Why Collaborative Filtering Hits a Ceiling
Collaborative filtering works well for high-volume, low-context decisions (what to recommend next based on similar users). It fails for high-value, high-context CX decisions because it cannot account for the full richness of the customer relationship — recent service interactions, stated preferences, lifecycle stage, real-time inventory constraints, and cross-channel behaviour. GraphRAG-powered personalisation addresses this by grounding every decision in a semantic knowledge graph that preserves the complete customer context.
WTA’s Predictive Personalisation Architecture
WTA’s standard personalisation engine uses Azure AI Search for hybrid vector retrieval, Azure Cosmos DB for real-time customer context with TTL-based stale data pruning, Microsoft Agent Framework 1.0 for next-best-action agent orchestration, and Langfuse for recommendation quality evaluation against conversion and satisfaction baselines. The knowledge graph (Neo4j or Azure Cosmos DB Gremlin API) preserves entity relationships across products, customers, interactions, and channels — enabling contextually grounded recommendations that are also explainable for GDPR compliance. See how WTA builds AI-enhanced experience and personalisation platforms for enterprise clients.
Frequently Asked Questions
What is the difference between collaborative filtering and GraphRAG-powered personalisation? Collaborative filtering matches users based on similar behaviour patterns. GraphRAG-powered personalisation traverses a knowledge graph of the full customer relationship — including recent service history, stated preferences, lifecycle stage, and cross-channel behaviour — enabling contextually grounded decisions that collaborative filtering cannot achieve.
How does WTA measure the impact of predictive personalisation? WTA tracks conversion rate lift, average order value improvement, customer satisfaction score (CSAT), and churn reduction as primary personalisation ROI metrics. All metrics are baselined before deployment and tracked in real time via Azure Monitor dashboards.
How long does WTA take to build a production personalisation engine? WTA delivers production-grade GraphRAG-powered personalisation engines in 90 days — from knowledge graph design and data ingestion through agent deployment, A/B testing, and live observability.
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