AI-powered personalization at massive scale
Industry:
Luxury Retail
Revenue:
$6 Billion annually
Scale:
5M+ customers
Queries:
45M+ monthly
Saks Fifth Avenue, a premier luxury retailer, faced the challenge of delivering personalized shopping experiences to millions of customers while maintaining the high standards expected in luxury retail. With 5 million active customers generating 45 million product recommendation queries monthly, they needed a database solution that could handle massive scale while providing real-time, AI-powered personalization. SurrealDB's vector search and graph capabilities enabled them to transform their e-commerce platform into an intelligent, responsive system that understands each customer's unique preferences and shopping patterns.
Key challenges
Massive scale requirements
Processing 45 million recommendation queries monthly for 5 million customers required unprecedented database performance and scalability.
Low conversion rates
Despite significant web traffic, conversion rates were stagnant at 1.5%, far below industry benchmarks for luxury retail.
Real-time personalization
Generic product recommendations led to disengaged customers. They needed millisecond response times for personalized suggestions.
Complex data relationships
Customer preferences, purchase history, browsing patterns, and product relationships were spread across siloed systems, making unified analysis impossible.
Solutions
Massive-scale vector search
Implemented SurrealDB's vector search capabilities to process 45 million recommendation queries monthly with sub-100ms response times, enabling real-time AI-powered personalization.
Graph-based customer intelligence
Leveraged SurrealDB's graph database features to map complex relationships between 5 million customers, their purchase history, browsing patterns, and product preferences.
Unified data platform
Consolidated customer data from multiple touchpoints into a single, scalable platform, eliminating data silos and enabling comprehensive customer insights.
AI-enhanced recommendation engine
Integrated large language models (LLMs) with SurrealDB's vector capabilities to deliver contextually aware, personalized product recommendations that adapt to customer behavior in real-time.
Results
Massive scale achieved
45M queries
Successfully processes 45 million recommendation queries monthly for 5 million customers with consistent sub-100ms response times.
Conversion rate surge
1.5% → 4%
Conversion rates improved from 1.5% to 4%, representing a 167% increase and millions in additional annual revenue.
Customer retention boost
↑ 30%
Personalized experiences fostered customer loyalty, with repeat purchases increasing by 30% across their 5 million customer base.
Perfect reliability
99.99% uptime
Maintained 99.99% uptime even during peak shopping periods, processing millions of queries without performance degradation.
