AI-Driven Personalization In E-Commerce

🤖 How To Design Better AI Experiences. With practical guidelines on how to add AI when it can help users, and avoid it when it doesn’t ↓ Many articles discuss AI capabilities, yet most of the time the issue is that these capabilities either feel like a patch for a broken experience, or they don't meet user needs at all. Good AI experiences start like every good digital product by understanding user needs first. 🚫 AI isn’t helpful if it doesn’t match existing user needs. 🤔 AI chatbots are slow, often expose underlying UX debt. ✅ First, we revisit key user journeys for key user segments. ✅ We examine slowdowns, pain points, repetition, errors. ✅ We track accuracy, failure rates, frustrations, drop-offs. ✅ We also study critical success moments that users rely on. ✅ Next, we ideate how AI features can support these needs. ↳ e.g. Estimate, Compare, Discover, Identify, Generate, Act. ✅ Bring data scientists, engineers, PMs to review/prioritize. 🤔 High accuracy > 90% is hard to achieve and rarely viable. ✅ Design input UX, output UX, refinement UX, failure UX. ✅ Add prompt presets/templates to speed up interaction. ✅ Embed new AI features into existing workflows/journeys. ✅ Pre-test if customers understand and use new features. ✅ Test accuracy + success rates for users (before/after). As designers, we often set unrealistic expectations of what AI can deliver. AI can’t magically resolve accumulated UX debt or fix broken information architecture. If anything, it visibly amplifies existing inconsistencies, fragile user flows and poor metadata. Many AI features that we envision simply can’t be built as they require near-perfect AI performance to be useful in real-world scenarios. AI can’t be as reliable as software usually should be, so most AI products don’t make it to the market. They solve the wrong problem, and do so unreliably. As a result, AI features often feel like a crutch for an utterly broken product. AI chatbots impose the burden of properly articulating intent and refining queries to end customers. And we often focus so much on AI that we almost intentionally avoid much-needed human review out of the loop. Good AI-products start by understanding user needs, and sparkling a bit of AI where it helps people — recover from errors, reduce repetition, avoid mistakes, auto-correct imported files, auto-fill data, find insights. AI features shouldn’t feel disconnected from the actual user flow. Perhaps the best AI in 2025 is “quiet” — without any sparkles or chatbots. It just sits behind a humble button or runs in the background, doing the tedious job that users had to slowly do in the past. It shines when it fixes actual problems that it has, not when it screams for attention that it doesn’t deserve. Useful resources: AI Design Patterns, by Emily Campbell https://www.shapeof.ai AI Product-Market-Fit Gap, by Arvind Narayanan, Sayash Kapoor https://lnkd.in/duEja695 [continues in comments ↓]

AI products like Cursor, Bolt and Replit are shattering growth records not because they're "AI agents". Or because they've got impossibly small teams (although that's cool to see 👀). It's because they've mastered the user experience around AI, somehow balancing pro-like capabilities with B2C-like UI. This is product-led growth on steroids. Yaakov Carno tried the most viral AI products he could get his hands on. Here are the surprising patterns he found: (Don't miss the full breakdown in today's bonus Growth Unhinged: https://lnkd.in/ehk3rUTa) 1. Their AI doesn't feel like a black box. Pro-tips from the best: - Show step-by-step visibility into AI processes - Let users ask, “Why did AI do that?” - Use visual explanations to build trust. 2. Users don’t need better AI—they need better ways to talk to it. Pro-tips from the best: - Offer pre-built prompt templates to guide users. - Provide multiple interaction modes (guided, manual, hybrid). - Let AI suggest better inputs ("enhance prompt") before executing an action. 3. The AI works with you, not just for you. Pro-tips from the best: - Design AI tools to be interactive, not just output-driven. - Provide different modes for different types of collaboration. - Let users refine and iterate on AI results easily. 4. Let users see (& edit) the outcome before it's irreversible. Pro-tips from the best: - Allow users to test AI features before full commitment (many let you use it without even creating an account). - Provide preview or undo options before executing AI changes. - Offer exploratory onboarding experiences to build trust. 5. The AI weaves into your workflow, it doesn't interrupt it. Pro-tips from the best: - Provide simple accept/reject mechanisms for AI suggestions. - Design seamless transitions between AI interactions. - Prioritize the user’s context to avoid workflow disruptions. -- The TL;DR: Having "AI" isn’t the differentiator anymore—great UX is. Pardon the Sunday interruption & hope you enjoyed this post as much as I did 🙏 #ai #genai #ux #plg

