A leading global retail chain with over 2,000 stores across North America and Europe sought to modernize its customer insights and inventory management capabilities. Despite significant investments in data warehousing and BI tools, the retailer faced fragmented insights across sales, supply chain, and customer loyalty systems.
Traditional analytics and machine learning approaches focused on tabular relationships — customer → transaction → product — and failed to capture the complex network effects that drive modern retail behavior.
The retailer needed to:
Detect cross-store and cross-category purchase influences
Identify high-impact customers and assortments using network-level insights
Optimize inventory replenishment by understanding relational dependencies
Build a fraud detection and recommendation system resilient to sparse and evolving data
Tytan’s AI Innovation team introduced a Graph Machine Learning (Graph ML) framework that connected millions of customer, transaction, and product data points into a dynamic Knowledge Graph.
We used a multi-stage pipeline:
Unified POS, CRM, and supply-chain datasets into a property graph (using Neo4j & Azure Cosmos DB Graph API)
Defined nodes: Customer, Product, Store, Transaction, Category, and Supplier
Created weighted edges based on behavioral and temporal correlations (e.g., “co-purchase within 7 days”, “shared store region”)
Generated node embeddings using GraphSAGE and Node2Vec, capturing similarity and influence between entities
Combined traditional features (e.g., RFM scores) with structural features (e.g., PageRank, centrality)
Trained a Graph Attention Network (GAT) to predict cross-sell probability and next-best-offer recommendations
Used link prediction to uncover hidden associations between customers and products
Visualized relationships through graph dashboards in Power BI and interactive knowledge maps for business users
Delivered explainability through SHAP and attention-based interpretability of GNN models
| Metric | Before | After |
|---|---|---|
| Cross-sell Conversion Rate | 8% | 19% (+137%) |
| Stock-out Incidents | 14% | < 5% |
| Marketing Spend ROI | 2.3× | 3.9× |
| Fraud Detection Precision | 78% | 92% |
| Time to Insight (weekly report cycle) | 3 days | 30 minutes |
Graph ML brought a relational understanding of data, moving beyond traditional row-column correlations to network intelligence.
This enabled:
Context-aware recommendations considering peers’ behaviors and item affinity
Early detection of demand cascades across store networks
Adaptive segmentation powered by community detection algorithms
Data Layer: Azure Synapse + Databricks + Cosmos DB (Graph API)
Graph ML: PyTorch Geometric, DGL, Neo4j Graph Data Science Library
Visualization: Power BI with Graph Connectors, Streamlit for interactive insights
Deployment: Azure ML Pipelines + AKS for scalable GNN serving
The project evolved from a data science pilot into a strategic decision-intelligence platform, now influencing:
Store layout and product placement
Dynamic pricing recommendations
Targeted promotions and loyalty rewards
Supplier network optimization
The client now uses Graph ML as a core component of their retail AI operating model, driving millions in additional revenue and measurable customer retention improvements.
“By connecting the dots through Graph ML, we helped the retailer see relationships that were always there — but invisible to traditional analytics. This network-first view is the foundation of truly intelligent retail.”
— Henry Du, Chief Data Scientist, Tytan Technology Inc.
The next phase includes integrating real-time graph updates, temporal GNNs, and LLM-driven graph query generation — enabling business teams to ask questions in natural language and receive insights drawn directly from the knowledge graph.
If your business is sitting on years of disconnected data, Graph ML can connect it into actionable intelligence.
📩 Reach out to our AI & Data Science team at info@tytantech.com to explore how we can help transform your enterprise.
