Leveraging Machine Learning for Personalization

Sharing key learnings and insights from our Real-Time (In-Session) Personalization journey at CARS24 — a capability that has transformed how we personalize the car buying experience at scale. Leveraging advanced sequence-based neural networks and real-time Kafka streaming infrastructure, we've developed a dynamic machine learning pipeline that processes more than a million user interactions daily. Our deep learning models rapidly adapt to user behaviour, delivering personalized car recommendations with sub-200ms latency. Highlights: ✅ Advanced sequence-based neural network architecture  ✅ Real-time streaming and processing of user behaviour signals with Kafka  ✅ Rapid feature engineering and inference using optimized real-time databases  ✅ High scalability for continuous model retraining and deployment Performance Impact: 📈 Across all discovery widget we achieved a highest Impression-to-View (I2V) rate and on the 'Best Matches' recommendation rail on our car detail page and buyer home page. 📈 Delivered a strong Impression-to-Booking Initiation (I2BI) conversion rate across different discovery widgets, underscoring high user relevance and engagement. Business Outcomes: 🚀 Significant uplift in user engagement  🚀 Marked reduction in user drop-offs  🚀 Enhanced personalization and superior user experience The attached flow chart outlines the architecture behind this AI-powered personalization pipeline — from real-time clickstream ingestion to ML inference and personalized recommendations. #RealTimePersonalization #AI #MachineLearning #DeepLearning #Kafka #DataScience #RecommendationEngine #TechInnovation #AI  #Personalization #pubsub #CARS24 #transformers #llm #genai

Building Agentic Graph Systems That Learn and Adapt to Each User 🛜 Graph-based systems represent a significant advancement in creating truly personalized and agentic AI systems by enabling sophisticated patterns of memory, recommendation, and contextual awareness to work together seamlessly. The integration of graph structures allows AI agents to maintain complex webs of relationships while actively learning and adapting to individual users' needs and preferences. First, graph structures provide a natural foundation for building memory systems that can evolve into sophisticated recommendation engines. The ability to traverse and weight relationships between entities enables systems to transform from passive storage into active agents that can anticipate needs and suggest relevant actions. This is particularly powerful because the graph structure captures not just individual pieces of information, but also their context, outcomes, and interrelationships. Second, graph-based systems excel at incorporating multi-dimensional pattern recognition. Unlike traditional recommendation systems that might focus on simple similarity metrics, graph structures can simultaneously process temporal patterns, contextual relationships, user behaviors, and outcome patterns. This multi-faceted analysis enables recommendations that are both more accurate and more nuanced than conventional approaches. Third, the adaptive learning capabilities of graph-based systems create a powerful feedback loop for personalization. When users respond to suggestions, their feedback modifies the weights of relevant connections in the graph. This creates a self-improving system where successful patterns naturally strengthen while less helpful ones fade. The adaptation works at both individual and aggregate levels, enabling systems to balance personalized learning with broader pattern recognition. Fourth, graph structures provide elegant solutions to common challenges in personalization systems, particularly the cold start problem. Even with limited initial information about a new user, the system can leverage indirect relationships and partial matches to make meaningful recommendations. As more interactions occur, these initial connections rapidly refine through feedback and pattern matching. Fifth, graph-based systems offer sophisticated privacy controls while maintaining high levels of personalization. This architectural approach enables highly personalized experiences while maintaining appropriate privacy protections. The integration of these capabilities has profound implications for AI system design. The graph structure serves as a unified framework where memory, learning, and recommendation capabilities can seamlessly interact. This enables increasingly sophisticated agents that can not only store and retrieve information but actively predict and suggest relevant knowledge and actions based on deep contextual understanding.

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? 

🍒 Unlocking Personalization with Conditional Average Treatment Effects (CATE)  🤔 What if we could move beyond the "one-size-fits-all" approach to decision-making? What if we could tailor treatments—like new features, pricing, or interventions—to the unique characteristics of each user or group?  🪩 Enter Conditional Average Treatment Effects (CATE), a game-changer for personalized decision-making.  👉 While the Average treatment Effect (ATE) indicates if a treatment is effective on average, CATE goes a step further. It addresses the question, "For whom does this treatment work best?" CATE allows us to estimate how treatment effects change among subgroups by conditioning on individual factors (e.g., user behavior, demographics, or preferences). 🟡 Why This Matters   Imagine you’re launching a new feature in your app. Rolling it out to everyone might not be the best strategy—some users might love it, while others could find it irrelevant or even frustrating. With CATE, you can identify which users are most likely to engage with the feature and tailor its rollout accordingly.  👉 Or think about dynamic pricing. Instead of setting the same price for everyone, CATE helps you determine the optimal price for each user segment, maximizing revenue while maintaining customer satisfaction.  🟡 The Limits of Prediction   Traditional machine learning focuses on predicting outcomes (e.g., “How much will usage increase if we add this feature?”). But prediction alone doesn’t tell us how the treatment influences the outcome. To personalize decisions, we need to estimate the slope of the treatment response function—how sensitive each individual is to the treatment.  👉 Here’s the catch: slopes are unobservable. We can’t see how a single user would respond to every possible version of a feature or price point. This brings us back to the fundamental problem of causal inference: we can never observe the same unit under different treatment conditions at the same time.  🟡 How CATE Helps   CATE allows us to estimate these unobservable slopes by leveraging heterogeneity. By grouping users based on their responsiveness to treatment, we can tailor decisions to maximize impact.  ❗If you want to dive deeper into how CATE works and how to implement it, check out the book Causal Inference in Python https://shorturl.at/X8hPo by Matheus Facure. It’s a fantastic resource for anyone looking to master causal inference techniques and apply them to real-world problems. #CausalInference #Personalization #CATE

