revenue forecasting at account, region, product line, or rep level aggregates deal-level probabilities into the range your leadership needs for resource planning. Risk-adjusted pipeline views replace the single-point forecasts that are consistently wrong in both directions.

XGBoost and LightGBM gradient boosting handle the mixed feature types in CRM data well -- categorical fields like product line and segment, numerical fields like deal value and days in stage, and derived behavioral features from engagement tracking. SHAP (SHapley Additive exPlanations) values explain why each deal received its probability score: a sales rep can see that a specific deal is scored low because it has been in the proposal stage 14 days longer than historical wins at this deal size, not just that the score is 32%. Cross-validation uses time-series split rather than random split for temporal CRM data -- evaluating model performance on future periods that the model never trained on prevents the false accuracy of models that learn from data that wouldn't have been available at prediction time. MAPE and RMSE evaluation metrics quantify forecast error in business terms: a revenue forecast with 8% MAPE means the model's monthly revenue prediction is typically within 8% of actual. MLflow tracks every experiment -- feature set, hyperparameters, and evaluation metrics -- so the model registry records which version is in production and why it was selected. Confidence intervals accompany every forecast so leadership can distinguish a high-confidence Q3 projection from a wide-range estimate where external factors dominate.

Statistical anomaly detection for the operational data streams where unusual patterns indicate a problem worth investigating: transaction fraud signals (unusually large transactions, unusual frequency from a single account, unusual geographic patterns), equipment sensor readings that precede failure (temperature drift, vibration increase, current draw deviation), user behavior that indicates account compromise (login from new geography immediately before large transaction), and process metrics that signal impending bottlenecks (queue depth growth, processing latency increase). Isolation Forest, Autoencoder, and LSTM-based anomaly detection models selected based on whether the anomalies are point anomalies (single unusual events), contextual anomalies (events unusual given current context), or collective anomalies (sequences of events that are individually normal but collectively unusual). Alert routing sends anomaly notifications with the specific signal and supporting context to the operations or fraud team -- not a generic alert that requires expert interpretation to act on.

Equipment failure prediction for manufacturing lines, logistics fleets, HVAC and facility systems, and any asset class where unplanned downtime has a material cost and where the failure is preceded by detectable signals in sensor data. Models trained on vibration, temperature, pressure, current draw, and acoustic sensor readings combined with maintenance records and failure history -- learning the specific pattern of sensor drift that precedes bearing failure, motor overheat, or hydraulic system degradation on your specific equipment types. Remaining useful life (RUL) estimates quantify how long each asset is expected to operate before requiring maintenance, enabling condition-based scheduling that replaces both fixed-interval servicing (replacing components that still have useful life) and run-to-failure (replacing components after they've already caused downtime). Integration with your CMMS (IBM Maximo, SAP PM) or ERP triggers work order creation automatically when RUL falls below a configurable threshold.

Recommendation systems that surface the most relevant products, content, or next actions for each user based on their behavior history and similarity to other users with known outcomes. Collaborative filtering (matrix factorization, neural collaborative filtering) for platforms with sufficient user interaction data to learn from co-purchase and co-engagement patterns. Content-based models for cold-start situations where a new user or new item has no interaction history. Hybrid models that combine both approaches, weighted based on the available data for each recommendation context. A/B testing framework with holdout groups validates that the recommendation model's lift in click-through, conversion, or engagement is statistically significant before full deployment -- preventing the false precision of models that look good on offline metrics but don't improve business outcomes. Integration with your e-commerce platform (Shopify, Magento), content platform, or mobile app via recommendation API.