Learn How Walmart Used Neo4j to Achieve Sustainable Competitive Advantage via RecommendationsIn a little over 50 years, Walmart grew from a small, family-owned business to become the world’s largest public corporation, with over 2 million employees and annual revenues of $470 billion.

But Walmart didn’t just become the world’s largest retailer by chance. Rather, they understood their customers’ needs for personalized and relevant product recommendations. In order to build that sort of real-time recommendation engine, they used Neo4j to get ahead of their competition.

In this series on sustainable competitive advantage, we’ll cover how graph databases give your enterprise an edge when it comes to insights from data relationships. In past weeks, we’ve discussed how graph databases ensure sustainable competitive advantage, different approaches to developing applications with connected data and how Gamesys leveraged Neo4j for competitive advantage.

This week, we’ll take a closer look at how Walmart increased their retail competitive advantage by using Neo4j to power real-time product recommendations.

Building a Real-Time Recommendation Engine with Neo4j


In its drive to provide the best customer web experience, Walmart knew it needed to optimize online shopping recommendations. After all, shoppers expect finely tuned, highly personalized recommendations and react coolly to one-size-fits-all suggestions.

This new user experience required a database that would connect masses of complex buyer and product data to gain super-fast insight into customer needs and product trends.

Walmart’s team substituted a complex batch process with Neo4j, the perfect tool for real-time product recommendations.

“A relational database wasn’t satisfying our requirements about performance and simplicity, due to the complexity of our queries,” explained Walmart Software Developer Marcos Walda, of the eCommerce-Brazil group. “Neo4j helps us understand our online shoppers’ behavior and the relationship between our customers and products, providing a perfect tool for real-time product recommendations.”

By design, graph databases can quickly query customers’ past purchases, as well as instantly capture any new interests shown in the customer’s 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 easily to outperform relational and other NoSQL database management systems.

The Bottom Line: Walmart Gains Retail Competitive Advantage


Walmart is now using Neo4j to make sense of online shoppers’ behavior in order to be able to optimize-up and cross-sell major product lines in core markets. Specifically, Neo4j has been deployed in its remarketing application since 2013, run by the company’s eCommerce IT team based in Brazil.

“With Neo4j we could substitute a complex batch process that we used to prepare our relational database with a simple and real-time graph database,” Marcos explained. “We could build a simple and real-time recommendation system with low latency queries.”

Marcos concluded: “As the current market leader in graph databases, and with enterprise features for scalability and availability, Neo4j is the right choice to meet our demands.”

Not only did the real-time recommendation engine suggest better products for Walmart’s ecommerce customers, but Walmart achieved a significant and sustainable competitive advantage over other online retailers who offered less-relevant product recommendations.


Download this white paper, Sustainable Competitive Advantage: Creating Business Value through Data Relationships, and discover how your company can use graph database technology to leave your competition behind.

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About the Author

Kamille Nixon, Product Team

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Kamille Nixon provides marketing savvy and thought leadership about the positive impact of good technology design on business goals. Prior to this, Kamille identified through original research the trend that data governance would become a major market force in systems architecture and data modeling. She guided a leading database tools company to successfully tailor its offering for data governance before the rest of the market recognized the trend.


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