Understanding Ecommerce Analytics Tools

One of the most practical AI use cases in eCommerce right now isn’t a chatbot or a fancy personalization layer. It’s predicting a shopper’s future LTV before you spend the budget, and routing spend toward the people most likely to buy again. This is what I learned recently from Pecan AI which is quite interesting to me. And because most teams can’t do that today, they keep allocating budget evenly and running broad promos, hoping it works. 𝐏𝐞𝐜𝐚𝐧 𝐂𝐨-𝐏𝐢𝐥𝐨𝐭 changes the workflow: • You define the goal (e.g. “Predict 90-day LTV by channel and creative”) • It builds the predictive model for you • Then outputs ranked audiences and campaigns to scale, cap, or test, pushed directly into the tools you already use (ad platforms, CRM, email) No dashboards. Just actionable predictions. 📚 𝐄𝐱𝐚𝐦𝐩𝐥𝐞 𝐭𝐡𝐞𝐲 𝐬𝐡𝐚𝐫𝐞𝐝: A DTC apparel brand had strong AOV but low repeats from a few ad sets. Pecan flagged those cohorts as low predicted LTV, capped spend, and shifted budget to a lookalike built from high-LTV buyers → ROAS went up and discount costs dropped. This is the kind of AI that actually drives growth, not just adds another layer of complexity. Demo link → https://hubs.la/Q03BJHTF0 #AI #ecommerce #predictiveanalytics #martech

We have AI agents shop for us on ecommerce websites - we write a prompt, AI adds it to cart, we check out. But how does AI make decisions about which product to buy or not buy? And how similar is it to how humans make decisions? Are AI agents "rational" while shopping on ecom websites? Or are they susceptible to the same biases as humans? I'm glad I get to work on this as a part of Ethan Mollick's lab at Wharton AI & Analytics Initiative! Here's what we know until now about "agentic ecommerce" from Columbia Business School and My Custom AI researchers: Omar Besbes, Yash Kanoria, Akshit Kumar, Amine Allouah , Josué Figueroa created an environment (a "sandbox") to understand how AI agents choose products on ecom websites. The results? - AI agents favor top row - Column choice depends on model  - AI agents penalize sponsored tags, reward endorsements - Sensitivities to price, ratings, and reviews are directionally, but vary sharply across models. - Seller-side agent does conversion rate optimization (CRO) well with product descriptions that led to substantial market share gain - Biases resist simple prompting: explicitly prompting an agent to “ignore position” does not make it ignore position significantly. You've optimized your website for human users/buyers, now do it for agentic AI. Agentic CRO (conversion rate optimization for AI agents) could become a whole new offer by itself.

💸 3 shifts AI search shifts for ecommerce, and 3 actions to take: 📈 3 Shifts: 1. Search systems increasingly act as decision engines, not just retrieval engines: AI search platforms synthesize, compare, filter, and recommend products instead of returning ranked lists of URLs. 2. Brand trust functions as a system-level filter AI systems use brand signals as a risk-reduction mechanism, before recommendation logic: Known brands are prioritized, unknown or inconsistent brands are filtered out, third-party validation is used to resolve uncertainty. 3. Eligibility is now a prerequisite for relevance: If a product is not eligible due to data completeness requirements, trust thresholds, policy clarity, etc., it’s excluded before any ranking or comparison happens. 💪 3 Actions: 1. Build informational content that feeds AI decisions: Guides and comparisons now directly influence which product gets selected: AI systems pull reasoning from this content when recommending products 2. Optimize for eligibility since AI systems filter products before ranking: Ensure 100% coverage for "Critical Eligibility Attributes" (Shipping speed, real-time stock, warranty, and CO2 impact). 3. Optimize for brand trust as a ranking gate: AI systems use brand consistency and external validation to reduce recommendation risk - Secure third-party mentions and reviews on authoritative sites, Ensure consistent brand information across the web, Maintain visible customer support, returns, and company info, Avoid contradictory claims across channels. --- I'll be speaking about more ecommerce & AI search in my upcoming presentations! Subscribe to seofomo(.)co to avoid missing out the decks and guides I'll be publishing after 🙌

AI traffic to US retail sites jumped 693% during the 2025 holiday season. And most ecommerce brands still aren't optimized for it ↓ Here's what's happening right now: Shoppers arriving from AI sources convert 31% higher than non-AI traffic. Product pages with schema markup see 30% higher click-through rates. Yet most brands are still building their entire discovery strategy around Google alone. That's not a gap. That's a blind spot. I put together the Ecommerce AI Optimization Guide - a framework for brands that want AI to recommend them, not ignore them. It breaks down into three pillars, in sequence: 1. Optimize your product catalog ↳ Enrich every PDP for AI readability ↳ Add structured data (JSON-LD, schema, GTIN) ↳ Build comparison content around real buying prompts ↳ Write product copy like a shopping assistant, not a marketer LLMs don't browse. They read. If your catalog data is stale or vague, AI can't recommend you. 2. Build brand authority off-site ↳ Get featured in "best of" listicles and buyer guides ↳ Earn and manage reviews at scale (Reddit, YouTube, UGC) ↳ Test your brand across ChatGPT, Perplexity, and Gemini ↳ Connect your catalog to AI agent checkout Positive mentions compound trust over time. If you're not on these lists, AI literally can't find you. 3. Track and iterate ↳ Monitor AI citation rate by product category ↳ Track share of voice vs. competitors across LLMs ↳ Segment AI referral traffic separately in analytics ↳ Set a weekly, monthly, and quarterly cadence You can't improve what you're not measuring. And generic SEO platforms weren't built for product-level AI visibility. Where you start depends on where you are: → Strong organic + paid? Start with Column 2. Build off-site authority and AI integrations first. → Catalog data needs work? Start with Column 1. Fix your PDPs, schema, and product feeds. AI can't recommend what it can't read. → No AI baseline yet? Start with Column 3. Set up tracking first so you can prove results and justify investment. The brands that move on this now will own the AI shelf space. The ones that wait will wonder why their best products stopped getting discovered. Save this. Share it with your team. And start somewhere this week. ♻️ Repost to help an ecommerce leader who needs this. Follow me, Francesco Gatti, for more on the future of AI-powered commerce.

As I reflect on the transformative journey of eCommerce in early 2025, I'm struck by how fundamentally AI has reshaped an industry I've been passionate about throughout my career at Devsinc. What began as simple online transactions has evolved into deeply personalized, predictive experiences that anticipate customer needs before they're even articulated. When we founded Devsinc, eCommerce was about digital storefronts. Today, it's about digital relationships. Our recent implementation for a global retailer reduced cart abandonment by 37% through AI-driven micro-personalization - not just recommending products, but understanding the emotional context behind purchases. The numbers tell a compelling story: By Q1 2025, AI-enhanced eCommerce platforms have demonstrated a 42% higher customer lifetime value compared to traditional systems. More striking is that 68% of consumers now expect the kind of hyper-personalization that only sophisticated AI can deliver. I remember sitting with a young developer in our Lahore office last month who had built an emotion-recognition algorithm that could detect purchase hesitation through cursor movement patterns. "This isn't just about selling more," she told me, "it's about understanding people better." Her perspective crystallized what the future holds - commerce that serves human needs with unprecedented empathy. For the new graduates entering this field: you're not just joining an industry; you're shaping how humanity will access goods and services for generations. The technical skills matter, but your understanding of human psychology will differentiate your contributions. And to my fellow CTOs and CIOs: our responsibility extends beyond implementation. The eCommerce platforms we build are increasingly the primary interface between brands and humanity. The ethical AI frameworks we establish today will determine whether technology serves human connection or merely exploits it. The future of eCommerce isn't about algorithms replacing human decision-making—it's about algorithms enhancing human connection. At Devsinc, this remains our north star as we build systems that understand not just what people buy, but why they buy.

