AI-Driven Personalization In E-Commerce

Data privacy and ethics must be a part of data strategies to set up for AI. Alignment and transparency are the most effective solutions. Both must be part of product design from day 1. Myths: Customers won’t share data if we’re transparent about how we gather it, and aligning with customer intent means less revenue. Instacart customers search for milk and see an ad for milk. Ads are more effective when they are closer to a customer’s intent to buy. Instacart charges more, so the app isn’t flooded with ads. SAP added a data gathering opt-in clause to its contracts. Over 25,000 customers opted in. The anonymized data trained models that improved the platform’s features. Customers benefit, and SAP attracts new customers with AI-supported features. I’ve seen the benefits first-hand working on data and AI products. I use a recruiting app project as an example in my courses. We gathered data about the resumes recruiters selected for phone interviews and those they rejected. Rerunning the matching after 5 select/reject examples made immediate improvements to the candidate ranking results. They asked for more transparency into the terms used for matching, and we showed them everything. We introduced the ability to reject terms or add their own. The 2nd pass matches improved dramatically. We got training data to make the models better out of the box, and they were able to find high-quality candidates faster. Alignment and transparency are core tenets of data strategy and are the foundations of an ethical AI strategy. #DataStrategy #AIStrategy #DataScience #Ethics #DataEngineering

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

🍽️ AI isn't just changing how we work—it's revolutionizing how we eat! At Wellness Coach, we're fascinated by how artificial intelligence is creating a nutrition revolution right before our eyes. Imagine snapping a photo of your lunch and instantly knowing its exact nutritional breakdown. Or receiving meal suggestions perfectly tailored to your unique gut microbiome and metabolism. This isn't science fiction—it's happening now. AI systems can predict your blood sugar response to different foods without invasive testing, making smart eating accessible to everyone, not just those managing health conditions. What excites me most is how these technologies democratize nutrition knowledge. The expertise once limited to dietitians and nutritionists is becoming available through your smartphone. Food industry leaders are also using AI to predict ingredient shortages and maintain quality, ensuring healthier options remain on shelves consistently. The future of wellness isn't just about counting steps—it's about understanding exactly what your body needs to thrive. How do you think AI will change your relationship with food in the coming year? I'd love to hear your thoughts! #AIinWellness #NutritionTech #HealthInnovation #WellnessCoach

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

How do we balance AI personalization with the privacy fundamental of data minimization? Data minimization is a hallmark of privacy, we should collect only what is absolutely necessary and discard it as soon as possible. However, the goal of creating the most powerful, personalized AI experience seems fundamentally at odds with this principle. Why? Because personalization thrives on data. The more an AI knows about your preferences, habits, and even your unique writing style, the more it can tailor its responses and solutions to your specific needs. Imagine an AI assistant that knows not just what tasks you do at work, but how you like your coffee, what music you listen to on the commute, and what content you consume to stay informed. This level of personalization would really please the user. But achieving this means AI systems would need to collect and analyze vast amounts of personal data, potentially compromising user privacy and contradicting the fundamental of data minimization. I have to admit even as a privacy evangelist, I like personalization. I love that my car tries to guess where I am going when I click on navigation and it's 3 choices are usually right. For those playing at home, I live a boring life, it's 3 choices are usually, My son's school, Our Church, or the soccer field where my son plays. So how do we solve this conflict? AI personalization isn't going anywhere, so how do we maintain privacy? Here are some thoughts: 1) Federated Learning: Instead of storing data in centralized servers, federated learning trains AI algorithms locally on your device. This approach allows AI to learn from user data without the data ever leaving your device, thus aligning more closely with data minimization principles. 2) Differential Privacy: By adding statistical noise to user data, differential privacy ensures that individual data points cannot be identified, even while still contributing to the accuracy of AI models. While this might limit some level of personalization, it offers a compromise that enhances user trust. 3) On-Device Processing: AI could be built to process and store personalized data directly on user devices rather than cloud servers. This ensures that data is retained by the user and not a third party. 4) User-Controlled Data Sharing: Implementing systems where users have more granular control over what data they share and when can give people a stronger sense of security without diluting the AI's effectiveness. Imagine toggling data preferences as easily as you would app permissions. But, most importantly, don't forget about Transparency! Clearly communicate with your users and obtain consent when needed. So how do y'all think we can strike this proper balance?

