Understanding the Role of AI in Ecommerce Analytics

AI is BS. Not the technology. The talk track. I attended the NRF Foundation Big Show this week, and everything was “AI-something.” AI for inventory. AI for pricing. AI for customer service. AI for world peace (okay, maybe not yet). With all that noise, it’s easy to feel overwhelmed—and a bit cynical. The possibilities are incredible, but slapping “AI” on everything doesn’t make it useful. Understanding how these tools work to solve actual business problems is critical. I’ve found it’s helpful to kind of simplify it into the two categories that really matter: ✍️ Generative AI is like an extremely knowledgeable friend who can produce new things—written content, images, & beyond—if asked in just the right way. A chatbot interface makes that generative AI friend more accessible: you give it a prompt (for example, “Write a short product description for a new running shoe”), and it instantly creates a response from all the information it has internalized. 🕵️♀️ Agentic AI goes further. It's more like a proactive personal assistant with the same deep knowledge. Instead of waiting on precise prompts, it can infer tasks and even carry them out automatically. For example, it can figure out when stock is running low & reorder items without being explicitly told every step to take. How retailers might use each: 1️⃣ Generative AI: Product Descriptions: Automatically create rich, engaging product descriptions for online catalogs that match the brand’s voice. Marketing Content: Draft email campaigns, social media copy, & blog posts. Store Layouts & Visuals: Suggest store display ideas or mockups, using AI-generated images to spark new merchandising concepts. 2️⃣ Agentic AI: Inventory Management: Monitor incoming sales data & reorder items proactively before inventory runs out. Customer Service Automation: Act on customer requests (like returns or shipping updates) without a staff member walking it through each step. Dynamic Pricing: Continuously check market trends, competitor prices, and demand patterns, then adjust product prices accordingly—without needing a person to oversee it all. I think Agentic AI will provide the biggest benefits and the biggest disruptions because consumers love convenience and businesses love efficiency – and it delivers both. AI is evolving faster than Moore’s Law—doubling every 3 months instead of 18. Do the math—it’s mind-blowing. Moore’s Law gets you 10X improvement in 5 years. At this pace, AI could be 1,000,000X in 5 years! (h/t Kasey Lobaugh) In just a few years, we could see retail transformed by super-powered sales associates, hyper-personalized shopping journeys, and supply chains optimized to unimaginable levels. But first we have to cut through the noise to make sure we’re making the right choices. Are you experimenting with any tools successfully—or are you overwhelmed by the hype (or both!)? #AI #agenticAI #agents #retail #NRF

AI in Ecom Marketing: What’s Actually Working (And What’s Overhyped) AI is everywhere right now, and brands are either doubling down on it or panicking that they’re falling behind. So let’s break it down—what’s actually driving revenue in eCommerce, and what’s just noise? AI That’s Actually Moving the Needle: ✅ Klaviyo’s AI-Powered Predictive Analytics Klaviyo uses AI to predict customer lifetime value (CLV), churn risk, and expected next order date. This helps brands segment customers more effectively, prioritize high-value buyers, and send targeted retention campaigns. Pros: Increases retention by focusing on customers most likely to repurchase. Cons: Can be inaccurate if your brand doesn’t have enough historical data. ✅ Meta Advantage+ & Google’s Performance Max AI-powered ad platforms like Meta’s Advantage+ and Google’s Performance Max automatically test creatives, placements, and audience segments. This shifts ad spend toward the highest-performing assets in real-time. Pros: Reduces manual campaign management and optimizes budget allocation. Cons: Lack of manual control—AI makes the decisions, which can sometimes favor short-term ROAS over long-term brand growth. ✅ AI-Driven Personalization Tools like Octane AI (Shopify quiz builder) and Klaviyo’s personalized recommendations use AI to create hyper-personalized shopping experiences. Pros: Helps brands guide customers to the right products, increasing AOV. Cons: Needs well-structured data to function properly—poor tagging or limited purchase history can reduce effectiveness. AI That Still Needs Work: ❌ Fully AI-Generated Copywriting (ChatGPT, Jasper, Copy.ai) AI-generated copy lacks emotional depth and creativity. It’s great for idea generation but still needs human oversight. ❌ AI Chatbots for Customer Support (Drift, Intercom, Zendesk AI) Most AI chatbots fail when it comes to handling complex customer issues. They work well for FAQs, but for high-ticket items, human support still wins. ❌ "Set It & Forget It" AI for Ad Management These don’t guarantee profitability. If the product page isn’t optimized or the offer isn’t compelling, no AI can fix it. The Bottom Line: AI Works Best When Paired With a Strong Strategy The brands scaling profitably in 2025 are using AI to optimize acquisition, CRO, and retention—not to replace them. Instead of chasing every AI tool, focus on where AI can improve efficiency in your existing marketing stack.