As consumers seek more individual experiences and interactions, companies turn to #AI to deliver 𝙥𝙚𝙧𝙨𝙤𝙣𝙖𝙡𝙞𝙯𝙚𝙙 𝙥𝙧𝙤𝙢𝙤𝙩𝙞𝙤𝙣𝙨 𝙖𝙩 𝙨𝙘𝙖𝙡𝙚. For some time now, companies have been trying to address customer needs through #personalization, using data and analytics to craft more relevant consumer experiences. Using improved analytics models, brands and retailers can better provide valuable offers to micro-communities wherever they want to engage. Meanwhile, #genAI enables marketers to create tailored content that is relevant to those groups. According to McKinsey & Company, marketers should unlock personalization at scale, by upgrading five areas of their #martech stack and processes: 1. Data: by improving #data collection and analysis, marketers can gain deeper insights into customer behaviors and preferences. 2. Decisioning: to develop personalized promotions and content through more robust targeting, companies can also benefit from refreshing their #decision engines with new AI models. 3. Design: a sophisticated design layer that oversees offer management and #content production helps manage the process, fueling both operational excellence and agility. 4. Distribution: achieving true, real-time personalization requires a sophisticated #marketing architecture that delivers seamless and consistent messaging to the right audiences at the right time on the right channel. 5. Measurement: to validate the #ROI of personalization efforts, rigorous incrementality testing, standardized performance metrics, and measurement playbooks are essential. Are there other capabilities or technologies required for marketers to better target promotions and deliver individual content?

AI is no longer just an experimentation tool. It’s reshaping the entire optimization landscape. With this shift comes many untapped opportunities. Working with Andrius Jonaitis ⚙️, we've put together a growing list of 40+ AI-driven experimentation tools ( https://lnkd.in/gHm2CbDi) Combing through this list, here are the emerging market trends and opportunities you should know: 1️⃣ SELF-LEARNING, AUTO-OPTIMIZING EXPERIMENTS 💡 Opportunity: AI is creating self-adjusting experiments that optimize in real-time. 🛠️ Tools: Amplitude, Evolv Technology, and Dynamic Yield by Mastercard are pioneering always-on experimentation, where AI adjusts experiences dynamically based on live behavior. 🔮 How to leverage it: Focus on learning and developing tools that shift from static A/B testing to AI-powered, dynamically updating experiments. 2️⃣ AI-GENERATED VARIANTS 💡 Opportunity: AI can help you develop hypotheses and testing strategies. 🛠️ Tools: Ditto and ChatGPT (through custom GPTs) can help you generate robust testing strategies. 🔮 How to leverage it: Use custom GPTs to generate test ideas at scale. Automate hypothesis development, ideation, and test planning. 3️⃣ SMARTER EXPERIMENTATION WITH LESS TRAFFIC 💡 Opportunity: AI-driven traffic-efficient testing that gets results without massive sample sizes. 🛠️ Tools: Intelligems, CustomFit AI, and CRO Benchmark are pioneering AI-driven uplift modeling, finding winners faster -- with less traffic waste. 🔮 How to leverage it: Don't get stuck in a mentality that testing is only for enterprise organizations with tons of traffic. Try tools that let you test more and faster through real-time adaptive insights. 4️⃣ AI-POWERED PERSONALIZATION 💡 Opportunity: AI is creating a whole new set of experiences where every visitor will see the best-performing variant for them. 🛠️ Tools: Lift AI, Bind AI, and Coveo are some of the leaders using real-time behavioral signals to personalize experiences dynamically. 🔮 How to leverage it: Experiment with tools that match users with high-converting content. These tools are likely to develop and get even more powerful moving forward. 5️⃣ AI EXPERIMENTATION AGENTS 💡 Opportunity: AI-driven autonomous agents that can run, monitor, and optimize experiments without human intervention. 🛠️ Tools: Conversion AgentAI and BotDojo are early signals of AI taking over manual experimentation execution. Julius AI and Jurnii LTD AI are moving toward full AI-driven decision-making. 🔮 How to leverage it: Be open-minded about your role in the experimentation process. It's changing! Start experimenting with tools that enable AI-powered execution. 💸 In the future, the biggest winners won’t be the experimenters running the most tests, they’ll be the ones versed enough to let AI do the testing for them. How do you see AI changing your role as en experimenter? Share below: ⬇️