🚨Breaking🚨 Amazon just turned Rufus into an agent. Every AI shopping assistant waits for you to ask. Amazon just stopped waiting. Amazon rolled out 'Scheduled Actions' in Rufus last week. It offers a handful of suggested use cases to get shoppers started, and the one I clicked on for the video below was "Set a birthday reminder with gift ideas." Sounds boring, but it's actually quite a bit shift in capability. Gift discovery is one of the biggest use cases driving people to ChatGPT and other general-purpose LLMs for shopping help. "What should I get my brother-in-law who likes cycling and hates gadgets" is exactly the kind of query Amazon can't afford to lose to OpenAI. Scheduled Actions does two jobs at once: it pushes Amazon further into agentic commerce, and it builds a moat around many of retail's most lucrative, most AI-native categories. Scheduled Actions lets Rufus act on a schedule instead of only responding to live queries. Amazon's other starter suggestions include monthly coffee recommendations, new book alerts, and automated cleaning supply restocks. But the suggested prompts aren't the story. They're training wheels. The real shift is that shoppers can define their own. 🗣️ Remind me to buy running shoes every 18 months. 🗣️ Alert me when the price on [specific product] drops below $X. 🗣️ Send me a new hot sauce every fortnight. 🗣️ Remind me about pet food before it runs out, based on the pack size. 🗣️ Suggest new swimwear before the kids outgrow them. Amazon just handed shoppers a programmable layer over their own commerce life. Why the birthday example is an intersting one: 🛒 Gift-giving is one of the highest-intent, most brand-agnostic queries in commerce. "Gift ideas for mum" doesn't care whose brand ranks, it cares about fit. 🛒 Birthdays are recurring and predictable. Amazon now has a calendar of future purchase intent, not a guess. 🛒 It captures relationship data Amazon didn't own before. Who matters to you, their age, what you bought last year, what they actually liked. 🛒 It moves discovery off Google and ChatGPT and into Amazon's own interface, where the shelf is stocked and checkout is one tap. For brands, this rewrites the brief. If your product isn't in Amazon's logic for specific occasions, usage patterns, replenishment cycles, and relationships, you don't get scheduled. Structured attributes stop being a hygiene factor and become the entire shelf. For retailers, the warning is sharper. Every scheduled action Amazon captures is a future purchase locked in before anyone else gets to pitch. No ad auction. No comparison tab. No competing retailer even in the room - it's definitely time to begin exploring your own conversational assistant. The interesting question isn't which use cases Amazon suggests💡 It's what shoppers will invent once they realise Rufus can just do it for them 🤔

AI is getting more personal — and it’s changing how global brands connect with consumers. We’re entering an era where AI doesn’t just automate — it individualizes. From product design to marketing, personalization is becoming the new standard of brand experience. 🟣 Nike uses AI to tailor product recommendations and predict purchasing behavior through its SNKRS and Nike App platforms — driving a reported 40% increase in engagement. 🟢 Coca-Cola leveraged generative AI for its “Create Real Magic” campaign, allowing fans to co-create digital art and content, reaching over 2 billion impressions globally. 🔵 Starbucks uses its “Deep Brew” AI engine to personalize offers and store operations, contributing to a 10–15% lift in loyalty engagement. 🔴 Netflix attributes over 80% of viewership to AI-driven recommendations — proving how deeply personalization drives retention. What’s changing is not just the technology, but the intent: AI is no longer about scaling efficiency — it’s about scaling empathy. The brands that lead this shift are turning data into connection, algorithms into experience, and scale into trust. #ArtificialIntelligence #Personalization #BrandInnovation #MarketingAI #CustomerExperience #GenerativeAI via @tingle.ai #DigitalTransformation #Ai

Google just announced a suite of AI-driven shopping tools designed to transform the online retail experience 💥 Here’s what’s coming: 🧠 AI Shopping Mode: A conversational search tool that understands natural language and context. Think: “best dress for a trip to Cannes in May” – and it will deliver results tailored to weather, location, and purpose. 👗 Virtual Try-On 2.0: Shoppers can now upload full-body photos to see how items actually look on them — including drape, fit, and texture across different body types. A game-changer for apparel conversion rates. 💸 Agentic Checkout + Price Alerts: Users can set price targets, and when an item hits the right price, Google will notify them — or even auto-checkout via Google Pay. What this means for eComm marketers: ▪️ SEO and product data quality will become even more critical ▪️ Conversion will increasingly depend on visual assets (UGC, try-ons, real models) ▪️ Brands need to prep for a world where shopping journeys are conversation-first Currently, these features are being rolled out in the U.S. via Google Search Labs, with plans for broader availability in the future.

