AI Personalisation Engine Development

Collaborative filtering using customer purchase and browsing history to identify customers with similar behaviour and surface items those customers engaged with. Content-based similarity using product attributes and tags to recommend items with matching characteristics. Hybrid model combining both approaches so recommendations are useful for both new and returning customers. Cold-start handling for new users with no history and new products with no interaction data, using category affinity and item attribute matching as fallback. Real-time scoring API returning ranked recommendations per user in under 100ms so the recommendation layer does not add latency to page load.

Multi-armed bandit or supervised model predicting the best next offer, message, or channel for each individual customer. Configurable action space covering email, push, SMS, in-app messaging, and paid retargeting so the model selects from the channels available in your stack. Business rules overlay to prevent margin-destroying offers being sent to low-LTV customers regardless of model prediction. Daily scoring batch for scheduled campaign use with real-time fallback for trigger-based sends. Action and outcome logging that feeds back into model training so the model improves as campaign data accumulates.

Purchase propensity score per customer updated on a configurable schedule, using recency, frequency, and monetary signals alongside browsing behaviour and category affinity. Churn risk score with configurable threshold that triggers win-back campaign entry automatically. Upgrade propensity for subscription businesses identifying customers likely to convert to a higher tier. LTV prediction for acquisition ROI calculation and bid optimisation in paid media. Scores published to your CDP or CRM so campaign segmentation uses model outputs directly rather than requiring a data team export for each campaign build.

Personalisation API called at email or web render time returning the right hero image, subject line variant, product tile, or offer for each individual user based on their current score and segment membership. Template variable system for integration with email tools including Klaviyo, Salesforce Marketing Cloud, and Braze so personalisation works within your existing send infrastructure. Web personalisation via JavaScript snippet for client-side rendering or server-side integration for frameworks that support it. Fallback content for users with insufficient data so personalised surfaces never return empty results.

Experiment framework for comparing personalised vs. control experiences across email, web, and in-app surfaces. Statistical significance tracking so tests run long enough to produce reliable results before a winner is declared. Holdout group configuration for measuring incremental lift from personalisation vs. no personalisation for contacts who receive default content throughout the experiment. Revenue-per-visitor and conversion-rate lift reporting per experiment. Multi-armed bandit for continuous traffic allocation shifting toward winning variants as data accumulates, without waiting for a fixed experiment duration to end.

Automated data pipeline from your data warehouse or CDP to the model training environment so retraining runs on fresh data without manual data exports. Scheduled model retraining on a configurable cadence -- daily, weekly, or triggered by data volume thresholds. Performance drift detection with alerts when model accuracy degrades below a configurable threshold, so the team knows when the model needs attention rather than discovering it in campaign results. Feature importance reporting that shows which signals drive the model outputs so marketing teams understand why customers receive specific recommendations. Feedback loop from campaign outcomes -- clicks, purchases, conversions -- into model training data so the model learns from real campaign performance.