Today’s retailers are grappling with unparalleled product complexity. Not only do they manage far more products and lines than ever before, but they also often span multiple channels, and must operate across multiple customer segments and geographies. To understand the scale of this, ASOS adds around 5,000 new items every week.
This growth in product catalogue complexity demands a new approach. One where AI doesn’t just assist human decision-making, it fundamentally reimagines how product data is created, maintained, and leveraged across the entire commerce ecosystem and chain.
AI targeting the operational backbone of retail
Retailers have moved long past simple inventory lists. Now, sustainable competitive advantages come from optimising “behind the scenes” of technology systems, all designed to enable customers to have more seamless experiences. This is a graduation from simply layering AI onto existing customer touchpoints.
Unified commerce requires consolidating all those disparate data sources, from in-store data to rich app data sources, into a single, authoritative product domain. This serves all touchpoints and channels and is a conscious architectural shift taken to eliminate the traditional, channel-specific catalogs model that has served retail for decades. Now, mobile apps, in store-systems, and workforce tools, access the same identical, real-time information.
For example, Wimbledon has kicked off for the summer. They will have the championship store on the grounds, a mobile app where tennis fans can purchase goods, and a core website with products too- all working from the same hub of data on what inventory is available.
However, this unified commerce approach requires high technical requirements. Systems must deliver consistent performance, regardless of channel load, or concurrent workloads, while maintaining data integrity across distributed architectures. Modern document-based platforms excel and thrive in this environment, providing the flexibility to accommodate diverse product structures, whilst maintaining the performance characteristics required for real-time commerce, like busy periods of summer sales or sporting events like Wimbledon or the Olympics.
Product data is more complex than ever before
Contemporary product catalogs are significantly more complex than traditional systems. Modern datasets include everything from video content, customer reviews and images, AI-generated cross-sell recommendations to dynamic pricing data. All of this can vary dramatically across product categories. This complexity compounds the challenge for data professionals to maintain data completeness and accuracy across multiple channels.
If a dress is worn, say by a celebrity at a Wimbledon match, the pressure is then on for the first retailer to go to market with the trending product. Retailers must balance the need for comprehensive product information with the imperative to achieve the fastest time to market. In this instance, the product must have enough detail in the summary for shoppers to be able to find it easily through traditional approaches- gen AI can help supplement product summary speedily and accurately.
One of our MongoDB customers manages over 15 million across diverse store formats. Without unified data architecture and prior to modernising with MongoDB, the retailer struggled with inconsistent pricing, incomplete product information, and customer experience challenges. When self-checkout systems failed during peak periods, the organisation faced significant revenue losses, a consequence of inadequate product data infrastructure.
The stress test of summer sales
Summer sales have begun. They expose the critical importance of robust product catalog operations. During these periods, retailers must simultaneously manage complex promotional pricing, accommodate a surge of traffic, process elevated transaction volumes, and maintain service quality across all touchpoints.
Organisations with traditional catalog systems often struggle to implement promotional strategies effectively, as batch processing cycles cannot accommodate the dynamic pricing requirements of modern promotional campaigns. Real-time price adjustments, inventory-based promotional triggers, and channel-specific promotional strategies require infrastructure capable of processing and distributing updates instantaneously.
Imagine you are a small retailer selling tennis rackets, and an unseeded tennis player using your product has gotten through to the Wimbledon final. Viewers are searching where they might be able to purchase one for themselves. If you are using a traditional system, you may not be able to capitalise on this traffic.
Organisations considering catalog transformation must address several critical factors. First, the technical architecture must accommodate both current requirements and future AI capabilities, including vector search, content generation, and real-time personalization.
Second, the implementation approach should prioritise business continuity while enabling rapid capability development. Phased migration strategies that maintain operational stability while introducing new capabilities incrementally prove most effective.
Third, organisations must consider the integration requirements across their existing technology stack. Modern catalog systems serve as the foundation for multiple business functions, requiring careful coordination with inventory management, pricing systems, customer experience platforms, and business intelligence tools.
Hitting an ace
The retail landscape increasingly rewards organisations that can deliver superior customer experiences while maintaining operational efficiency. Product catalog excellence has become a prerequisite for success in this environment, as it enables the real-time, personalised, and consistent experiences that customers expect. It’s about mastering the fundamentals, whilst also embracing innovation just like we see in tennis.
Organisations that delay catalog modernisation face increasing competitive disadvantage as customers gravitate toward retailers that can deliver superior discovery, accurate information, and seamless experiences across all touchpoints. The window for transformation remains open, but the competitive gap widens each quarter as leading retailers extend their capabilities.