𝗧𝗟;𝗗𝗥: Amazon's multi agent design in 𝗜𝗻𝘀𝗶𝗴𝗵𝘁 𝗔𝗴𝗲𝗻𝘁𝘀 orchestrates specialized AI workers that transform how 1M+ sellers run their businesses leading to outsize outcomes. 𝗙𝗿𝗼𝗺 𝗗𝗮𝘁𝗮 𝗢𝘃𝗲𝗿𝗹𝗼𝗮𝗱 𝘁𝗼 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 E-commerce sellers face a paradox: rich tools everywhere, insights nowhere. Amazon's response? 𝗜𝗻𝘀𝗶𝗴𝗵𝘁 𝗔𝗴𝗲𝗻𝘁𝘀 (IA)—an LLM-based multi-agent system that lets sellers simply ask: "𝘞𝘩𝘢𝘵 𝘸𝘦𝘳𝘦 𝘮𝘺 𝘵𝘰𝘱 10 𝘱𝘳𝘰𝘥𝘶𝘤𝘵𝘴 𝘭𝘢𝘴𝘵 𝘮𝘰𝘯𝘵𝘩?" or "𝘏𝘰𝘸 𝘥𝘰𝘦𝘴 𝘮𝘺 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴 𝘤𝘰𝘮𝘱𝘢𝘳𝘦 𝘵𝘰 𝘣𝘦𝘯𝘤𝘩𝘮𝘢𝘳𝘬𝘴?" (Read more here: https://bit.ly/41cbt4R) No more hunting through dashboards. Just natural conversation yielding precise data insights. 𝗧𝗵𝗲 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 IA's hierarchical manager-worker structure optimizes for coverage, accuracy, and latency: 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 𝗔𝗴𝗲𝗻𝘁:  • Lightweight encoder-decoder for Out-of-Domain detection (96.9% precision)  • BERT-based classifier for agent routing (83% accuracy, 0.31s latency)  • Query augmentation for temporal disambiguation  • Parallel processing to minimize latency 𝗪𝗼𝗿𝗸𝗲𝗿 𝗔𝗴𝗲𝗻𝘁𝘀:  • Data Presenter: Handles descriptive analytics ("Show me sales trends")  • Insight Generator: Provides diagnostic analysis ("How is my business performing?") 𝗧𝗵𝗲 𝗦𝗲𝗰𝗿𝗲𝘁 𝗦𝗮𝘂𝗰𝗲: 𝗥𝗼𝗯𝘂𝘀𝘁 𝗗𝗮𝘁𝗮 𝗠𝗼𝗱𝗲𝗹 Unlike fragile text-to-SQL approaches, IA leverages:  • API-based data retrieval with built-in constraints  • Divide-and-conquer query decomposition  • Dynamic domain knowledge injection  • Strategic planning for granular data aggregation 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲  • 89.5% question-level accuracy  • <15s P90 latency  • 97.7% relevancy score  • 95.8% correctness score All of this is powered by of course Amazon Web Services (AWS) Bedrock and SageMaker. Currently live for Amazon US sellers, transforming how businesses interact with their data. Great work by Jincheng Bai and team! 𝗧𝗵𝗲 𝗔𝗺𝗮𝘇𝗼𝗻 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 Insight Agents isn't just another chatbot—it's a force multiplier for sellers. By combining lightweight specialized models with strategic LLM deployment, Amazon delivers enterprise-grade insights at conversational speed. The future of business intelligence isn't more dashboards. It's intelligent agents that understand your questions and deliver precise, actionable insights.