E-Commerce Product Information Management

4,700%. That's how much AI agent traffic to retail sites grew year-over-year. Google Cloud just published something every CPG leader should read. Their thesis: product data is the new packaging. Not a metaphor. A literal business requirement. Here's what they mean: when a consumer's AI agent searches for "verified sustainable packaging" or "gluten-free snack under $5," it's not reading your brand story. It's not admiring your shelf design. It's parsing structured attributes, metadata, and tagged product facts. If your data doesn't contain it, your product doesn't exist. This matters more right now than at any point in ecommerce history. Here's why: 75% of consumers expect tariffs to push grocery prices higher. 38% tried a new brand in the last 3 months alone. Price sensitivity is at an all-time high. At the exact moment your brand needs to justify its premium, the discovery mechanism is shifting from human browsing to machine parsing. Morgan Stanley projects nearly half of online shoppers will use AI shopping agents by 2030 — accounting for 25% of their spending. McKinsey estimates $1 trillion in U.S. agentic commerce transactions by 2030. And yet — most CPG product data was built for human merchandisers, not AI agents. The typical PIM system stores descriptions optimized for keyword search. Not structured attributes for agent queries. Not machine-readable claims for recommendation engines. Not real-time availability signals for purchase agents. Google's guidance is specific: standardize product facts, enrich attributes, structure pricing and availability data, implement schema markup that machines can parse. This isn't a future problem. Amazon Rufus is handling 274M queries daily — right now — pulling 78% of its recommendations from products NOT in traditional search results. The invisible shelf isn't theoretical. It's operational. And here's the uncomfortable truth for most brands: the gap between "we have a PIM" and "our data is agent-ready" is massive. Most brands have the first. Almost none have the second. The brands closing that gap fastest are treating product data infrastructure the same way they treated digital media infrastructure five years ago — as a must-fund capability, not a back-office IT project. When Google tells the market that product data is the new packaging, the smart move isn't to debate it. It's to ask: is our packaging ready? #DigitalShelf #AgenticCommerce #CPG #ContentAI #ProductData #Ecommerce

CatalogRAG: A Game-Changer for E-commerce Product Attribute Prediction Researchers from Amazon have developed CatalogRAG, a novel retrieval-augmented generation system that's transforming how we handle missing product attributes in multilingual e-commerce catalogs. With nearly half of relevant structured attributes missing across product types, this is a critical challenge for global retailers. The Technical Innovation: Instead of relying on external knowledge sources, CatalogRAG leverages the catalog ecosystem itself. The system employs a multi-stage retrieval framework that combines term-based filtering with BM25 text-based search to identify similar products within the same product type and language store. Under the Hood: - Strategic Filtering: Uses product type constraints to narrow search space, then applies BM25 on product titles for semantic similarity - Heuristic Re-ranking: Prioritizes entries with higher glance views (customer engagement metrics) and same-brand products to maintain consistency - Few-shot Example Construction: Selects up to 3 highly relevant examples per missing attribute, incorporating them into attribute-specific prompts Impressive Results: Testing across US, German, and French stores showed remarkable improvements: - Up to 34% increase in recall for attribute prediction - Up to 43% improvement in catalog completeness - Particularly strong performance in non-English markets (French store showed highest gains) Why This Matters: The system captures store-specific conventions, brand patterns, and category relationships that external knowledge bases simply can't provide. By using similar products as contextual examples, it maintains consistency with existing catalog patterns while adapting to language-specific nuances. This approach demonstrates that sometimes the best knowledge source is right within your own ecosystem. The implications for global e-commerce platforms managing multilingual catalogs are significant.

I grew up in a family that ran a mid-sized fashion retail business, stores, loyal customers, seasonal rushes, and the occasional stockroom chaos. While I took the corporate route, I stayed close to the backend of it all, especially when conversations shifted to e-commerce. The move online wasn’t smooth. We listed on marketplaces, ran ads on Google and Facebook but quickly realised how broken digital selling can be if your product catalog isn’t in shape. We faced it all: Google rejecting listings for bad titles, shopping ads running on sold-out products, campaigns showing variants that didn’t exist and hours lost figuring out why products weren’t live. Turns out, it’s not always the ads that fail. It’s the data behind them. These are the kind of headaches most e-commerce brands silently suffer through until something like Strique steps in. Strique is an AI-powered marketing platform built for e-commerce brands and their Catalog module Strique Feed Engine makes product listings cleaner, sharper, and campaign-ready across Google and Meta. Here are a few key features that stood out to me: ✅️ Real-time inventory sync with Shopify, so sold-out products stop showing up in ads ✅️ Smart product sets you can build using filters like price, tags, or stock levels, ideal for promos or retargeting ✅️ Bulk editing of product titles, descriptions, and prices, with a rollback option if needed ✅️ Listing optimization that rewrites product titles and descriptions for better visibility and clicks ✅️ Automated rules that keep your catalog clean like removing low-stock items without manual checks It solves a lot of the behind-the-scenes problems that quietly drain ad budgets and impact performance. It’s no surprise brands like Inc.5, Chemistry, and Crimzon have seen double-digit lifts in ROAS, CTR, and AOV. If you manage growth, catalogs, or have ever helped someone sell online, Strique is worth a look.

