Case Study: Walmart achieves real-time personalized recommendations with Neo4j

Walmart uses Neo4j to optimize customer experience with personal recommendations

Walmart, the world’s largest retailer with hundreds of millions of weekly customers and a global eCommerce presence, needed to deliver fast, highly personalized “you may also like” recommendations for its Brazilian online shoppers. Traditional relational databases and a heavy batch process couldn’t meet the performance and complexity required to connect customer and product data in real time.

Walmart’s eCommerce team adopted Neo4j, a graph database, to replace the batch workflow with low-latency, real-time recommendation queries that combine historical and session data. Deployed in production since 2013, the solution improved understanding of shopper behavior and customer–product relationships, enabling optimized up-sell and cross-sell in core markets with enterprise-grade scalability.

Walmart

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