Real-Life Examples Of AI Personalization In Retail

Explore top LinkedIn content from expert professionals.

Summary

AI personalization in retail refers to the use of artificial intelligence to create tailored shopping experiences by analyzing customer behavior, preferences, and real-time data. From personalized recommendations to dynamic content adjustments, businesses are leveraging AI to make shopping more intuitive and engaging.

  • Blend data with context: Top retailers like Starbucks and McDonald's use AI to factor in customer preferences, weather, and time of day to provide tailored recommendations, such as customized drink suggestions or dynamic drive-thru menus.
  • Create conversations, not transactions: Companies like ASOS are implementing generative AI tools to offer conversational shopping experiences, helping customers discover products based on their unique fashion preferences.
  • Combine AI with human touch: Retailers like Target use AI for tasks like style matching and product pairing while empowering staff to build loyalty through personalized, human-driven customer interactions offline and online.
Summarized by AI based on LinkedIn member posts
  • View profile for Luke deWilde

    GTM & Marketplace Leader @ Claim | Investor & Advisor | 📝 Write about tech x culture | Ex-Warriors, 49ers

    5,493 followers

    Main Street, 1985: The corner store clerk knows your name and your Tuesday Coca-Cola fix is waiting for you when you walk in. No data lakes, just memory and genuine care. As operators, how do we build for this experience with all this pressure to implement AI? Fast-forward to 2025. Foot traffic has moved online, loyalty stacks have exploded, and AI promises that same sixth-sense hospitality at hyperscale… but usually it still feels like a spreadsheet wearing a name tag. Where AI is already adding delight today: ☕ Starbucks: Deep Brew blends weather + daypart + order history - Frappuccinos on a scorcher, Pumpkin Cream Cold Brew when the SF fog rolls in. 🔺 McDonald’s: They bought, integrated and sold Dynamic Yield to flip drive-thru menus in real time (rain = more McCafé). 🍕 Domino’s ANZ: DOM Pizza Checker computer vision has inspected 50M+ pizzas, catching quality issues before the pies hit the box. Where AI kind of sucks right now: 🤷 The black box problem: Predicts what, rarely why - and why is where hospitality lives. Creepy over-personalization: one mistimed ping trained on too much data goes helpful into stalker-y very fast 🚰 Data flows one way: the brand “knows” me, but I don’t feel known and usually can't wait in Here's what I want to see more of from merchants and brands: 1️⃣ Humanizing the handshake. Kick off new-user flows or product intros with a 10-second voice note from the founder or CEO. Low lift, high warmth. 2️⃣ Letting data pick the moment, letting people pick the words to keep your brand standing out. Algorithms trigger when; your team writes copy that still sounds like you. Even you personalize with AI, let people deliver the foundations. 3️⃣ Closing the loop offline. Train staff to reference digital touchpoints IRL (“Saw your DM and your first purchase anniversary in the app - dessert’s on us”). As we pipe real-time purchase data into surprise-and-delight rewards, the secret sauce remains - AI and ML does the math, humans deliver the magic and create the loyalty.

  • View profile for Hiren Dhaduk

    I empower Engineering Leaders with Cloud, Gen AI, & Product Engineering.

    8,938 followers

    Any Fashion Marketplace with 900+ brands serving millions of customers will want to deliver personalized product recommendations. But a random tech won't help here. You need careful execution of Gen AI & cloud. The interesting thing is that a fashion company named ASOS has this solution incorporated into their app. Here's what they've done: - AI-Powered Shopping: ASOS integrated Microsoft Azure OpenAI Service and Azure AI prompt flow to build a conversational AI experience. Customers can now engage with AI on their website and app, making shopping feel more personalized and dynamic. - Personalized Product Discovery: With AI’s ability to understand customer preferences and trends, ASOS curates tailored product suggestions based on current fashion trends and individual style. It’s like having a personal shopping assistant. - Rapid Development: Using Azure’s prompt flow, ASOS was able to quickly prototype and test their AI experience, streamlining development while maintaining high standards of quality and security. - Ethical AI: ASOS prioritized responsible AI, ensuring the solution was free from biases and malicious prompts, offering customers a safe and brand-consistent experience. The result? A powerful AI-driven shopping experience that feels natural, authentic, and incredibly personalized. As AI continues to evolve, ASOS.com is a prime example of how retail can use generative AI to not just keep up with trends but set them. If you're looking to create a truly personalized shopping experience, AI is the way forward. P.S. What AI-driven features do you think will transform e-commerce next? #GenerativeAI #TechinFashion #MicrosoftAzure #Asos #Simform

  • View profile for Adnan Awow

    Visionary Data Science Exec | AI-Powered Recommender Systems | MLOps & Personalization Expert | Driving Digital Strategy, Loyalty, & $MM Impact for Fortune 50 Companies | Gamification & Offer Optimization

    4,596 followers

    I’m thrilled to share how our Data Science team at Target is using GenAI to make a real difference—for our guests and the business. You may have heard about this work from our leader Prat Vemana at SXSW on AI and retail. Here’s a closer look at what we’ve built and the results we’re seeing: Introducing GRAM (GenAI-powered Related Accessory Model): - Attribute Prioritization: Automatically ranks hundreds of features (color, material, brand) so you see the best add-ons first. - Aesthetic Matching: Learns “style harmony” to suggest visually cohesive pairings—think pillowcases that perfectly complement your sheets. - Massive Scale: Scores at the product-type level and parallelizes compute, handling hundreds of thousands of Home SKUs (and ready to expand). February ’25 A/B test results in our add-to-cart flyout: 🚀 11% lift in guest interactions 📈 12% boost in display-to-conversion 💰 9% growth in attributable demand Rolled out in April ’25, GRAM proves that smart AI + real business insights move the needle—driving engagement, conversion, and revenue. How is your team using GenAI to reshape personalization? Would love to hear your stories! #AI #MachineLearning #Personalization #GenAI #RetailTech  #MachineLearning Prat Vemana Brad Thompson Melissa Ludack Ranjeet Bhosale Darshan Nagaraja David Relyea Amit Pande Rankyung Hong Alina Nesen Longjiang Yang #RetailTech #AI #MachineLearning #Personalization #GenAI

Explore categories