Marketing Automation & Customer Service is no longer just about sending emails or filling out contact forms. With AI these flows can become journeys: interactive and truly personalized - unlocking new levels of engagement and conversion in Whatsapp or Chat. But where to start? Here’s a breakdown of the top journeys most e-commerce brands have implemented and how I rank their AI potential and impact: 1️⃣ Product Recommendations | AI Potential: High Helping your customer to make a choice and find the product that fits their needs. > Move beyond static scripts! AI can find best fitting products with LLM powered semantic search, resolve blockers, compare products and provide tailored suggestions. 2️⃣ Welcome Flow | High You offer an incentive, collect and opt-in and further into > With AI, this flow can become interactive: No form like answering all extrated from a normal informal conversation. Enrich their profiles for future personalization (email, birthday, ...) 3️⃣ Customer Service | High Taking care when your customers have a problem: > AI Agents will provide 24/7 multilingual support. Collect the info you need before handing over to a human if the certain problems still need the human insight, access, or touch. Save costs while enhancing customer experience. 4️⃣ FAQ Automation | Medium Make it easy for customers to find answers. > AI ensures responses are nuanced and personalized. 5️⃣ Abandoned Cart | Medium Customer is (almost) ready to buy, but got interrupted or needs a little nudge > Send a(i) personalized message based on the exact product they have in their cart. Highlight how it fits their preferences or past purchases. 6️⃣ Cross-Sell / Up-Sell | Medium Encourage customers to buy complementary products. > AI can craft compelling arguments for upgrades, bundles or next product to buy. 7️⃣ Birthday or Special Day Campaigns | Medium Send wishes and a little gift > Let AI create a personalized message, image, or video and send it via WhatsApp. 8️⃣ Winback / Replenishment | Low Remind customers to repurchase or return. > Personalization helps, but the core is timing. 9️⃣ Review Collection | Low Gather feedback and build trust with REVIEWS.io or alike > AI can personalize requests and handle negative feedback gracefully avoiding bad reviews. 🔟 Back-In-Stock | Low Notify customers when the product they wanted to buy is available again. > AI can add a personalized touch to the reminder [don't want to get out of stock? Talk to VOIDS] 1️⃣1️⃣Referral Programs | Low Encourage word-of-mouth with incentives for sharing. > AI can personalize referral messages for higher trust and conversion. 1️⃣2️⃣Fulfilment Updates | Low Keep customers informed about their orders. > Let AI add a personal touch related to the product shipped. [Want to turn into an upsell opportunity: Karla is doing a great job here] The future of e-commerce is about conversations, not campaigns. Which flow or journey are you excited to tackle first? #conversationalai

The search bar is dead. And most e-commerce platforms don’t even know it yet. After working closely with AI systems and recommendation engines, I’ve learned one thing: “Personalized shopping” was never truly personal. It was pattern matching. It was collaborative filtering. It was reactive logic pretending to be intelligence. Now we’re entering a different era. → From personalized to personal → From search-based discovery to proactive intelligence → From browsing endlessly to AI agents working for you This is agentic commerce. Traditional e-commerce makes you do the heavy lifting: Search → Filter → Scroll → Compare → Hope Agentic commerce flips the entire model: Describe what you want → AI delivers with context One of the most interesting examples I’ve seen is Glance. They are not building another shopping app. They’re building a contextual, agentic AI commerce layer powered by multiple specialised agents working together. Instead of one algorithm guessing what you like, Glance deploys multiple AI agents working for you in parallel: → Weather Agent analysing real-time climate and fabric suitability → Trends Agent tracking global shifts and micro-trends → Occasions Agent anticipating upcoming events → Physical Agent understanding your skin tone, undertones, and body type → Lifestyle Agent decoding your aesthetic preferences All coordinated by an orchestrator that synthesises everything into a unified styling strategy. That’s not basic personalization. That’s contextual intelligence. And the most powerful shift? You see yourself in the generated looks. Not stock visuals. Not generic models. You. Commerce becomes a conversation instead of a search box. From personalized to personal. AI agents working for you. Learning with every interaction. Refining your style instead of just tracking clicks. This is the rise of agentic commerce. #Glance #AICommerce #AgenticAI

As an analyst, I was intrigued to read an article about Instacart's innovative "Ask Instacart" feature integrating chatbots and chatgpt, allowing customers to create and refine shopping lists by asking questions like, 'What is a healthy lunch option for my kids?' Ask Instacart then provides potential options based on user's past buying habits and provides recipes and a shopping list once users have selected the option they want to try! This tool not only provides a personalized shopping experience but also offers a gold mine of customer insights that can inform various aspects of a business strategy. Here's what I inferred as an analyst : 1️⃣ Customer Preferences Uncovered: By analyzing the questions and options selected, we can understand what products, recipes, and meal ideas resonate with different customer segments, enabling better product assortment and personalized marketing. 2️⃣ Personalization Opportunities: The tool leverages past buying habits to make recommendations, presenting opportunities to tailor the shopping experience based on individual preferences. 3️⃣ Trend Identification: Tracking the types of questions and preferences expressed through the tool can help identify emerging trends in areas like healthy eating, dietary restrictions, or cuisine preferences, allowing businesses to stay ahead of the curve. 4️⃣ Shopping List Insights: Analyzing the generated shopping lists can reveal common item combinations, complementary products, and opportunities for bundle deals or cross-selling recommendations. 5️⃣ Recipe and Meal Planning: The tool's integration with recipes and meal planning provides valuable insights into customers' cooking habits, preferred ingredients, and meal types, informing content creation and potential partnerships. The "Ask Instacart" tool is a prime example of how innovative technologies can not only enhance the customer experience but also generate valuable data-driven insights that can drive strategic business decisions. A great way to extract meaningful insights from such data sources and translate them into actionable strategies that create value for customers and businesses alike. Article to refer : https://lnkd.in/gAW4A2db #DataAnalytics #CustomerInsights #Innovation #ECommerce #GroceryRetail

