AI-Driven Personalization In E-Commerce

AI: A Game Changer for Retail AI is not just infiltrating retail, it's overhauling operations. A sneak peek into its impact: 📈 Enhanced Personalization: - AI algorithms analyze massive amounts of customer data to provide personalized shopping experiences. - Tailored product recommendations based on browsing history, preferences, and purchase patterns. - Customers feel more connected to brands that truly understand their needs. 💡 Smarter Inventory Management: - Predictive analytics help retailers optimize inventory levels and minimize stockouts or overstock situations. - AI-powered systems monitor sales trends, weather forecasts, and even social media sentiment analysis to make accurate demand forecasts. - This reduces costs associated with excess inventory and ensures products are readily available when customers want them. ⏳ Efficient Supply Chain Operations: - Leveraging machine learning algorithms, AI streamlines supply chain processes by automating tasks such as procurement, transportation optimization, and warehouse management. - Real-time tracking enables better visibility and control across the entire supply chain network. - Retailers can reduce lead times, improve order accuracy, and enhance overall operational efficiency. 💬 Intelligent Customer Service: - Chatbots powered by natural language processing (NLP) provide instant support to customers 24/7 through various channels like websites or messaging apps. - Quick response time for queries improves customer satisfaction while reducing support costs for retailers. - Advanced chatbots can even handle complex inquiries or complaints without human intervention. ✨ Augmented Reality Shopping Experience: - Virtual try-on allow customers to visualize products before making purchasing decisions. - AR technology enhances online shopping by providing interactive experiences such as virtual showrooms or "try-before-you-buy" features. - This immersive experience bridges the gap between brick-and-mortar stores and e-commerce platforms. 💻 E-commerce Fraud Detection: - AI algorithms detect patterns and anomalies in real time, helping retailers identify fraudulent transactions. - Enhanced security measures reduce the risk of fraud and protect both customers and businesses from financial losses. 🌐 Seamless Omni-channel Integration: - AI enables seamless integration of online and offline channels, creating a harmonized shopping experience for customers. - Consistent branding, personalized promotions, and unified customer profiles across all touchpoints enhance the overall customer journey. Embrace AI to spearhead growth and exceptional experiences in the evolving retail landscape. #AIinRetail #RetailRevolution #CustomerExperience #InnovationInTheIndustry

#Amazon just killed the old e-commerce algorithm. Rufus now has memory & it changes the game more than Prime ever did. For 20 years, #ecommerce placements ran on two engines: ▪️Product-based logic → “You bought a phone, here’s a case.” ▪️Crowd-based logic → “People who bought X also bought Y.” That era is over. Now, with Rufus AI memory, a third engine arrives: ▪️Contextual logic → “Yesteday you asked for trail shoes. Today you’re back - here’s a water-resistant jacket that completes your kit.” This is bigger than chat. Rufus memory will fuel every surface on Amazon: Sponsored Display, PDP recos, offsite retargeting. One memory, everywhere. A full-funnel intelligence system that learns once and sells everywhere. Why it matters: 1️⃣ Smarter cross-sell → Rufus won’t waste placements on what was just bought. It will anticipate the next logical purchase 2️⃣ Full-funnel impact → Memory won’t stay in chat. Expect it to power every algorithmic slot across Amazon. 3️⃣ Journey > click → Performance is no longer about CTR. The real metric: How often does Rufus recall and re-recommend your brand across the funnel? 4️⃣ Content = algorithm fuel → If your PDP doesn’t spell out connections (pairs with, next in routine, complementary use cases), Rufus won’t link you into the journey. What brands must do now: ▪️Design ecosystems, not SKUs → Build routines, bundles, and adjacencies. Memory rewards portfolios that tell a story. ▪️Engineer cross-sell signals → Use content to “teach” Rufus where your product fits in the customer journey. ▪️Hit hygiene benchmarks → Near-200 character titles, 7+ visuals, A+ content, 4.3★+, Prime/FBA - still a non-negotiable fundamental priority ▪️Adopt new KPIs → Share of voice in Rufus answers, attach rate, and repeat recommendation frequency. Business impact This is the algorithmic pivot of the decade. Contextual AI shifts Amazon from a #marketplace with recommendations into a shopping brain that curates, recalls, and predicts. Every surface, every placement, every touchpoint is now personalized by a history of interactions. Day 1 for the industry - we will see other #online #OMNIchannel giants follow. Retailers with strong loyalty programs are sitting on a goldmine once they connect life context with shopping intent. If you’re not training contextual algorithms to remember your brand, you’re training them to forget you.

