Neo4j is the Go-To Technology for Retail

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 and Walmart to transformed their businesses, providing their customers with routing recommendations, personalization, product recommendations and promotions, all in real time.

Read the Data Sheet

Fast Track

  • Driving Innovation in Retail with Graph Technology

    Discover how real-world retailers use Neo4j to drive innovation in product and promotion recommendations to supply chain visibility.

    Download the white paper
  • 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

Neo4j Retail Customers

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

  • Play Video
    How to Gain Competitive Advantage in Retail using Graphs
  • Play Video
    GraphConnect SF 2015 / Michal Bachman, GraphAware Real-Time Recommendations
  • Play Video
    Building a High Performance Pricing Engine at Marriott

Why Retailers Choose Neo4j

Business Outcomes

Increase Revenue

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.

Mitigate Risk

Graph-based tools are foundational in modern fraud detection, retail logistics and asset management.

Challenges

Real-Time Capabilities

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.

Flexibility

Neo4j easily ingests and processes connections from multiple data sources, solving problems with data stored in disparate silos.

Why Neo4j?

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.

Flexible schema

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.

High availability

The built-in, high-availability features of Neo4j ensure your user data is always available to your mission-critical recommendation engine.