Enhancing User Experience With AI-Driven Suggestions

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

Real-time personalization is killing your conversion rates. Everyone's obsessing over "hyper-personalized experiences." Dynamic content. AI recommendations. Real-time everything. But they're making a fatal mistake: They're optimizing for relevance while destroying speed. And speed ALWAYS wins. After auditing 300+ high-traffic sites, here's what I discovered... 🔍 The Personalization Paradox The Promise: 20-30% engagement lifts through real-time customization The Reality: Every second of load delay = 32% bounce rate increase Most sites are trading 15% conversion gains for 40% traffic losses. That's not optimization. That's self-sabotage. Here's the systematic approach that actually works... 🔍 The Zero-Latency Personalization Framework Layer 1: Predictive Preloading Stop reacting. Start predicting. → Chrome's Speculation Rules API: Prerenders likely pages → AI Navigation Prediction: 85% load time reduction → User Journey Mapping: Anticipate next actions Example: Amazon preloads product pages based on cart behavior. Result: Sub-second "personalized" experiences that feel instant. Layer 2: Edge-Side Intelligence Move computation closer to users: → CDN-Level Personalization at edge nodes → Sub-100ms response times globally The Math: Traditional: Server → Processing → Response (800ms) Edge-Optimized: Cache → Instant Delivery (50ms) Layer 3: Asynchronous Architecture Never block the main thread: Base page renders (0.8s) Personalization layers load (background) Content updates seamlessly User never sees delay 🔍 The Fatal Implementation Errors Error 1: JavaScript-Heavy Personalization Loading 500KB of scripts for 50KB of custom content. Error 2: Synchronous API Calls Blocking page render for recommendation queries. Error 3: Over-Personalization Customizing elements that don't impact conversion. Error 4: Ignoring Core Web Vitals Optimizing engagement while destroying SEO rankings. The Fix: Performance-first personalization architecture. 🔍 My Advanced Optimization Stack Data Layer: → IndexedDB for instant preference retrieval → Server-Sent Events for real-time updates → Intersection Observer for lazy personalization Delivery Layer: → Feature flags for gradual rollouts → Minified, bundled assets → Progressive image loading Results Across Portfolio: → Sub-2-second loads maintained → 25% retention improvements → 20% revenue lifts → 40% better SEO performance Because here's what most miss: Personalization without speed optimization isn't user experience. It's user punishment. The companies winning in 2025? They've cracked the code on invisible personalization. Users get exactly what they want, exactly when they want it. And they never realize the system is working. === 👉 What's your biggest challenge: delivering relevant content fast enough, or measuring the true impact of personalization on business metrics? ♻️ Kindly repost to share with your network

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

For years, companies have been leveraging artificial intelligence (AI) and machine learning to provide personalized customer experiences. One widespread use case is showing product recommendations based on previous data. But there's so much more potential in AI that we're just scratching the surface. One of the most important things for any company is anticipating each customer's needs and delivering predictive personalization. Understanding customer intent is critical to shaping predictive personalization strategies. This involves interpreting signals from customers’ current and past behaviors to infer what they are likely to need or do next, and then dynamically surfacing that through a platform of their choice. Here’s how: 1. Customer Journey Mapping: Understanding the various stages a customer goes through, from awareness to purchase and beyond. This helps in identifying key moments where personalization can have the most impact. This doesn't have to be an exercise on a whiteboard; in fact, I would counsel against that. Journey analytics software can get you there quickly and keep journeys "alive" in real time, changing dynamically as customer needs evolve. 2. Behavioral Analysis: Examining how customers interact with your brand, including what they click on, how long they spend on certain pages, and what they search for. You will need analytical resources here, and hopefully you have them on your team. If not, find them in your organization; my experience has been that they find this type of exercise interesting and will want to help. 3. Sentiment Analysis: Using natural language processing to understand customer sentiment expressed in feedback, reviews, social media, or even case notes. This provides insights into how customers feel about your brand or products. As in journey analytics, technology and analytical resources will be important here. 4. Predictive Analytics: Employing advanced analytics to forecast future customer behavior based on current data. This can involve machine learning models that evolve and improve over time. 5. Feedback Loops: Continuously incorporate customer signals (not just survey feedback) to refine and enhance personalization strategies. Set these up through your analytics team. Predictive personalization is not just about selling more; it’s about enhancing the customer experience by making interactions more relevant, timely, and personalized. This customer-led approach leads to increased revenue and reduced cost-to-serve. How is your organization thinking about personalization in 2024? DM me if you want to talk it through. #customerexperience #artificialintelligence #ai #personalization #technology #ceo

80% of people prefer to buy from brands that personalize. Yet most businesses still send generic campaigns. Here’s how I use AI to change that 👇 Step 1: Build Your Data Foundation → Consolidate customer data from all sources → Clean and structure your data → Create unified customer profiles → Map customer journeys Step 2: Choose the Right AI Tools → Start with predictive analytics → Add dynamic content generation → Implement real-time personalization engines → Focus on tools that integrate with your stack Step 3: Create Personalization Frameworks → Segment audiences by behavior → Design content templates → Set up trigger-based workflows → Define success metrics Real examples that work: 1/ E-commerce: → AI analyzes browsing patterns → Predicts next likely purchase → Personalizes email timing ↳ Result: 40% higher conversion rates 2/ B2B Marketing: → AI scores leads in real-time → Customizes content by industry → Automates follow-up timing ↳ Result: 3x faster sales cycles 3/ Content Marketing: → AI suggests trending topics → Personalizes content recommendations → Optimizes posting schedules ↳ Result: 2x engagement rates Warning: Avoid these common mistakes: → Implementing AI without clean data → Focusing on tools over strategy → Forgetting the human element → Ignoring privacy concerns Remember: AI amplifies your marketing. It doesn't replace your strategy. Start small, measure results, scale what works. What's your biggest challenge with marketing personalization? Comment below. Sign up for my newsletter for more marketing and AI content: https://lnkd.in/gSi-nA2F Repost or follow Carolyn Healey for more like this.