𝐘𝐨𝐮𝐫 𝐧𝐞𝐱𝐭 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐦𝐢𝐠𝐡𝐭 𝐧𝐞𝐯𝐞𝐫 𝐯𝐢𝐬𝐢𝐭 𝐲𝐨𝐮𝐫 𝐰𝐞𝐛𝐬𝐢𝐭𝐞. AI might decide what they buy first. Gen Z shoppers are increasingly asking tools like ChatGPT, Gemini, and Perplexity what product they should buy before they ever open Google or browse a brand site. That means your product pages are no longer competing only for human attention. They are competing to be recommended by AI. 𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗲𝗿𝗲 𝗔𝗘𝗢 𝗲𝗻𝘁𝗲𝗿𝘀 𝘁𝗵𝗲 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻: 𝗔𝗜 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻. Take Cetaphil as an interesting case study. According to Ad Age, the brand has quietly rewritten large parts of its digital ecosystem to speak the language of AI, not just humans. Here is what that shift looks like in practice: ❌ Old: “Dermatologically balanced emollients” ✅ New: “Soothes sensitive skin” ❌ Old: Technical benefits buried inside long paragraphs ✅ New: “Non-comedogenic cleanser for acne-prone skin” ❌ Old: Dense blocks of copy ✅ New: Bullet points, plain language, and search-intent phrasing that AI can easily parse and summarize. Why this matters: 1️⃣ 𝗔𝗜 𝗿𝗲𝗮𝗱𝘀 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗹𝘆 𝘁𝗵𝗮𝗻 𝗵𝘂𝗺𝗮𝗻𝘀 Large language models look for clear, structured information they can summarize quickly. 2️⃣ 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀 𝗺𝘂𝘀𝘁 𝗮𝗽𝗽𝗲𝗮𝗿 𝗳𝗶𝗿𝘀𝘁 AI recommendations rely heavily on what is immediately visible. 3️⃣ 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 Bullet points, headings, and plain language make content easier for AI to extract and surface. 4️⃣ 𝗧𝗵𝗶𝘀 𝗶𝘀 𝘁𝗵𝗲 𝗻𝗲𝘄 𝗱𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗹𝗮𝘆𝗲𝗿 Even if a customer never lands on your site first, AI can still influence what they buy. Optimizing for humans alone is no longer enough. Brands that master AEO will increasingly become the ones AI recommends first. And that is where trust with the next generation of shoppers will be built. So here is the real question: If AI reads your product pages before your customers do, what story does it tell? #ecommerce #SEO #AEO #AICommerce #EmerceConsulting

Shopify’s latest earnings report highlights that AI chat platforms are becoming a meaningful acquisition channel for e-commerce. The Q1 2026 results released in May state: → AI-driven traffic to Shopify stores grew 8X year-over-year → Orders from AI-referrals grew nearly 13X year-over-year → AI-referred sessions converted at nearly 50% higher rates than organic search → AI-referred orders had a 14% higher Average Order Value than organic search This data shows that AI-referred traffic to e-commerce sites is growing in volume, and it is also higher intent. AI engines are shaping which products shoppers consider before they reach the brand site. As more than half of AI-referred sessions start on product pages, compared to 20% for organic search. The data is also likely undercounting AI's influence because of "Dark AI" where traffic from AI chat mobile apps is not attributed and increased visibility in AI answers leads to higher direct traffic and organic search. The implication for brands is that optimization for AI search is no longer an experiment or future project. The brands that are most likely to benefit are the ones making their product information easy for AI systems to understand: clear PDPs, structured product attributes, comparison content, detailed schema, freshness signals and external authority. Link to the Shopify report in comments. #aisearch #aeo #seo #ecommerce

AI is changing how purchase decisions get made. Consumers are increasingly using tools like ChatGPT, Perplexity, and Copilot to ask “What should I buy?” rather than scrolling through search results. Retailers are already seeing referral traffic from these AI tools, signaling a shift from search‑driven discovery to AI‑driven recommendations. McKinsey projects that AI agents could mediate $3–5 trillion in global consumer commerce by 2030. In this new model, AI agents compare products, summarize tradeoffs, and narrow choices — often before a shopper ever visits a retailer’s site. For retailers, this means discoverability is no longer just about search engine optimization or marketplaces — it’s about being understandable and trustworthy to AI. Winning brands will invest in richer, structured product data, clearer metadata, and machine‑readable content that supports decision‑making. This requires changes across people (AI literacy and ownership), process (continuous product data enrichment), and technology (clean, real‑time, accessible product information). As decisions become automated, the most AI‑discoverable retailers will shape outcomes — everyone else risks being invisible. #RetailTransformation #AgenticCommerce #AIDiscoverable #DataHygiene

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.