NEW from me for Modern Retail: The application of artificial intelligence as a customer-facing element of physical stores is far from one-size-fits-all. In February, The Vitamin Shoppe opened an “innovation store” in New York City’s Upper East Side with a “Shoppe Advisor” touch screen. The AI-powered screen provides product information, wellness articles and videos, as well as information on in-store and online inventory. It aims to enable “more informed, interactive conversations throughout the shopping experience,” according to Retail Dive. Last summer, The Guitar Center Company launched Rig Advisor, an AI shopping assistant used by customers on the store floor to explore and compare gear. Customers can scan a QR code in the store and type in a question, and Rig Advisor will recommend products that are in stock at that specific location. Guitar Center CEO Gabe Dalporto told Modern Retail in December that Rig Advisor was built to fill the void when a customer walks into a store and the associates are too busy with other guests to help them. “This is basically everything an associate can do, on your app or on your mobile device,” Dalporto said. These two examples alone show how AI use cases for in-store shopping, in discovery, research and checkout, can vary. Not even considering behind-the-scenes use cases like supply chain tech and employee assistants, retailers are finding all sorts of ways to bring the technology into brick and mortar. These range from big kiosks to mobile app features, audio summaries or computer vision. Story below with Greg Carlucci of Gartner and Melissa Minkow of CI&T. https://lnkd.in/gYJF9CUg

I went to an AI UX workshop last night expecting recycled LinkedIn advice about "building AI trust through transparency." Instead, Isabella Yamin tore down LinkedIn's job posting flow using her CarbonCopies AI framework in real-time, while founders shared raw implementation struggles. It completely changed how I'm rethinking Maibel's onboarding flow. Here's what I stole from B2B SaaS principles to redesign emotional AI for B2C: 1️⃣ Progressive disclosure with purpose LinkedIn's fatal flaw? Optimizing for completion ease > Outcome quality. Recruiters are drowning in irrelevant applications because AI never learns what "qualified" means. The personalization paradox: How do we give users enough control without overwhelming them? Users don't want "frictionless". They want INFORMED control. 📌 At Maibel: I was falling into the same trap, making emotional coaching setup so simple that the AI couldn't understand user context. Now? Progressive complexity with clear trade-offs. Show users how their choices impact outcomes. → Want deeper insights? Add more context. → Want faster setup? Here's what the AI can't personalize. 2️⃣ Closed-loop data intelligence: What Platfio gets right They've built a platform for software agencies where where every data point feeds back into the entire system. User preferences in marketing flows shape proposals. Campaign performance shapes future recommendations. Every interaction becomes intelligence for future recommendations. 📌 At Maibel: Most wellness apps store emotional check-ins like digital journals. I'm turning them into predictive feedback loops. Emotional intelligence isn’t static but COMPOUNDS. Today's reflections shift tomorrow's suggestions. Patterns fuel prevention. Users' inputs on Monday could predict AND prevent Friday's breakdown. 3️⃣  Multi-modal creativity: Wubble's transparency approach Translating images and files into music - who'd have thought? They've cracked multi-modal creativity where users become co-creators, not passive consumers. The breakthrough moment for me: What if users could see how their visual environment contributes to emotional context? 📌 At Maibel: Users upload images of their day and see how AI analyzes emotional cues: cluttered workspace = overwhelm, junk food = stress eating. Multi-modal understanding users can contribute to and influence. 💡 The bottom line? B2B Saas gets one thing right: Every interaction has to earn trust. In B2B, failed AI means churn. In emotional AI, failed trust breaks belief in tech entirely. 📌 Here's what we're doing differently at Maibel: → Progressive complexity → Context-aware feedback → Multi-modal participation → Intelligence that compounds with every input. It's not just about building WITH AI. I'm designing systems that learn understand YOU before you even need to explain yourself. Kudos to Isabella, Shivang Gupta The Generative Beings, Shaad Sufi Hayden Cassar and everyone who shared deep product insights.