Enhancing Product Discovery in E-Commerce

Excited to share insights from Walmart 's groundbreaking semantic search system that revolutionizes e-commerce product discovery! The team at Walmart Global Technology(the team that I am a part of 😬) has developed a hybrid retrieval system that combines traditional inverted index search with neural embedding-based search to tackle the challenging problem of tail queries in e-commerce. Key Technical Highlights: • The system uses a two-tower BERT architecture where one tower processes queries and another processes product information, generating dense vector representations for semantic matching. • Product information is enriched by combining titles with key attributes like category, brand, color, and gender using special prefix tokens to help the model distinguish different attribute types. • The neural model leverages DistilBERT with 6 layers and projects the 768-dimensional embeddings down to 256 dimensions using a linear layer, achieving optimal performance while reducing storage and computation costs. • To improve model training, they implemented innovative negative sampling techniques combining product category matching and token overlap filtering to identify challenging negative examples. Production Implementation Details: • The system uses a managed ANN (Approximate Nearest Neighbor) service to enable fast retrieval, achieving 99% recall@20 with just 13ms latency. • Query embeddings are cached with preset TTL (Time-To-Live) to reduce latency and costs in production. • The model is exported to ONNX format and served in Java, with custom optimizations like fixed input shapes and GPU acceleration using NVIDIA T4 processors. Results: The system showed significant improvements in both offline metrics and live experiments, with: - +2.84% improvement in NDCG@10 for human evaluation - +0.54% lift in Add-to-Cart rates in live A/B testing This is a fantastic example of how modern NLP techniques can be successfully deployed at scale to solve real-world e-commerce challenges!

Rufus is an AI designed to revolutionize product discovery through natural language understanding, inference, and multimedia optimization. Here's how it works and how sellers can use it to boost their sales. Rufus changes the rules of product discovery by focusing on context, not just keywords. Instead of matching queries like "desk lamp" to products with the same exact words, Rufus identifies noun phrases and their relationships. For example: 1. A shopper asks: "What lamp is best for reading in bed?" 2. Rufus identifies key phrases like “reading lamp” and “bedside.” 3. It ranks products semantically, recommending items with phrases like “adjustable bedside reading lamp with eye-friendly light.” This ensures shoppers see relevant, high-quality products tailored to their needs. Key Features  1. Noun Phrase Optimization (NPO): Rufus focuses on detailed, descriptive phrases. Sellers should build product titles and descriptions differently: ▪️ Instead of: "Table Lamp" ▪️ Use: "Vintage Brass Table Lamp with Adjustable Arm for Home Office." 2. Visual Label Tagging (VLT): Rufus reads images as well as text. Adding overlays like “Energy Efficient | 6 Brightness Levels” directly on product images can increase discoverability. 3. Semantic Understanding: Rufus connects implied customer needs to product benefits. For example, it knows “easy-to-clean” is relevant for a query like “pet-friendly couch.” 4. Q&A Enhancement: Rufus thrives on clear answers to common customer questions. Example: Q: “Does it fit a queen-size mattress?” A: “Yes, our bed frame is designed for all queen-size mattresses up to 12 inches thick.” 5. Inference Optimization: Rufus maps product features to inferred benefits. A product labeled “durable non-stick pan” might also be shown for “easy-to-clean cookware.” Steps Sellers Need to Take 1. Optimize Product Titles with Rich Noun Phrases ▪️ Use descriptors like material, design, and purpose. Example: “Professional Chef Knife Set with German Steel Blades”. 2. Enhance Images with Text ▪️ Include labels like “Anti-Fog Coating | Shatterproof Design” directly on images. ▪️ Ensure images demonstrate key features clearly 3. Leverage FAQs ▪️ Anticipate shopper questions and weave them into your listings. Example: Q: “How do I clean this air fryer?” A: “Wipe with a damp cloth or place removable parts in the dishwasher.” 4. Use Semantic Context in Descriptions ▪️ Avoid keyword stuffing; write naturally. Example: “This ergonomic office chair supports your back during long hours at your desk, making it perfect for work-from-home setups.” 5. Update Content Regularly ▪️ Monitor trends in customer queries and adapt your listings accordingly. If shoppers search for “eco-friendly packaging,” ensure your products highlight those features. 6. Incorporate Click Training Data Insights ▪️ Analyze which features customers click on most and highlight them in your product content. Amazon’s Rufus thrives on detailed, customer-centric content.

