Case Study: Bringing Structure to Multi-Brand Product Data

One of the UK’s "Big Four" retailers uses Bluestone PIM to orchestrate a vast product universe that spans grocery, fashion, and home merchandise. The platform supports multiple retail brands and product ranges within a single data ecosystem.

The organisation operates across three major product domains:

• Groceries
• Clothing
• General Merchandise (including home, electronics, and garden products)

Each category has completely different product data requirements. Food products require nutritional values and allergen information. Clothing relies on size guides, fabrics, and variant management. General merchandise includes technical specifications and compatibility attributes.

To manage this complexity, the retailer implemented three dedicated Bluestone PIM instances. Each instance is optimised for a specific product domain while still supporting a unified data strategy.

Instead of operating fragmented product data systems, the retailer established a unified product data platform powered by automation and AI-driven enrichment. The result is a high-velocity product data engine capable of supporting rapid weekly grocery updates alongside seasonal fashion launches.

Thousands of new SKUs now reach digital shelves every week with consistent and reliable product data.

Key Strategic Focus Areas

• Multi-Brand Product Data Orchestration
  The retailer manages dramatically different product data structures across categories. Bluestone PIM enables clear separation between product domains while maintaining central governance and consistent workflows.

• AI-Driven Product Enrichment at Scale
  AI automation generates product descriptions, attributes, and metadata automatically. This reduces manual work for merchandising teams and speeds up catalogue updates during seasonal launches.

• Digital Asset Synchronisation
  A custom DAM-to-PIM integration automatically links product photography, marketing assets, and compliance documents to the correct SKUs across web and mobile channels.

Before implementing Bluestone PIM, product information was spread across multiple internal systems maintained by different departments.

This fragmented system environment created operational friction across several areas:

• Inconsistent product information between channels
• Slow supplier onboarding processes
• Manual product enrichment workflows
• Limited visibility across product data ownership

At the same time, the retailer needed to manage large-scale catalogue complexity. The organisation maintains more than 700 product attributes across 4 different brands, covering multiple retail divisions and digital channels.

Maintaining clarity across product structures was another challenge. When food, fashion, and general merchandise attributes exist in the same environment, category hierarchies quickly become difficult for teams to navigate.

The organisation required a product data platform capable of supporting both scale and structural clarity, without slowing down product launches.

The implementation focused on building a composable product data architecture that separates product domains while maintaining a unified governance framework.

Separation of Product Domains

Three Bluestone PIM environments were deployed to support the retailer’s main divisions:

• Grocery products
• Clothing products
• General merchandise products

This separation prevents attribute conflicts and keeps product editing interfaces relevant for each business unit.

Integration Architecture

Bluestone PIM was positioned as the single source of truth for product data.

Supplier systems and PLM platforms send product data through ingestion layers that clean, standardise, and normalise incoming information before it enters the PIM environment. This prevents low-quality data from reaching downstream systems.

Once products are enriched and approved, the data is distributed through APIs to digital commerce platforms, search engines, and other applications supporting the retailer’s online experience.

Implementation Timeline

The implementation forms part of a broader three-year digital transformation programme (2024–2026) focused on modernising the retailer’s product data infrastructure and improving scalability across its omnichannel ecosystem.

Custom PIM Extensions: A dedicated plugin was developed to support large-scale asset management and manual product imports, giving teams better control over high-volume product content updates.

Enterprise-Scale Catalogue Management: The platform manages more than 960,000 SKUs with over 700 product attributes, supporting rapid catalogue growth while maintaining high platform performance.

Multi-Context Product Data Distribution: Product information is published to multiple digital contexts, ensuring the correct data is delivered to the appropriate markets, channels, and customer touchpoints.

The retailer implemented a MACH-based PIM architecture built on microservices, API-first integrations, cloud infrastructure, and headless commerce principles.

This architecture decouples product data from legacy backend systems and creates a flexible data layer that feeds all digital channels.

Bluestone PIM acts as the central product data hub, delivering structured product information through API endpoints to search platforms, e-commerce systems, and other digital services across the organisation.

Bluestone PIM supports the retailer’s digital strategy by enabling faster product onboarding and improved data governance.

Key capabilities include:

High-Speed Product Imports: Automated ingestion pipelines allow supplier product data to be imported and validated quickly without manual builds.

AI-Based Attribute Identification: Image recognition technologies help identify product attributes such as colour, category, and style, which can then be applied to product records automatically.

Centralised Data Governance: The platform maintains a single source of truth across grocery, clothing, and general merchandise divisions.

Operational Efficiency: Retail media asset approvals that previously required one to three weeks can now be completed in approximately 90 seconds through automated validation workflows.

Cost Reduction: AI-powered compliance audits reduced verification costs to roughly £0.01 per audit.

Data Consistency at Scale: The platform maintains consistent product information for more than one million enriched products across web and mobile channels.

The retailer continues to expand its use of Bluestone PIM as part of its long-term digital commerce strategy.

Future initiatives include:

AI-Driven Product Discovery: The organisation is developing personalised product recommendations powered by enriched product data and intelligent search capabilities.

Sustainability Transparency: Upcoming initiatives will introduce carbon footprint and packaging impact data directly into the product experience.

Catalogue Expansion: The next phase will expand SKU capacity and support the full integration of general merchandise categories into the digital commerce ecosystem.