Ever seen a beautiful perfume bottle online (on TikTok or Instagram) through a review by an influencer or in a shop, but couldn't get a sense of its scent? As a perfume enthusiast, I've been there! That challenge inspired my latest project: a computer vision pipeline that provides deep insights without a single sniff. I wanted to build a system that goes beyond simple recognition. In the messy reality of social media posts and retail displays, perfume bottles are often surrounded by clutter. My pipeline aims to mimic human perception: first, finding the bottle; second, recognizing it; and finally, inferring its core characteristics like its fragrance family (or notes). Here's a look at how this end-to-end system works: Precise Object Detection: I leveraged YOLOv5 to train a model that expertly locates perfume bottles in diverse, real-world images, outputting exact bounding boxes. Fine-Grained Product Identification: The cropped bottle images are then fed into a fine-tuned ResNet50 classifier, capable of distinguishing specific products (e.g., "Chanel No. 5," "YSL Black Opium") even among similar-looking bottles. Fragrance Family Classification: The identified product is then mapped to its corresponding fragrance family (e.g., Floral, Woody, Oriental) using a second-stage ResNet classifier trained on curated scent metadata. To ensure this sophisticated backend was easily accessible, I architected the deployment in two key parts: The core inference logic is exposed via a Flask API, providing a robust and scalable way to integrate the computer vision models. The entire user experience, from image upload to results display, is powered by a Streamlit app, seamlessly hosted on Streamlit Cloud for global accessibility and ease of use. This project showcases how we can bridge the gap between clean datasets and real-world image complexity, leveraging modern deployment practices to deliver a tangible solution. It excites me because it touches on real-world problems in visual search and AI-powered retail, all while helping fellow perfume lovers explore new scents! PS: Still in demo phase so let me know about the bugs, what you think or if you have any feedback? Explore the live demo here: https://lnkd.in/gxp53uCK

Something clicked for me this week… I was exploring how AI is changing how we shop! And I realised we’re entering a completely NEW phase of product discovery 😅 Not through Google. ❌ Not through influencers.❌ But through conversational AI.✅ People aren’t just searching anymore. They’re asking. - “What’s the best shampoo for curly hair?” - “Recommend a vegan face wash under £20.” - “What are some eco-friendly travel kits?” And here’s the part that stopped me: And if your product isn’t showing up in ChatGPT or Gemini? You’re not just missing sales. You’re invisible. I have worked in marketing for decades! I’ve seen trends come and go. But this one feels different. It’s not a “maybe.” It’s happening. Quietly. Rapidly. So I’ve started looking into: - How D2C brands can register their product data into AI models - What tools like ChatGPT actually “see” when people ask for recommendations -And how we, as marketers, can show up where the next-gen consumer is asking This shift reminds me of when brands hesitated to build for mobile. Or ignored TikTok at first. We all know how that played out. 😉 This new wave of AI-first discovery needs a different playbook. 👉🏻👉🏻 So here’s a step by step guide for D2C brands to show up in AI recommendations: 1. Structure your product data using Schema.org / JSON-LD 2. List on Google Shopping, Amazon, and Shopify (these feed AI models) 3. Integrate with ChatGPT plugins like Klarna, Shop.app or use your own GPT 4. Write product content that answers real questions (not just keywords) 5. Test visibility by asking ChatGPT or Gemini to recommend your product category 6. Keep optimizing based on what AI can (and can’t) find If you’re building a brand today especially in D2C, it’s time to think beyond search. The new product shelf is a ‘chat box’ :) If you are a D2C founder or a consumer brand and need more insights on to leverage AI first shopping, comment ‘Product discovery’ and I will share the playbook with you. #AI #D2C #Productdiscovery #Search #Shoppingtrends #consumerbehaviour