AI search has changed how ecommerce visibility works. Rankings and keywords still matter, but they no longer determine whether you appear in AI answers. LLMs pull from many sources and validate information across channels, so brands now need a practical strategy to influence what these systems surface. Here is the actionable version of the playbook: 1. Strengthen the brand mention layer These mentions usually come from Reddit, Inc., media coverage, YouTube reviews, and user sentiment. To increase volume and quality: • Seed conversations on Reddit via customer outreach and product education. • Pitch journalists with data, not product claims. • Send reviewers standardized product kits with accurate specs. • Increase review velocity on Amazon, Walmart, and your own site. Goal: create widespread awareness that models can pick up reliably. 2. Win citations by becoming a source of truth Citations influence how the model describes your brand. To increase them: • Publish structured, factual resources. Examples include comparison charts, ingredient or materials breakdowns, and step by step usage guides. • Make your product pages machine readable with flawless schema markup, identical naming, and consistent specs. • Update all your public product data quarterly. Goal: give LLMs clean, verifiable information they can quote confidently. 3. Influence product recommendations This is the highest value layer. To increase recommendation frequency: • Get included in publisher listicles and buying guides through affiliate programs or product samples. • Make sure your product appears in retailer categories with high review counts and strong Q and A sections. • Encourage customers to mention specific attributes in their reviews so AI models learn your positioning. • Publish expert testing results wherever possible. Goal: fit the queries that drive buying decisions, not just general awareness. 4. Build consensus across independent sources Models reward brands whose reputation looks the same everywhere. To create this: • Audit every major source in your category twice a year. Look at Amazon sentiment, YouTube reviews, TikTok results, Reddit threads, niche forums, and publisher guides. • Identify mismatched attributes, outdated specs, or conflicting feature claims. Fix them one by one. • Monitor competitor positioning across these same channels to understand why they appear more often in AI answers. Goal: eliminate contradictions so LLMs treat your brand as the consistent default. 5. Fix consistency across every product feed LLMs cross check your data across Amazon, Shopify, Walmart, Google Merchant, and any structured source. To avoid exclusion: • Standardize SKU names and attribute formats. • Keep pricing aligned within a narrow range. • Use the same dimensions, specs, and materials everywhere. • Remove outdated product copies from old marketplaces. Goal: reduce confusion so AI systems trust your data. (Find Step 6 and 7 in the comment section)

Google is no longer the first stop for product discovery. New data from PartnerCentric confirms what many of us in e-commerce already feel happening: TikTok, Pinterest, Reddit, Inc., and Discord are reshaping how people discover, evaluate, and buy products—especially among Gen Z and millennials. Here are the shifts worth paying attention to: 1. 1 in 10 Gen Z shoppers prefer TikTok over Google for finding products 2. 50% of Gen Z use Discord for shopping—often in private, loyalty-driven communities 3. 2/3 of U.S. consumers use Reddit in some form, with younger shoppers turning to it for trusted reviews 4. TikTok Shop users now spend ~$40/month—$50 for millennials And while Google still plays a role, it’s being edged out during the most influential parts of the funnel: discovery and validation. This is the rise of social-first commerce. For brands and marketers, it’s not just about ads—it’s about being part of the conversation where it starts. Your future customers aren’t searching. They’re scrolling, watching, and asking strangers on Reddit.

ChatGPT eCommerce drop: Part 3 (foundational Q&A) Q: Why should eCommerce leaders pay attention to ChatGPT’s shopping assistant? The way consumers discover and decide what to buy is fundamentally shifting, from keyword search to conversation. If your product content isn’t optimized for AI discovery, you're lagging. Q: How is this different from Google search or traditional marketplace discovery? Old-school search engines return a list of links or paid ads. ChatGPT returns curated, context-rich product suggestions with images, pricing, reviews, and direct buy links. Difference is that AI models understand intent, not just keywords. Instead of “best sneakers,” a user may ask, “What’s a comfortable walking shoe for traveling through Europe in the summer?” ChatGPT understands that nuance and recommends accordingly. Q: What powers ChatGPT’s product recommendations? It’s a mix of structured product data and contextual intent signals. Product metadata (titles, descriptions, tags, inventory) Real-world reviews with specific use cases or outcomes Signals of trust (brand credibility, availability, content quality) Integrations with platforms like Shopify and product feed partners The AI model then uses this data to recommend products that match the why, not just the what. Q: So what changes for brands now that AI is in the shopping flow? Discovery is an earned visibility game. You can’t just outbid, you have to out-relevance. Generic content doesn’t work; rich context wins. Volume of reviews matters less; specificity and clarity matter more. The brands showing up in ChatGPT’s results are the ones with deep, well-structured content and high-context product storytelling. Q: What are the key elements brands should focus on to stay visible in AI-driven shopping? Priorities: 1. Structured Data Implement schema markup across product pages. Use tools like Shopify’s native integrations to feed product info cleanly. 2. Contextual Product Descriptions Who is this for? What does it solve? What makes it different? 3. High-Context Reviews Prompt users to share how and why they used a product. 4. Review Accessibility Make reviews public, crawlable, and visible next to your products. 5. Feed Accuracy Keep product data synced: availability, pricing, variants, and descriptions. Outdated info will kill your ranking in AI. AI models favor reviews that mention specific use cases, emotions, and product outcomes. A single thoughtful review like “Perfect for marathon runners with flat feet” now outranks 50 vague 5-star ratings. I’m excited for this AI eCommerce era. More to come from The Other Group #ai #ecommerce #commerce

🚀 Big move in commerce: OpenAI just rolled out a Shopping Research tool inside ChatGPT — here’s what that means for brands, retailers and shoppers. 🔍 What the tool offers: • Lets users ask ChatGPT to describe what kind of product they want and get a personalized buyer’s guide in minutes. • Compares across products, features, trade-offs and delivers deep research rather than a quick answer. • Initially rolling out to Free, Plus, Pro and logged-in users — broad access. 💡 Why it matters for e-commerce and retail tech: • Brands and retailers gain a new channel of discovery — AI becomes a first interface, not a just a tool for search. • With generative AI, the discovery→purchase path gets shorter and more conversational, raising stakes for merchandising, personalization and visual tech. • For you working in fashion, luxury & multi-brand retail tech: this underscores the need to own the “AI-first” workflow (visuals, metadata, cross-channel signals) — the tools around you will be consumed through GPT-style experiences. • Even mid-market & enterprise stacks (think your GTM for orchestration layers) need to factor in how brands will integrate with AI-driven shopping agents — not just web search and ads. 🧭 Actionable next steps: • Audit your product metadata, visual assets and brand/style narrative — does it hold up when summarized by an agent versus traditional search? • For your GTM with orchestration layer clients: highlight how orchestration + visual/AI tech can plug into this agent-ecosystem (not just Google/Meta). • For retail brands: begin experimenting with conversational shopping flows (via ChatGPT-like agents), making sure you’re “found” when the AI asks for options. • For your generative AI consulting work: position this as a shift in “discoverability” (to borrow your framing around AEO/GEO) — agents will now ask products about your brand; make sure the brand answers.

Product discovery isn’t just an event—it’s an ongoing journey where the customer is always at the center. After diving into “Continuous Discovery Habits” by Teresa Torres, it’s clear that successful product teams make continuous learning a habit, not a phase. This book is a must-read for product owners and their teams to foster a truly collaborative process where decisions aren’t made by one person but through shared understanding and teamwork. One key insight? From the initial stages to later iterations, customer understanding should never stop. 💡 Customer interviews and feedback loops are critical throughout the lifecycle—not just at the start. 🌳 Solution tree mapping helps teams visualize possibilities, keep track of what’s being tested, and align on what truly solves the customer’s problem. 🤝 Most importantly, involving cross-functional teams in these practices ensures everyone—from design to development—has a shared understanding of the problem space and can contribute meaningfully to the solution. In my work, both for my own products and client projects, I’ve adopted: 💬 Frequent customer interviews to deepen our insights 🌿 Collaborative solution mapping to explore ideas and align the team 📊 Regular sharing of findings to keep everyone in sync The result? We don’t just build—we learn, adapt, and improve continuously with the customer in focus. If you’re a product owner or part of a product team, this book is a game changer. It helps you foster a habit of customer-centric discovery while ensuring alignment across all roles. Are you already practicing continuous discovery in your teams? How do you ensure customer understanding remains at the heart of your product journey? Let’s exchange ideas! 🔍💬 #CustomerFirst #ProductDiscovery #Collaboration #SolutionMapping #ContinuousLearning #ProductManagement #TeamAlignment #MustRead

