Proactive Customer Experience Management

🧑🏽 Designing Better Personalization UX. With guidelines on how to better tailor content and features to user’s needs and interests. ✅ Customization allows users to choose exactly what they want. ✅ Personalization anticipates what they want behind the scenes. ✅ We personalize to match specific needs without user’s effort. ✅ We allow users to customize preferences, filters, layout, data. 🤔 But often only very few people customize their experience. 🚫 Past behavior doesn’t always predict future actions. 🤔 Users often have different needs at different times. ✅ Design a wide range of presets, templates and defaults. ✅ Track frequent actions and errors, and suggest shortcuts. ✅ Always add content, or reshuffle it, rather than removing it. ✅ Expose users to non-matching topics to avoid filter bubbles. 🤔 Often users don’t know what they need, or what they’d like. ✅ Good personalization is deeply embedded in a user journey. ✅ Search for moments when you want to win user’s attention. ✅ Ask users explicitly about their intent to learn their context. ✅ Let users override personalization if it goes against their needs. ✅ When journey breaks, don’t stitch it, but tie a beautiful bow. We can’t personalize without research. Collect reliable data about users first. Then segment users into groups with shared needs. Decide what messages you have for each group. And define a user model, content model and metadata that go along with it. Then decide on individual or role-based personalization. Choose touchpoints where personalized UX will be served. Apply the logic across your channels, but give users full control of their data. In that process, define how the team will test and measure the impact of personalization over time. Such a project might often feel like a huge leap of faith without immediate benefits. But if done well, it can increase customer lifetime value significantly — but you will need short-term victories to get a long-term commitment. So start slowly. Run experiments. Personalize where you can make the highest impact. More often than not, the outcome will be worth the effort — even although most users will never even notice it, they might stay for many years to come. ✤ Useful resources Personalization: A Practical UX Guide, by Taras Bakusevych https://lnkd.in/e8v6WF9U Five Levels Of Recommendations, by Guillaume Galante https://lnkd.in/eKqsZtJ5 Definitive Guide To Personalization (free eBook, PDF) https://lnkd.in/eCA_a5Xh Personalization Pyramid, by Colin A. Eagan M.S., Jeffrey MacIntyre https://lnkd.in/eaztWU8e ✤ Books – The Person in Personalisation, by David Mannheim – Hello {first name}, by Rasmus Houlind 🎀 – The Personalization Paradox, by Val Swisher, Regina Lynn Preciado – Personalization Mechanics, by John Berndt #ux #design

Smart CRM Basics Predictive Customer Behavior Modeling The Advantages of Predictive Behavior Modeling When Marketers can target specific customers with a specific marketing action – you are likely to have the most desirable campaign impact. Every marketing campaign and retention tactic will be more successful. The ROI of upsell, cross-sell, and retention campaigns will be more significant. For example, imagine being able to predict which customers will churn and the particular marketing actions that will cause them to remain long-term customers. Customers will feel the greater relevance of the company’s communications with them – resulting in greater satisfaction, brand loyalty, and word-of-mouth referrals. Enhancing Customer Segmentation for Personalization Predictive analytics refines customer segmentation by identifying patterns within data. By understanding customer segments on a deeper level, businesses can personalize their interactions, marketing messages, and product recommendations. This tailored approach fosters a stronger connection with customers, leading to increased loyalty. Anticipating Customer Needs Through Lead Scoring Lead scoring becomes more accurate with the integration of predictive analytics. By evaluating customer data, such as interactions with emails, website visits, and social media engagement, businesses can prioritize leads based on their likelihood to convert. This ensures that sales teams focus their efforts on leads with the highest potential. Optimizing Sales Forecasting Accurate sales forecasting is crucial for effective resource allocation and business planning. Predictive analytics in CRM analyzes past sales data, market trends, and customer behaviors to generate more accurate sales forecasts. This empowers businesses to make informed decisions, allocate resources efficiently, and capitalize on emerging opportunities. Transforming CRM with Predictive Analytics Predictive analytics is revolutionizing CRM by providing invaluable insights into customer behaviors. From personalized marketing campaigns to proactive churn prevention, businesses can leverage these predictions to enhance customer relationships and drive growth. As technology continues to advance, integrating predictive analytics into CRM systems is not just a strategy for staying competitive; it's a key component in building lasting customer-centric businesses in the digital age. #PredictiveAnalytics #CRMInsights #CustomerBehavior #DataDrivenDecisions #BusinessIntelligence #CustomerRetention #SalesForecasting #MarketingStrategy #EthicalCRM #DynamicPricing

It surprises me how many e-commerce brands pretend to offer a personalized storefront, but show the same store to everyone. The attached visual that shows what a modern storefront actually looks like behind the scenes, which is a simple system that reacts in real time. Thought it would be useful to break this down into three stages with the recommended tech stack below: Stage 1: Signals (data in) You capture (live) what’s already happening the moment someone arrives. How they got there, what they’re doing, what device they’re on, and whether they’ve bought before. Typical stack: • Segment or RudderStack for event capture • Shopify events and customer data • Google Tag Manager • Meta / TikTok UTMs for paid context Focus on clean, real-time signals without overengineering identity. Stage 2: Decisions (what to show) Those signals get turned into a simple decision immediately. Which message, which products, which path makes sense for this visitor right now. If it’s not fast enough to change the first screen, it doesn’t count. Typical stack: • Dynamic Yield or Nosto • Vercel edge logic • Cloudflare Workers • Simple rules or light models, not heavy AI Remember, speed beats sophistication. Stage 3: Experience (what changes) The storefront responds on arrival. The hero, first product grid, and primary CTA change instantly so the site feels relevant from the first moment. Typical stack: • Shopify Hydrogen or native Shopify sections • Contentful or Optimizely • