Walmart Optimizes Customer Experience with Real-time Recommendations
In its drive to provide the best web experience for its customers, Walmart wanted to optimize its online recommendations. These days, shoppers expect finely tuned, highly personalized recommendations and react less well to one-size-fits-all suggestions. But to achieve this requires data products that can connect masses of complex buyer and product data (and connected data in general) to gain insight into customer needs and product trends, super-fast. Walmart recognized the challenge it faced in delivering this with traditional relational database technology.
As Marcos explained: “A relational database wasn’t satisfying our requirements about performance and simplicity, due the complexity of our queries.” To address this, Marcos’ team decided to use Neo4j, a graph database, for which category Neo4j is the market leader.
By design, graph databases can quickly query customers’ past purchases, as well as instantly capture any new interests shown in the customers’ current online visit – essential for making real-time recommendations. Matching historical and session data in this way is trivial for graph databases like Neo4j, enabling them to easily outperform relational and other ‘NoSQL’ data products.
Marcos had first seen Neo4j demonstrated at the QCON New York conference in 2012 and he recognized: “With Neo4j, we could substitute a heavy batch process with a simple and real-time graph database.” Based on this, his team trialed the technology, citing positive results that helped finalize the decision. The verdict? “It suits our needs very well.”