How AI Agents, Machine-Readable Product Data, and New Business Models Are Reshaping Retail AI AI does not see websites. Customers do not see machine data. Customers see AI-selected products. This shift can be understood through "AI Aperture," my framework for bringing legal and commercial issues in AI into focus. This article applies AI Aperture to AI shopping. Retail AI is shifting purchases from human customers to AI Agents acting on their behalf. Traditional e-commerce assumes a human actor: a customer visits a website, searches for products, selects an item, and completes a transaction. In Retail AI, product visibility depends on what is "visible" to AI Agents. To create visibility, retailers must translate product information into Decision Data in a form that can be read and processed by AI Agents. This data includes product features, pricing, fulfillment, and delivery terms. When AI Agents "see" a product, they see only what is represented in the Decision Data. Definitions "Decision Data" is machine-readable data used by AI Agents to select products. "AI Agents" are AI shopping agents and other AI systems that process Decision Data using customer-defined criteria. The "Commerce System" is the combination of software and logic that connects customer-defined criteria with retailer-provided data and executes the transaction. AI Agents evaluate structured, machine-readable product data to determine which products are considered for purchase. Decision Data must be accurate, complete, and continuously updated. Retailers must align internal systems to produce data at this level of quality. If a product is not represented in a way an AI Agent can process, it may never be considered. It is not ranked lower. It is excluded. Competition shifts accordingly. Retailers are no longer competing on presentation. They are competing on the quality of Decision Data. Where IT and AI functions are provided by third-party vendors, retailers rely on those vendors for data quality that directly drives sales. Agreements should measure performance based on delivery of data of the required quality and timeliness. Incomplete Decision Data renders products invisible. Poor data leads to lost sales. Inconsistent data produces unpredictable outcomes. The legal framework reflects both traditional contract principles and rules embedded in system design. Rules, constraints, and execution logic in the Commerce System operate as functional equivalents of contractual terms. Lawyers must align contractual terms and governance structures with how AI Agents operate on Decision Data within the Commerce System. Takeaways Product visibility depends on whether products are visible to AI Agents. This requires continuously updated data feeds. Each model presents distinct legal risks. The legal framework combines encoded system rules and traditional contracts. When vendors provide AI services, operational control shifts. Liability does not.

Are you trying to fix every piece of your product data at once? That’s your WORST mistake: I see it all the time. Companies come to us drowning in messy data. Spreadsheets everywhere. Disconnected systems. Inconsistent taxonomies. They think they need to perfect everything immediately. But Derek Sewell at dataX.ai said it best: You don’t have to boil the ocean. DATA MANAGEMENT IS A JOURNEY, NOT A ONE-AND-DONE PROJECT When your company is in motion, starting from scratch, it’s overwhelming. You have to sort out who owns what. You have to decide what data matters most. Even after investing in a top-tier DAM, PIM, or CMS, the work never ends. Maintenance is continuous. Derek shows clients that you should focus on your top performing SKUs first. Get their images, descriptions, dimensions—everything—just right. This isn’t about fixing every product immediately. It’s about prioritizing what drives revenue. It's not easy, but it's wholly worth it. CREATE A PRIORITIZATION PLAN This is what to do: 1. Identity critical data –Find where your product information lives –Pull out the SKUs that really move your business forward 2. Develop a roadmap: –Prioritize improvements for those top performing SKUs –Set up processes for continuous validation and updates You'll save valuable time, boost sales, and free your team from endless, inefficient maintenance. Too many companies get overwhelmed, thinking they'll never get their data in order. You just need a plan that prioritizes what will impact your business the most, then apply that plan to everything else. Don't think of your data as a burden. Remember it’s your most valuable asset when  managed correctly. ____________________________ I'm on a mission to help e-commerce leaders sell more. Follow along as I share what I'm hearing from around our industry.

Your Product Data Strategy Will Make or Break Your AI Future Here's what most e-commerce leaders miss: AI doesn't just need your product data—it needs it structured, standardized, and conversation-ready. The gap we're seeing: ✗ 73% of retailers have fragmented product information ✗ Most product catalogs aren't AI-accessible ✗ Brand values and stories remain machine-unreadable Leading retailers are investing in: ✓ Structured data markup for AI discovery ✓ API-first product information architecture ✓ Intent-driven content optimization Real impact: Early adopters report significant improvements in AI-mediated product discovery compared to competitors still using traditional approaches. The window to build AI-ready infrastructure is narrowing. Organizations that wait risk being left behind in the next wave of commerce evolution. What's your data readiness strategy? https://lnkd.in/eqCk85AN #ProductData #AIReadiness #RetailTech #DigitalCommerce Frederic Levy Radu Buta Tim Sutton Sylvere Azoulai Flavio Quaranta Diane Bekhor Æthos Pegasus Angel Accelerator Akeneo: The Product Experience Company Joanna Lambadjieva