Retail & Neo4j: Personalized Promotion & Product Recommendations
By Bryce Merkl Sasaki
November 6, 2017|
Reading time: 6 minutes
Today’s retailers face a number of complex and emerging challenges. Thanks to lower overhead and higher volume, online behemoths like Amazon can deliver products faster and at a lower price, driving smaller retailers out of business. In order to compete,… Read more →
Keywords: Connected Data • Cyber Monday • ecommerce • graph technology • personalized promotion • product recommendations • real-time recommendations • Recommendation Engine • Retail • Walmart
How Walmart Uses Neo4j for Retail Competitive Advantage
By Bryce Merkl Sasaki
December 14, 2015|
Reading time: 3 minutes
In 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… Read more →
Keywords: competitive advantage • ecommerce • graph database • neo4j • product recommendations • real-time recommendation • Recommendation Engine • retail competitive advantage • sustainable competitive advantage • Walmart
Graph Databases in the Enterprise: Real-Time Recommendation Engines
By Bryce Merkl Sasaki
September 28, 2015|
Reading time: 3 minutes
Whether your enterprise operates in the retail, social, services or media sector, offering your users highly targeted, real-time recommendations is essential to maximizing customer value and staying competitive. Unlike other business data, recommendations must be inductive and contextual in order… Read more →
Keywords: browsing history • eBay • Graph Databases • logistics • product recommendations • purchase history • real-time recommendations • Recommendation Engine • use case • Walmart
Have a Graph Question?
Reach out and connect with the Neo4j staff.
Stack OverflowSlack
Contact Us
Share your Graph Story?
Email us: content@neo4j.com