Using a
property graph model to surface relevant content to users is now common practice for many digital experiences, from social media to retail. While no graph technology solution has been implemented at Nordstrom for personalization (
as of today), a one-step Markov chain, or transition matrix, was implemented in October 2017 to provide shoppers with a personalized sort of Homepage content on the mobile web experience and yielded significant conversion lift. While the transition matrix implementation proved successful in one instance, the approach is incredibly hard to scale or iterate upon using relational data structures.
To expand upon this proven success our Summer 2018 Nordstrom Hackathon team,
Graphathon, built a
neo4j graph database with our website clicksteam data. The MVP graph model included product view and purchase data connected by shopper interactions for adult men’s shoes. Our demonstrated use case for the graph associates similar shoppers via live clickstream data in order to present
personalized product recommendations of various strategies.