E-Commerce Technology Platforms

TIPS FROM THE AGENCY (https://lnkd.in/gsyMAU5u) While everyone's hyping up fancy link-building tactics… We grew a client's traffic by 437% in just 6 months with some simple structural fixes. Yup, good old onsite SEO took our e-commerce client from 758k to 4.08M monthly sessions. Here are some of the fixes that you can copy and paste for your own website: ▶️ Website Structure Optimization ✅ Created a New Category System: We analyzed the 1.5 million products that the client sold and restructured categories based on competitor research and product tags, making navigation smoother and improving search visibility. We specifically looked at how competitors structured their categories and tagged their products to align with how users were searching. ✅ Automated Product Assignments: We built an automation system to auto-assign products to categories using predefined rules and AI capabilities, reducing manual effort and orphan pages. ▶️ Product Description Enhancements ✅ Expanded Product Descriptions: We added key product details (dimensions, colors, materials, assembly steps) to enhance user experience and boost conversions. The key is to provide users with as much information about your products as possible, in a way that is easy to digest and understand. ✅ Enabled Advanced Product Filtering: New data fields allowed users to filter products by specific attributes (like materials, color etc), making product discovery faster and more intuitive. ▶️ Image Optimization ✅ Implemented a Systematic Alt Text Pattern: We created a structured approach to alt text by incorporating the image title and a specific phrase related to the client’s products. This improved image rankings in Google Image Search and made the website more accessible. ✅ Applied Alt Text Across All Product Images: Using automation, we ensured that every product image received relevant and optimized alt text. This prevented content gaps and provided consistent SEO improvements across the entire catalog. ▶️ Technical SEO Improvements ✅ Implemented a Dynamic Sitemap: We replaced the static sitemap with an automated version that updates instantly when new URLs are added, keeping search engines informed in real time. ▶️ Open Graph Implementation ✅ Optimized Metadata for Social Sharing: To improve content visibility and engagement, we implemented Open Graph (OG) tags for title, description, image, and URL. This ensured structured and compelling previews when shared on social media. ✅ Boosted Rankings for Recognized Authors: Since the authors were well-known, better metadata instantly improved their rankings, reinforcing their authority in search results. The result of these changes? Sessions: 758k → 4.08M (437% growth) Engaged users: 559k → 3.02M (440% growth) Want results like this? Head over to: https://lnkd.in/gsyMAU5u We’ll provide a free audit of your website and actionable strategies to scale your traffic right away.

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

How do you really know which platform is best for #b2becommerce? Even if someone hands you a shortlist of the “top” platforms, how do you actually evaluate them and determine which one fits your needs? The truth? You need an objective approach. The goal isn’t just picking a platform, it’s finding the one that: ✅ Delivers a great customer experience ✅ Plays best with business operations ✅ Minimizes long-term implementation and maintenance costs Here’s how to get there: 1️⃣ Discovery Strategic and tactical input from every key internal and external stakeholder- including customers. 2️⃣ Create user stories and requirements Who’s using the platform, and what do they need it to do (in granular detail)? 3️⃣ Score the platforms Prioritize based on documented requirements - not a vendor’s checklist. 4️⃣ Roll everything into a simple scoring structure. The process should be transparent, shareable, and decision-ready for leadership. B2B e-commerce isn’t just a tech decision, it’s an organizational commitment. Your scoring methodology needs to be clear, data-driven, and free from guesswork. We’ve been working on something that does just that. It removes the subjectivity from platform selection, helps identify the right platform based on your priorities, backed by objective data. No bias. No uncertainty. Just the right choice for your distribution or manufacturing business. What’s your company’s process for evaluating technology? #PlatformSelection #B2B #Ecommerce #DigitalTransformation #B2BPlatforms

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 put together a step-by-step guide to help you implement LLM Optimisation the right way for E-commerce. In this carousel, you’ll find the high-level overview. The full version includes: → How to generate a structured product feed (CSV, JSON, XML) with the right attributes: title, GTIN, MPN, price, availability, and product URLs → How to apply Schema . org Product markup using JSON-LD, with full code examples → How to configure your robots.txt to allow GPTBot, OAI-SearchBot and PerplexityBot access to your PDPs and feeds → How to prerender JS-heavy product pages using static snapshots or SSR → Where and how to submit your feed to Perplexity’s Merchant Program and OpenAI’s early access initiative → How to debug AI visibility using server logs, schema validators, and live prompt testing inside LLM tools If you’re working on e-commerce, SEO, or growth, this is where things are moving. 💬 Comment or DM me, and I’ll send you the full guide. 📌 Save this post if you're not ready yet but know you'll need it.

