Casos: Varejo
A Neo4j é a tecnologia referência para o varejo
O uso de dados conectados está no centro da transformação digital no varejo. Ao construir um mecanismo de recomendação de produtos ou promoções, personalizar as experiências do cliente ou reorganizar sua cadeia de fornecimento para atender às demandas do cliente de entrega no mesmo dia, você enfrenta desafios que exigem a capacidade de aproveitar as conexões de várias fontes de dados diferentes, e tudo isso em tempo real.
E a melhor tecnologia para enfrentar estes desafios é o banco de dados grafos nativo da Neo4j.
O uso de conexões de dados não é uma tarefa trivial, pois exige a capacidade de incorporar e analisar dados de várias fontes diferentes (por exemplo, dados de produtos, clientes, estoques, fornecedores, logística e sentimento social). A Neo4j foi desenvolvida especificamente para armazenar e processar essas relações de dados de várias fontes.
Os varejistas já perceberam que, em particular, os mecanismos de recomendação são as principais ferramentas que influenciam tanto a experiência do usuário quanto a receita. A Neo4j permitiu que gigantes do varejo como o eBay transformassem seus negócios, fornecendo a seus clientes recomendações direcionadas, personalização, recomendações de produtos e promoções, tudo isso em tempo real.
Fast Track
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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.
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Real-Time Recommendations
Learn how real-time recommendations increases revenues, optimizes margins, and improve customer experiences.
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Powering Recommendations with a Graph Database
Learn how retail giants like eBay are using graph databases to power their real-time recommendation engines.
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Neo4j Retail Customers
Customer Spotlight: Fortune 50 Retailer
This top US retailer used Neo4j to revolutionize and reinvent its website's real-time promotions engine. Thanks to its Neo4j-based solution, the company set an all-time record in online sales
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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
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.