It was a pleasure to talk to Paul Morrison at WNS about the impact of AI on retail. We discussed a wide range of topics, from the impact of GenAI on retailers operations, to how it could impact the customer journey. It's such a fascinating area which is changing at pace. Here are a few areas that I think will see the largest impact. ➡ Personalisation at Every Stage GenAI crafts individual experiences, from targeted product recommendations based on past purchases to custom promotions that hit right when a customer is most receptive. It builds customer loyalty by making each interaction feel tailor-made. ➡ Intelligent CX Support (WISMO) Solving the most common customer concern, “Where’s my order?” GenAI-powered chatbots handle this and other frequent queries instantly, freeing up staff and providing seamless, reliable support—no human intervention needed. ➡ Predictive Inventory Management By analysing sales patterns and seasonal demand (and thousands of other inputs such as weather, supply chain disruptions, social media buzz), GenAI forecasts precisely what stock to have on hand, minimising costly overstocking or disappointing stockouts. This ensures products are ready when customers want them. ➡ Dynamic Pricing, Rewards, and Promotions for Real-Time Relevance GenAI empowers retailers to adjust prices, rewards, and promotions in real-time based on demand, competitor trends, and customer profiles. This approach ensures every deal feels personalised, offering customers relevant discounts or loyalty rewards right when they’re most likely to engage. It’s a seamless way to stay competitive, maximise margins, and increase customer satisfaction—all while driving repeat business through tailored offers that adapt to each shopper's unique journey. ➡ Enhanced Loyalty Through Personalised Rewards GenAI helps personalise loyalty programme rewards, delivering offers that resonate based on individual behaviour, increasing retention and turning one-time buyers into repeat customers. Please do have a listen, I really enjoyed the conversation. Apple: https://bit.ly/AP3-L Spotify: https://bit.ly/SO3_L Amazon Music: https://bit.ly/AZ3_L

The recommendation is a powerful tool for e-commerce sites to boost sales by helping customers discover relevant products and encouraging additional purchases. By offering well-curated product bundles and personalized suggestions, these platforms can improve the customer experience and drive higher conversion rates. In a recent blog post, the CVS Health data science team shares how they explore advanced machine learning capabilities to develop new recommendation prototypes. Their objective is to create high-quality product bundles, making it easier for customers to select complementary products to purchase together. For instance, bundles like a “Travel Kit” with a neck pillow, travel adapter, and toiletries can simplify purchasing decisions. The implementation includes several components, with a key part being the creation of product embeddings using a Graph Neural Network (GNN) to represent product similarity. Notably, rather than using traditional co-view or co-purchase data, the team leveraged GPT-4 to directly identify the top complementary segments as labels for the GNN model. This approach has proven effective in improving recommendation accuracy. With these product embeddings in place, the bundle recommendations are further refined by incorporating user-specific data based on recent purchase patterns, resulting in more personalized suggestions. As large language models (LLMs) become increasingly adept at mimicking human decision-making, using them to enhance labeling quality and streamline insights in machine learning workflows is becoming more popular. For those interested, this is an excellent case study to explore. #machinelearning #datascience #ChatGPT #LLMs #recommendation #personalization #SnacksWeeklyOnDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gb6UPaFA