Google quietly added a new set of Merchant Center attributes They're called conversational attributes, and they exist for one reason: to feed your products into AI-driven surfaces like AI Mode in Search. Here's what you can now submit: → Question and answer - FAQs straight in your feed ("Does it support Bluetooth?") → Document link - manuals, assembly guides, spec sheets (PDF) → Related product - accessories, required parts, "often bought with" → Item group title + Variant option - clean variant structure AI can actually parse → Popularity rank - how a product performs vs the rest of your catalog A few things that make this a low-risk, high-upside move: 1. They're optional and won't affect the approval status of your existing products. 2. You can push them through a supplemental feed; no need to touch your primary data source. 3. If the info already lives in description, product_highlight, or product_detail, you don't duplicate it. Feed optimization used to be about getting approved and ranking in Shopping. Now it's also about being machine-readable for AI answers. The brands that structure their product data for conversational retrieval will be the ones AI surfaces actually cite and recommend. This is "GEO" at the feed level. And the spec is already live. Not sure if your product feed is ready for AI search? DM me FEED and your domain. I'll check whether your data is structured to get picked up by AI surfaces and what's missing

Most people think Shopify can’t handle complex B2B. False 🔴 Here’s what we built for Lupine lighting systems GmbH → 15+ customer groups — resellers, professional organizations, distributors - all managed from a single storefront. →Automated onboarding: B2B customers apply on-site, upload documentation, and get verified. One approval click assigns the right customer group, pricing, and access. Rejections send standardized communication. → Account lifecycle management: Every B2B account has an expiration date. The system auto-notifies when renewal is due. No action? Access revoked. No manual work. Dynamic pricing: Some groups get percentage discounts. Others get fixed wholesale pricing. Resellers see their price + suggested retail for margin calculations. Org buyers see only their price. →Regulatory logic: StVZO checks at checkout block non-compliant products for German addresses. B2B resellers are exempt. All of this runs on Shopify Flow + native capabilities. No custom middleware. No external B2B platform. The era of “Shopify can’t do B2B” is dead.

Ultraceuticals, Australia's leading cosmeceutical-grade skincare brand, supercharged their B2B digital transformation with BigCommerce. When Ultraceuticals set out to bring their offline B2B business online, they initially considered using their DTC platform Shopify: “Why wouldn’t we consolidate?” Jash Naidoo, IT Manager at Ultraceuticals, asked himself. “But clearly I was wrong. When we took it to market, we had some very strict goals and needs that just weren’t getting met with Shopify" Complex regional needs, buyer roles and permissioning , custom catalogs and pricing, and the desire to offer a high-quality, frictionless customer experience meant they needed a platform purpose-built for B2B ecommerce. That’s when they made the switch to BigCommerce’s #B2BEdition, and the transformation began: 🌎 Multi-storefront flexibility: Seamlessly tailored experiences for customers in Australia, New Zealand, and the U.S., with ERP integrations for each region. 🛒 Self-service empowerment: Customers can now place bulk orders, recheck order statuses, and manage invoices on their terms, anytime, anywhere. 👥 Customer-first features: By mimicking their old offline order forms, Ultraceuticals removed friction, making the shift to ecommerce effortless for long-time customers. As Jash Naidoo, IT Manager at Ultraceuticals, explained: "Instead of trying to squeeze everything into one storefront like we would have on Shopify, we’re able to do so much more with BigCommerce’s multi-storefront capabilities." 💡 The takeaway for B2B businesses: If your B2C platform is holding back your B2B operations, it’s time to rethink your tech stack. B2B ecommerce requires tools built to handle: 🛑 Role-based permissions and custom workflows 📦 Bulk orders and customer-specific pricing ⚙️ Seamless ERP and CRM integrations At Ultraceuticals, the result wasn’t just a digital transformation—it was a customer-first transformation. And that’s a win for everyone. A big shout-out to Scott Lovett and Jarrad Wild at Kobe Creations for their excellent delivery. Read the full case study here: https://lnkd.in/gxVCshuv #B2BEcommerce #DigitalTransformation #BigCommerce #CustomerExperience #Innovation Isabella Marco Shannon Ingrey Lauren Clark Ben Chambers Paul Dabrowski Daniel Fertig Lauryn Spence

Znode: The B2B Platform You Probably Haven’t Looked At (But Should) Over the last few weeks I’ve had multiple conversations with manufacturers and distributors who are frustrated with their commerce stack. Not because traffic is down. Not because marketing is failing. Because their platform fights their business model. Customer-specific catalogs feel bolted on. Pricing agreements live in spreadsheets. Approvals are handled by email. Sales reps still place orders manually because “it’s easier.” In those conversations, the usual names come up first — Shopify, BigCommerce, Adobe, etc. But there’s another platform that keeps surfacing in more complex B2B discussions: Znode. It’s not on Magic Quadrants. It’s not everywhere on LinkedIn. It’s not trying to win the SMB race. But when you start digging into: Multi-tier account hierarchies Contract pricing by customer + catalog Dealer / distributor portals Approval workflows that actually mirror procurement Quote-driven ordering …it starts to make sense why certain manufacturers swear by it. I’m not saying it’s the answer for everyone. If you’re a blended DTC/B2B brand optimizing CAC and creative velocity, there are probably better fits. But if you’re a manufacturer with complex pricing logic, reps in the field, and 20 years of ERP-driven agreements… it deserves a look. Curious how others are evaluating B2B platforms right now. Are you prioritizing ecosystem and speed? Or workflow depth and contract logic? Let’s compare notes.