AI-Driven Personalization Techniques

Memory & personalization might be the real moat for AI we’ve been looking for. But where that moat forms is still up for grabs: •App level •Model level •OS level •Enterprise level Each has very different dynamics. 🧵 ⸻ 1. App-level personalization Apps build their own memory & context for users. Examples: •Harvey remembering firm-specific legal knowledge for law firms •Abridge capturing patient conversations & generating notes for doctors •Perplexity building long-term search profiles for individual users ➡️ Most likely in vertical applications with focused use cases and domain-specific data. This is where Eniac Ventures is currently doing most of our investing ⸻ 2. Model-level personalization The model itself becomes personalized and portable across apps. Examples: •ChatGPT memory & custom instructions •Meta’s LLaMa fine-tuned on personal embeddings ➡️ Most likely in general-purpose assistants and broad horizontal use cases where user context needs to travel across apps. ⸻ 3. OS-level personalization Personalization happens at the OS level, shared across apps & devices. Examples: •Google Gemini native to Android •Apple (maybe) embedding Claude via Anthropic ➡️ Most likely in consumer devices and mobile ecosystems where platforms control distribution. ⸻ 4. Enterprise-level personalization Each enterprise owns and controls its own personalization layer for employees & customers. Examples: •Microsoft Copilot trained on company data •OSS models (LLaMa, Mistral) deployed on private infra with platforms like TrueFoundry •OpenAI GPTs fine-tuned & hosted in secure enterprise environments ➡️ Most likely in highly regulated industries (healthcare, financial services) where data privacy and compliance are critical. ⸻ Why it matters: Where memory & personalization “land” may define who captures AI value. Different layers may win in different sectors. Where AI memory lives may reshape who captures the next decade of value.

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

Stop getting it wrong with #AI: So, here’s the thing, if your idea of AI personalization is slapping a customers name on a promotional email or serving up “Customers who bought this also bought…” pop-ups, then congratulations—you’re stuck in 2012. I came across a solid research published in #HBR by #BCG on how leaders and laggers in various industries are applying AI. Yes consultants have a habit of putting things into frameworks and metrics, but this one was good BCG. Breaking some #myths on #Personalization and #AI : • 🧟Myth 1: AI is just for automation. No, it’s not. AI is for making people feel like you get them. #Netflix doesn’t just automate recommendations—it fine-tunes them to your weirdly specific taste for crime dramas, maybe some dark content with a hint of comedy. That’s connection. • 🧟Myth 2: Personalization = profits. In reality, loyalty and trust bring growth and add to profits. #Starbucks tailors offers through its Rewards app, focusing on loyalty first—and the profits follow. • 🧟Myth 3: Data hoarding equals success. Spoiler alert: it doesn’t. Collecting data without actionable insights is like hoarding junk. #Amazon, on the other hand, uses its data so well that 35% of its revenue comes from its AI-powered recommendation engine. It integrates browsing habits, past purchases, and customer reviews to suggest items that resonate. To quantify personalization maturity index multiply the below metrics: 1️⃣Empower Me(50%) Personalization starts with solving real problems not just offering flashy features. Example #Alibaba’s AI-driven tools empower small businesses by providing tailored logistics and financing solutions. 2️⃣Know Me(10%) Understanding your customer is essential. #Sephora’s AI-driven app uses purchase history and skin tone matching to suggest relevant products. 3️⃣Reach Me(10%) Timing and channels make or break personalization. #Uber’s predictive AI sends ride prompts exactly when users are most likely to need a car ride. Contrast this with brands that bombard customers with irrelevant offers, eroding trust. 4️⃣Show Me(10%) Visual and contextual relevance elevate personalization. #Sephora’s virtual try-ons demonstrate how personalized content enhances decision-making. Companies that rely on generic or mismatched ads lose credibility & engagement. 5️⃣Delight Me(10%) Creating unexpected moments of joy: #Spotify’s “discover weekly” doesn’t just predict your mood but it surprises and delights customer with a 56% engagement rate to prove it. 6️⃣Remaining 10% score weightage is attributed to CXOs championing AI projects Companies that treat AI-powered personalization as a strategic imperative, rather than a cost-cutting tool, stand to gain the most. Leaders like Netflix, Uber, Amazon, Starbucks, Spotify, Alibaba Group and SEPHORA dominate the Personalization Maturity Index because they’re masters of combining AI with human-centric strategies. Meanwhile, laggers just don’t know how to turn data into meaningful actions.

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?

When AI personalization work the most effectively? One of the most effective strategies to personalize your AI output is to train AI on your own information. Imagine this as letting AI absorb your thoughts and speech patterns. Our agency employs this strategy with our clients and ourselves. We record everything we do within our organization and with our clients. -- Every week, we have meetings with our co-founders, Sean, Edgars, and myself. → These meetings are recorded → We generate content from these recordings. -- Besides, we have weekly and bi-weekly content interview calls with clients. → During these calls, we gather knowledge and experiences from the clients. → They give us their insights for about 30 minutes → We use their responses to train the AI. -- In addition, we have a Telegram bot for our clients. → Clients can record answers to different questions from our content calendars. → We use these responses to further train AI. -- This method fills a gap that usually exists when you only use a simple prompt. It allows the AI to understand people's thoughts, feelings, language style, experiences, and plans. The role of AI is to make these thoughts readable and well-formatted. All the knowledge comes from people. So, if you can feed the AI enough of your own or someone else's information, that's the best way to personalize AI. Do you agree with this strategy? ♻️ If this post resonated with you, consider sharing and following for more insights!