Here’s how brand & retail C-levels should read OpenAI's insights on how people use ChatGPT (1.1M+ conversations): 1️⃣ Consumers use AI mostly for guidance (28.3%) and writing (28.1%). Shoppers rely on AI for 'everyday support', from tutoring over health advice to drafting text. 🛍️ In retail this translates to AI that helps consumers compare, explain & guide purchases rather than just sell = concierge at scale. ↳ Launch AI assistants as trusted advisors, not transaction engines. Credibility & expertise drive conversion! 2️⃣ Seeking info (21.3%) is the biggest motivation. Almost 1/5 are explicitly searching for specific info (18.3%). 💬 Customers know what they want to know.. Give them deep, accurate answers (e.g. origin of materials, how to style their new shoes, skincare ingredient safety). ↳ Answer Engine Optimization (AEO) will become as important as SEO! Invest in structured knowledge bases + natural-language search to capture this intent. If not, you'll lose terrain already at the Discovery Stage. 3️⃣ Creative ideation + self expression matter more than you think. People are experimenting with AI to create, role-play & brainstorm. 🧑🎨 Expect a bigger demand for co-creation. Soon consumers will want to design their own items, packaging, looks, 1 of 1 personalized recommendations, ... ↳ Pilot AI Brand creativity tools (e.g. sneaker customization, fragrance builder, style boards, ...) to increase engagement and loyalty. 4️⃣ Health, fitness, beauty & self-care (5.7%) have a natural overlap with luxury. The data confirms strong consumer reliance on AI for Beauty & Wellness. Both are strong pockets of growth for the luxury industry today. ↳ Lean into AI-driven beauty, skincare, nutrition & wellness advice. 💡 Merging expert guidance + brand storytelling + AI personalization is the way to win. 5️⃣ Technical help (7.5%) and translation (4.5%) are undercurrents. Today, we already see customers using AI to decode complexity. Whether they use it to translate, calculate or vibe code... ↳ So why not deploy AI to simplify e-commerce? Auto-duty calculators, multilingual product storytelling, aftercare instructions, returns automation, ... 6️⃣ Multimedia creation is still niche (6%), but growing. Image generation is only 4.2% today, yet visual platforms are dominating culture. So we can expect hyper-growth here. Visual AI will explode as fashion, sneakers & luxury are highly image-driven. Early adopters can own this space. ↳ Use AI for campaign content, product visualization or UGC enhancement.. BUT ensure strict brand control to maintain luxury equity. ... WHAT TO REMEMBER ... Consumers are not using AI primarily for fun, but for help, answer & creativity. The edge will come from turning AI into branded experiences, not just a back-office tool. 🚨 Are you a startup that's moving the needle in this space? Leave a comment with what you're doing and I'll hit you up.

𝗪𝗮𝗹𝗺𝗮𝗿𝘁 𝘂𝗻𝘃𝗲𝗶𝗹𝘀 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝘀𝗵𝗼𝗽𝗽𝗶𝗻𝗴: 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗲𝗱 𝗛𝗼𝗺𝗲𝗽𝗮𝗴𝗲𝘀 𝗳𝗼𝗿 𝗘𝘃𝗲𝗿𝘆 𝗦𝗵𝗼𝗽𝗽𝗲𝗿? 🤯 Walmart is pushing the boundaries of hyper-personalization. With a strategy that blends AI and AR, Walmart is leading the charge into a new era of retail, where every shopping experience feels custom-made. When Javier Gascón Inchausti shared the news last night in our The New Retail Business School experts forum, I was beyond than impressed. But the personalized homepage is just one piece of Walmart’s bold Adaptive Retail strategy, creating profoundly personal experiences, both online and in-store. In 2024, Walmart reported a staggering revenue of $648 billion, solidifying its position as the world’s largest retailer by revenue. 📲 Here’s how Walmart is revolutionizing retail: ✨ Custom homepages for every shopper: Walmart’s new AI-powered Content Decision Platform will soon allow each shopper to see a homepage tailored specifically to their preferences, interests, and past behaviors. 🔍 Retail-specific Large Language Models (LLMs): Walmart’s proprietary LLM platform, 𝗪𝗮𝗹𝗹𝗮𝗯𝘆, is trained on decades of internal data to provide highly contextual responses and personalized customer support. 🤖 AI-powered Customer Support Assistant: Leveraging GenAI, Walmart has created a more personalized Customer Support Assistant that recognizes customers from the start. This has made issue resolution faster and smoother for customers. 🔮 Augmented Reality shopping: Walmart is introducing 𝗥𝗲𝘁𝗶𝗻𝗮, an AR platform that allows shoppers to explore products in 3D environments, bringing immersive commerce experiences to life. From testing items in your home with "View in Your Home" to buying virtual goods for your avatars (and the real ones for yourself!), Walmart is taking shopping beyond the traditional model. 📦 Enhanced in-store and eCommerce integration: Walmart’s AI isn’t just transforming online experiences. It's also revolutionizing in-store operations by connecting employees with smarter data for product finding, stocking, and managing orders. Over 850M data points in Walmart’s product catalog have been improved, streamlining the process for associates and customers. 🎮 New virtual social environments: Walmart is expanding its reach into virtual worlds by testing immersive commerce platforms like Unity and Zepeto, allowing customers to buy both virtual items for their avatars and physical goods for themselves. As Walmart blends AI, AR, and immersive commerce, it’s redefining retail. The future of retail is deeply personal, immersive, and tech-driven, and Walmart is setting the bar. It's all about creating engaging, tailored experiences that meet customers exactly where they are, whether it’s online, in-store, or even in virtual worlds. #RetailInnovation #AI #AR #Personalization #CustomerExperience #Walmart #RetailTrends #FutureOfRetail #RetailTech #ImmersiveCommerce