HALF of donors don’t return after their first gift. Why? Because most nonprofits treat donors like ATMs, not people. Here’s how AI-driven personalization can help: 1. AI analyzes donor behavior, preferences & giving patterns. No more generic “Dear Supporter” emails. 2. It segments donors intelligently. Big givers, monthly donors, one-time donors, each gets tailored messaging. 3. It automates personalized outreach at scale. Birthday emails. Impact reports aligned with donor interests. Smart thank-yous. And the results? Nonprofits using AI personalization see retention rates increase by up to 20%. Even better: Personalized emails deliver 6x higher transaction rates than non-personalized ones. What does that mean for you? More recurring donors. More predictable revenue. Less time spent chasing one-off gifts. With purpose and impact, Mario

For years, #associations have relied on segmentation to "personalize" content. You might have: 🔹 One email for early-career professionals  🔹 One for mid-career members  🔹 Another for executives But here’s the problem: People aren’t segments. What if an early-career member is deeply invested in advanced AI applications? What if an executive wants fundamental leadership training? Segment-based personalization is outdated—and it’s leaving engagement (and revenue) on the table. The future is true 1:1 personalization, and AI is making it possible. AI-powered recommendation engines can: ✅ Understand what each member actually consumes and engages with  ✅ Deliver personalized event, course, and content recommendations  ✅ Increase engagement rates by double or triple compared to generic outreach The results? -Open rates on AI-personalized emails exceeding 100% (yes, people open them multiple times!) -Click rates that outperform industry averages -Deeper engagement and real connections between members with shared interests We’re not talking about future possibilities—we’re talking about real results happening today. Associations that embrace AI-driven personalization will see skyrocketing engagement, retention, and revenue. Those that don’t? They’ll struggle to compete. Are you leveraging AI for real personalization yet? #AI #Personalization #MemberEngagement 

Email marketing remains the bedrock of B2B success, consistently delivering for customer retention and new lead generation. Now, a transformative force is emerging to amplify these results: AI. 57% of larger B2B companies are integrating AI into their email strategies, recognizing its potential to revolutionize their funnels. AI isn't just about automation; it's about creating intelligent, hyper-personalized experiences that resonate with your prospects and customers at every touchpoint: - Hyper-Personalization at Scale: AI delves deep into individual buyer data, understanding their unique needs and behaviors to deliver tailored content that converts. - Intelligent Retargeting: AI dynamically re-engages leads based on their specific interactions, ensuring your message is timely and highly relevant. - Precision Optimization: AI continuously analyzes and refines subject lines and send times, maximizing open rates and engagement with scientific accuracy. - Dynamic Content that Adapts: AI generates personalized content blocks on the fly, ensuring each recipient sees the information most pertinent to their journey. While the potential is immense, successful AI implementation requires thoughtful consideration: - Authenticity: AI-generated content risks sounding robotic if not carefully trained on your brand's unique voice and the subtleties of human interaction. - Human Touch: AI is a powerful tool, but strategic oversight, creative input, and ethical considerations remain firmly in the human domain. - Quality Data: AI algorithms thrive on accurate and comprehensive data. Investing in CRM hygiene and data enrichment is paramount for AI to deliver meaningful insights and personalized experiences. Elevating Your Funnel: To move beyond simply adopting AI and truly leverage its transformative power, consider these strategic imperatives: - Pinpoint Your Bottlenecks: Identify the real friction points in your lead journey and CRM processes. Where are leads dropping off? Where is personalization falling flat? - Experiment with Purpose: Initiate focused pilot programs targeting those key bottlenecks. This allows for measurable learning and minimizes risk. Think: "Can AI improve our initial lead qualification response time?" or "Can AI personalize content to boost engagement in our nurture sequence?" - Fuel the Machine with Intelligence: AI is only as good as the data it consumes. Invest in data hygiene to ensure your CRM provides a rich, accurate foundation. - Infuse Your Brand DNA: Actively train your AI models on your brand voice, values, and target audience nuances. - Orchestrate with Human Expertise: AI should empower, not replace. Integrate human review & oversight into your workflows to ensure quality, ethical considerations, & brand alignment. - Build the Right Foundation: Recognize that successful AI implementation requires the right skills and support. Invest in marketing operations expertise..

The growing integration of AI in user experiences raises questions about balancing efficiency with emotional depth—while automation enhances personalization, the challenge lies in ensuring interactions feel genuinely human rather than scripted responses driven by algorithms. Emotion AI leverages machine learning and biometric data to analyze human emotions through facial expressions, voice tones, and behavioral patterns. Ai enhances digital interactions by adapting content and responses based on user sentiment, making experiences more engaging. Businesses apply it in chatbots that recognize frustration and adjust their replies, e-commerce platforms that suggest products based on mood, and marketing strategies that refine messaging for higher engagement. However, ethical concerns regarding privacy and consent emerge, as collecting emotional data requires strict transparency. A responsible approach involves securing user data, ensuring ethical AI practices, and continuously refining models to avoid biases and misinterpretations. #EmotionAI #ArtificialIntelligence #AIEthics #UXdesign #DigitalTransformation