Using a property graph model to surface relevant content is now common practice in many digital experiences. At Nordstrom, a one-step Markov chain implementation provided personalized homepage content on the mobile web experience, but scaling and iterating was challenging with relational data structures. To expand upon this success, a Neo4j graph database was built with website clickstream data, including product view and purchase data connected by shopper interactions for adult men's shoes. The team presented a simple initial concept for their graph that took into consideration just two steps of the customer journey: the current style being viewed and the most recent style viewed before that. They found all the paths of shoppers who had done that before and moved on to another item, suggesting the top item based on the number of paths observed. The team used Cypher queries to implement a "Viewed Next" feature, taking into consideration more context and returning the most popular styles. Recommendations are now a key navigation and discoverability tool for online shoppers, providing the right path forward and making a huge impact on customer experience and shopper outcomes. Graphs are well-suited for mapping a customer journey like this, allowing for efficient querying and aggregation. The team plans to continue investigating how to get graph-based strategies on par with their current system and apply this experience to other domains, such as modeling hand-curated outfits and creating tools for stylists and merchants.