Today, harnessing connected data is at the core of digital transformation in retail. Whether you’re building a product or promotions recommendation engine, personalizing customer experiences, or re-imagining your supply chain to meet customer demands for same-day delivery — you’re facing challenges that require the ability to leverage connections from many different data sources, and all in real time.
There’s no better technology to meet these challenges than a native graph database technology such as Neo4j.
Harnessing data connections is a non-trivial task, and it requires the ability to incorporate and analyze data from many different sources (e.g., product, customer, inventory, supplier, logistics and social sentiment data). Neo4j is specifically designed to store and process such data relationships across various sources.
Modern retailers realize that powerful recommendation engines, in particular, are core drivers of both user experience and revenue. Neo4j has enabled retailers like eBay, adidas and Walmart to transformed their businesses, providing their customers with routing recommendations, personalization, product recommendations and promotions, all in real time.
Build a Recommendation Engine in Two Minutes
Watch Andreas Kolleger, Senior Product Designer, build a recommendation engine using Neo4j in only two minutes.Watch now
Powering Recommendations with a Graph Database
Learn how companies like eBay and Walmart are using graph databases to power their real-time recommendation engines.Download the white paper
Shaping Up a Fitness Program Recommendation Engine & More
See how Benjamin Nussbaum builds a personalized recommendation engine for BeachBody fitness programs, nutritional supplements and more.Read more
On average, Neo4j processes over 90% of 35M+ daily transactions, each 3-22 hops in 4ms or less.
This top US retailer uses Neo4j to revolutionize and reinvent its real-time promotions engine. Thanks to the newly implemented Neo4j-based solution, during its peak season in 2016, the company set an all-time record in online sales, and also enabled the retailer to become one of the first in the US to offer synchronized in-store and online promotions.
Retail Video Case Studies
Why Retailers Choose Neo4j
Recommendations, personalization and logistics done right all have direct impact on revenues.
Create Higher Engagement
Improved personalization and content recommendations lead to higher user engagement.
Graph-based tools are foundational in modern fraud detection, retail logistics and asset management.
No database technology handles complex queries as efficiently and fast as a native graph database.
Ability to Use Most Recent Transaction Data
No batch processing when querying real-time transaction data.
Neo4j easily ingests and processes connections from multiple data sources, solving problems with data stored in disparate silos.
Native graph store
Unlike relational databases, Neo4j stores interconnected user and purchase data that is neither purely linear nor hierarchical. Neo4j’s native graph storage architecture makes it easier to decipher suggestion data by not forcing intermediate indexing at every turn.
Neo4j’s versatile property graph model makes it easier for organizations to evolve real-time recommendation engines as data types and sources change.
Performance and scalability
Neo4j’s native graph processing engine supports high-performance graph queries on large user datasets to enable real-time decision making.
The built-in, high-availability features of Neo4j ensure your user data is always available to your mission-critical recommendation engine.