🧭Governing AI Ethics with ISO42001🧭 Many organizations treat AI ethics as a branding exercise, a list of principles with no operational enforcement. As Reid Blackman, Ph.D. argues in "Ethical Machines", without governance structures, ethical commitments are empty promises. For those who prefer to create something different, #ISO42001 provides a practical framework to ensure AI ethics is embedded in real-world decision-making. ➡️Building Ethical AI with ISO42001 1. Define AI Ethics as a Business Priority ISO42001 requires organizations to formalize AI governance (Clause 5.2). This means: 🔸Establishing an AI policy linked to business strategy and compliance. 🔸Assigning clear leadership roles for AI oversight (Clause A.3.2). 🔸Aligning AI governance with existing security and risk frameworks (Clause A.2.3). 👉Without defined governance structures, AI ethics remains a concept, not a practice. 2. Conduct AI Risk & Impact Assessments Ethical failures often stem from hidden risks: bias in training data, misaligned incentives, unintended consequences. ISO42001 mandates: 🔸AI Risk Assessments (#ISO23894, Clause 6.1.2): Identifying bias, drift, and security vulnerabilities. 🔸AI Impact Assessments (#ISO42005, Clause 6.1.4): Evaluating AI’s societal impact before deployment. 👉Ignoring these assessments leaves your organization reacting to ethical failures instead of preventing them. 3. Integrate Ethics Throughout the AI Lifecycle ISO42001 embeds ethics at every stage of AI development: 🔸Design: Define fairness, security, and explainability objectives (Clause A.6.1.2). 🔸Development: Apply bias mitigation and explainability tools (Clause A.7.4). 🔸Deployment: Establish oversight, audit trails, and human intervention mechanisms (Clause A.9.2). 👉Ethical AI is not a last-minute check, it must be integrated/operationalized from the start. 4. Enforce AI Accountability & Human Oversight AI failures occur when accountability is unclear. ISO42001 requires: 🔸Defined responsibility for AI decisions (Clause A.9.2). 🔸Incident response plans for AI failures (Clause A.10.4). 🔸Audit trails to ensure AI transparency (Clause A.5.5). 👉Your governance must answer: Who monitors bias? Who approves AI decisions? Without clear accountability, ethical risks will become systemic failures. 5. Continuously Audit & Improve AI Ethics Governance AI risks evolve. Static governance models fail. ISO42001 mandates: 🔸Internal AI audits to evaluate compliance (Clause 9.2). 🔸Management reviews to refine governance practices (Clause 10.1). 👉AI ethics isn’t a magic bullet, but a continuous process of risk assessment, policy updates, and oversight. ➡️ AI Ethics Requires Real Governance AI ethics only works if it’s enforceable. Use ISO42001 to: ✅Turn ethical principles into actionable governance. ✅Proactively assess AI risks instead of reacting to failures. ✅Ensure AI decisions are explainable, accountable, and human-centered.

I once convinced a client making $200M+ annually to remove their AI-powered product recommendation engine. They thought I'd lost my mind. Their marketing team had spent months implementing dynamic content that changed based on visitor behavior. Real-time personalization that was supposed to "boost conversions by 15%." Instead, it was creating decision paralysis. When we tested their "smart" homepage against a simplified version... the simplified version converted 40% better. The personalization was creating cognitive overload. Too many choices. Visitors couldn't focus on what mattered. But there's actually a deeper issue brewing now, with AI. Recent research shows 71% of consumers want AI disclosure when sites are being personalized. They're getting creeped out by how much websites "know" about them (yeah, me too!). Meanwhile, companies are doubling down on hyper-personalization because the technology exists. This creates what I call the "Personalization Privacy Paradox": ↳ The more we optimize for individual preferences, the more we erode trust In the end the client kept personalization for logged-in users who opted in. And they made their default experience elegantly simple: ↳ Clear value proposition ↳ Obvious next steps ↳ No algorithmic guesswork personalization Sometimes the best personalization is knowing when NOT to personalize.