🚀 How do you ensure your customers see what they want to see — not just what you want to show? With AI and ML becoming core to ecommerce (both B2B and B2C), product discovery is getting a lot of attention. And rightly so. But here's the truth: most recommendation engines fail not because the models are bad, but because the first two steps were never right. Let me explain. Many product managers (especially in fast-paced orgs) jump into building rec engines with a "let's plug in collaborative filtering and see how it goes" mindset. But without clearly defining what type of recommendation makes sense for your use case — and how it ladders up to a business metric — you're setting yourself up for rework. Here's how I approach it when working with teams: Step 1: Business Understanding: Start with the why before touching the how. ◾ What are you recommending? Products? Content? Users? Services? ◾What does success look like? Higher CTR? More revenue? Better retention? ◾Where will it show up? Homepage, PDP, cart, email, app banner? ◾What constraints exist? Does it need to be real-time? Can it be batched overnight? Without alignment on this, even the most advanced ML model will fall flat. Step 2: Choose the Right Recommendation Type: Now comes the how — but it should be tailored to your product + user journey. ◾Content-based filtering: “You liked this, so you’ll like these similar items.” ◾Collaborative filtering: “Users like you also bought this.” ◾Hybrid models: The best of both worlds — widely used in ecommerce and streaming. ◾Knowledge-based systems: Rule-driven, useful when personalization is constrained (e.g., insurance, banking). Let me make this concrete with a simple example: Imagine you’re building a recommendation module for a first-time visitor on your site who hasn’t logged in. If you apply collaborative filtering, it’ll fail — there’s no past data to compare. But if you use content-based filtering on the item they’re browsing and pair it with trending items, you instantly make the experience better. It’s not about which model is smarter. It’s about which makes sense for the scenario. Let’s be honest — your recommendation engine’s success doesn’t start with machine learning. It starts with product thinking. #AI #ProductManagement #Ecommerce #Personalization #RecommendationEngine #ProductStrategy I write about #artificialintelligence | #technology | #startups | #mentoring | #leadership | #financialindependence   PS: All views are personal Vignesh Kumar

For years, true personalization in ecommerce felt out of reach, too complex, too reliant on massive data infrastructure But in 2025, it’s not just possible, it’s expected * Customer Data Platforms (CDPs) can now unify behavioral, transactional, and anonymous data to recognize visitors in real-time and dynamically segment audiences. * Generative AI builds on that foundation, automating hyper-personalized product recommendations, emails, and even entire storefronts tailored to browsing habits, purchase history, and preferences * Today’s ecommerce personalization means: individualized landing pages, AI chat that understands customer intent, and product suggestions that evolve with each click Brands are no longer optimizing for demographics, they’re creating a “segment of one” The results? Higher conversion rates, deeper customer retention, and a distinct competitive advantage But unlocking this requires more than tech; it demands a strategic approach to data, tools, and team readiness Are you leveraging personalization as a growth engine?