🌌 Amazon’s COSMO: Next-Gen Search! 🛒🚀 Amazon just launched COSMO, a game-changing AI-powered search system designed to revolutionize how we discover products. Unlike traditional algorithms that rely on keywords, COSMO focuses on customer intent and context, creating a shopping experience tailored to user needs. Here's what I takeway: 🎯 What is COSMO? COSMO uses commonsense knowledge graphs to understand why, how, and for whom a product is used. Think smarter searches—when someone looks for “shoes for pregnant women,” COSMO suggests slip-resistant options instead of generic results. Another example? A search for “gear for winter hiking” might prioritize insulated boots, thermal jackets, and crampons, understanding the user’s need for products suited to cold and icy conditions instead of generic outdoor gear. 🥾❄️ 🤖 How it Works: COSMO leverages large language models to process search intent and behavior. Integrates visual and textual data for a comprehensive product understanding. Already impacting 10% of Amazon searches with reported 8% engagement rate increases. 💡 What This Means for Brands: 1️⃣ Content Overhaul: Move beyond keywords. Focus on how your product is used, who uses it, and why it matters. Context is the new king! 👑 2️⃣ Visuals Matter: COSMO reads images! Optimize photos to show products in action and highlight benefits. ���� 3️⃣ Reviews are Gold: Reviews help train COSMO. Encourage customer feedback to boost your listing's relevance. ⭐ 4️⃣ No More Shortcuts: Keyword stuffing won’t cut it. Brands that embrace context-driven optimization will thrive. 📈 Why This Matters Now: The days of static product searches are ending. COSMO represents a shift towards personalized, AI-driven shopping experiences. Brands that prepare today will dominate tomorrow. 💬 Your Take: How do you see COSMO changing the e-commerce landscape? Are your listings ready for this transformation? Let’s discuss! 👇

Niche products will win in AI Search In LLM-driven product discovery intent matters most. When a user asks: “What’s the best backpack for women under 5'3” who bike to work?” ChatGPT doesn’t want 50 options. It wants 1–3 great matches. ✅ So yes, the more niche your product is, the more likely it is to be recommended. But Being Niche = High Intent Match But also = High Risk of Misunderstanding Your product will only be surfaced if: 1. The LLM knows your product exists 2. It understands who it’s for and when it’s best 3. It can find supporting signals (structured data, reviews, content) to back up the recommendation And that’s where many brands fail. So what to do? ✅ Use product descriptions that clearly explain: 1. Who it’s for 2. When it’s most useful 3. What problem it solves better than anything else ✅ Reinforce that in: 1. Structured schema (e.g. Product + Review + FAQ) 2. Product feeds (with GTINs, materials, use cases, etc.) 3. Third-party content (influencers, reviews, Reddit, etc.) ✅ And if you don’t have a ton of UGC yet? Use paid affiliate and influencer content to teach the AI what your product is good for. 💬How are you closing the gap between your product’s purpose and the AI’s understanding?

Worthy Read: How We Introduced Product Discovery Methods at Overleaf in 3 Steps by Roberta Cucuzza "We didn't write any code and yet we were able to see 2 ideas fail and 1 succeed!" 💡 Learn how Overleaf integrated continuous product discovery into their development process in three key steps: 🔍 Step 1: Getting started with user interviews and forming product trios (PM + UX + Developer) 🏃♀️ Step 2: Testing the approach with a 5-day discovery sprint 🤝 Built opportunity maps 🧠 Ran ideation sessions 📊 Tested assumptions 📈 Analyzed results 🔄 Step 3: Making it sustainable ✨ Created flexible discovery processes 🗺️ Maintain three opportunity maps 👥 Expanded to product quartets with Support team 💪 Embedded discovery naturally into daily work The best part? You don't need extra resources or a dedicated team to make product discovery work. Check out the comments for a link to the article. ❓ Have you ever participated in a product discovery sprint? Share your thoughts in the comments below.