This week marks a major milestone for AI’s ability to improve human health and wellness. Here’s a sneak peek at SmartCart™ 🎉 Food is the foundation of health, and since 2019, Hungryroot has been at the forefront of AI innovation in the food industry. Our original algorithm, known internally as "box fill logic” (or BFL), leveraged an operations research algorithm to automatically fill customers' grocery carts based on their preferences. While two thirds of what our customers bought was chosen by BFL — meaning it underpinned our entire value proposition — it was limited in its ability to dynamically scale and evolve over time. About a year ago, we started developing a new system entirely from scratch by our incredibly talented and experienced in-house team of data scientists, ML and OR engineers. The result is SmartCart™, a first-of-its-kind AI system. It’s comprised of ten machine learning models that integrate into an operations research algorithm and analyzes millions of data points to recommend groceries, recipes, and supplements to help you and your family live healthier, more joyful lives. It’s like having your own personal assistant for healthy living. Each model serves a unique and specific purpose. For example, one of the models, which is built using an approach similar to ChatGPT, analyzes your recent orders, cross-references data from other users, and applies machine learning to suggest the ideal characteristics of your next order — low sodium, quick-to-prepare meals, or high protein, on-the-go snacks, for example. Given the factors that matter most vary by person and evolve over time, there’s a model that applies a weight to each of the other nine models, all designed to optimize customer satisfaction. This flexible, customer-centric approach replaces the rigidity of traditional rule-based systems and is cutting-edge in the field of AI. (Hungryroot has already been granted several patents on the system.) SmartCart™ is already showing results. Customers who use SmartCart™ order twice as often on Hungryroot as customers who shop on their own, demonstrating its ability to help people achieve their health objectives, save time, and discover new foods that bring them joy. 90% of SmartCart™ customers report progress in their health goals since joining the service, and on average, they save 3 hours each week on meal planning, shopping, and cooking. They also discover 3 times more food than shopping on their own. SmartCart™ is a paradigm shift that will require multiple iterations, but I’m so incredibly proud of the team, and I can’t wait to see what our customers think.

Google Just Killed the Blue Links. What Hotel Teams Must Do to Stay Visible and Booked KEY TAKEAWAYS FROM GOOGLE I/O 2025 FOR TRAVEL: ➡️ Search is being reinvented. AI Mode now delivers single, synthesized responses instead of traditional link lists. This transforms how travelers get information. ➡️ Context is everything. Deep Search brings personalization by integrating data from Gmail, Docs, and more. It creates hyper-relevant travel recommendations. ➡️ Agentic AI is coming. Google’s AI will soon complete bookings on behalf of users without ever leaving search. ➡️ Content doesn’t get clicked. It gets chosen. Visibility now hinges on content being authoritative and structured enough for AI to use. ➡️ The booking journey is compressed. Travelers will go from inspiration to transaction in a single interface, often skipping websites entirely. WHAT THIS MEANS FOR HOTEL COMMERCIAL TEAMS: It’s not just search that’s changing. It’s the traveler’s behavior, expectations, and booking path. The direct channel is under pressure, not from OTAs this time, but from AI interfaces that are becoming the new gatekeepers. Google’s AI Mode is the clearest signal yet. Your property needs to be seen by AI to be chosen by travelers. This shift doesn’t just demand better marketing. It demands a new commercial mindset that understands AI, leverages its strengths, and trains teams to operate differently. AI ISN’T THE THREAT. AI ILLITERACY IS. Your visibility, your bookings, and your competitive edge will depend on how fast your team adapts to an AI-first world. The question isn't whether you should adopt AI. It's whether your team is equipped to think with it. FIVE ACTION STEPS FOR HOTEL TEAMS: 1️⃣ Audit your content for AI-readiness. Ensure your direct booking site and listings are structured, factual, and authoritative. AI doesn’t quote fluff. It pulls from clarity. 2️⃣ Upskill your commercial team in AI literacy. Teams must understand how AI systems surface content, evaluate trust, and make decisions. Train now or be filtered out. 3️⃣ Make your direct channel AI-friendly. Use schema markup, FAQs, and clear CTAs to help AI engines digest your offers and booking pathways directly. 4️⃣ Deploy AI tools in your operation. From AI voice agents to vibe marketing systems, use automation to deliver faster, smarter, and more personalized guest journeys across all touchpoints. Build for the whole journey, not just the click. 5️⃣ From inspiration to checkout, ensure your brand story and booking engine are aligned and optimized for seamless conversion. This applies whether users stay on your site or interact with your content through AI Mode. FINAL WORD: The blue links are fading. The AI window is opening. Only the brands that train, adapt, and innovate will earn visibility in the next phase of travel.