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 

At Amazon Web Services (AWS) Generative AI Innovation Center (#GenAIIC), we're seeing organizations evolve beyond basic AI implementations toward sophisticated foundation model (FM) customization strategies. FM customization options range from lightweight approaches like supervised fine-tuning to ground-up model development. We typically advise starting with lighter-weight solutions that require smaller amounts of data and compute, then progressing to more complex techniques only when specific use cases or remaining gaps justify the investment. Here's a summary of the techniques we leverage to maximizing model performance and ultimately maximize business outcomes through targeted optimization: 🔵Prompt Engineering: Zero-shot and few-shot learning techniques for immediate adaptability 🔵RAG (Retrieval Augmented Generation): Enhancing LLM outputs with private data sources while maintaining data governance 🔵Fine-tuning & Distillation: Optimizing model weights for domain-specific tasks while reducing computational overhead 🔵Reinforcement Learning: Implementing RLHF (Reinforcement Learning with Human Feedback) for improved model alignment 🔵Foundation Model Development: Custom architecture selection and pre-training for unique use cases Working with customers like Cosine AI, Volkswagen, and Synthesia has shown that successful implementations often combine multiple approaches, backed by robust evaluation metrics and hardware-level optimizations for both training and inference. Dive deeper into our top tips and technical implementation strategies: https://lnkd.in/eyDHxwiY Hannah Marlowe, PhD Rohit Thekkanal Sharlina Keshava Alexandra Fedorova Caleb Wilkinson Taimur Rashid Amanda Cohen Randi Larson Jackie Vendetti #AWS #GenerativeAI

After months of collaboration with some of the smartest people in advertising, I'm excited to share the IAB AI Personalization Playbook, a practical guide for scaling AI-powered creative personalization. 👉 Here's the thing: many organizations are running pilots. Few are actually scaling those pilots to full enterprise personalization. This playbook is our answer to that gap, a practical, cross-functional guide for moving beyond experiments to repeatable, responsible AI-powered personalization. It's built around three core principles: 1️⃣ Human-centered AI – Because humans still need to be in the driver's seat 2️⃣ Cross-functional integration – Marketing, creative, ops, legal, and data all need to be at the table internally and externally, media and creative teams need to be tightly orchestrated 3️⃣ Risk-tiered governance – Not all content needs the same level of oversight We worked with platforms, brands, agencies, and publishers to build something that's practical and usable, versus simply aspirational. The framework covers everything from strategic briefing to scaled production to measuring what matters. It's designed to help companies figure out where AI fits in your workflow, how to maintain brand integrity at scale, and what good governance actually looks like in practice. Huge thanks to everyone who contributed feedback, challenged assumptions, and helped make this better than any one organization could have built alone. Specifically, Adam Buhler, Executive Vice President, Creative Technology Digitas North America, Adwait Walimbe, Digital Transformation Advisor Adobe, Brian Hull, Head, Global Creative Labs The Weather Company, Graham Wilkinson, EVP, Chief Innovation Officer, and Global Head of AI Acxiom, Kelly O'Brien, Senior Product Manager, NPR, Paul Longo, General Manager, AI in Ads Microsoft, Todd Hassenfelt, EGlobal Digital Commerce Sr. Director, Strategy & Execution Colgate-Palmolive and all the internal teams at IAB. ⭐️ 👉 IAB AI Personalization Playbook: https://lnkd.in/dDqU7p3G #ai #personalization #creative #roi

Your marketing playbook just expired. AI has rewritten every rule while most brands are still playing by 2019 strategies. The companies adapting fastest aren't the ones with bigger budgets or better tech teams. They're the ones who understand how AI has fundamentally changed customer behaviour. Here's what the winners are doing differently: 1. The New Search Landscape: SEO meets LLM Traditional keywords are the past. Conversational queries are everything. Example: REI shifted from keyword-stuffed descriptions to contextual content addressing specific use cases, increasing AI-summarised results visibility by 47%. Reality check: Google's AI Overviews now appear in nearly half of all search results. 2. AI Assistants as Gatekeepers Your brand must be recognised by AI as a category leader to enter consideration sets. Example: Best Buy organised product attributes to match natural customer questions, achieving 35% increase in organic traffic from voice searches. The shift: AI now filters options before consumers see them. 3. Attention Compression Consumer attention spans shrink as AI summarises everything instantly. Action point: Front-load your value proposition in all communications. The pattern: Customers want to digest information about products quickly, not hunt to understand what’s in it for them. 4. Hyper-Personalisation Without Creepiness AI enables true 1:1 marketing at scale, but only if you balance customisation with transparency. Example: Sephora's Skin IQ tool provides personalised skincare recommendations, driving 35% growth in skincare sales. The principle: Use preference-based content sequencing with full transparency about data usage. 5. Multi-Modal Content Strategy AI-driven consumers expect seamless experiences across text, voice, and visual channels. Example: Domino's "AnyWare" approach allows ordering through voice assistants, text, social media, and apps. The requirement: Build centralised content hubs ensuring consistent messaging across all channels. 6. The Human Advantage As AI handles transactions, authentic human connection becomes your competitive edge. Example: Lululemon's in-store community events resulted in 25% higher repeat purchase rates compared to online-only shoppers. The opportunity: Community-building programs generate 23% higher customer lifetime value. The brands that thrive won't be those with the most sophisticated AI tools. They'll be the ones that use AI to enhance human connection rather than replace it. Which of these shifts will you implement first? ♻️ Found this helpful? Repost to share with your network.  ⚡ Want more content like this? Hit follow Maya Moufarek.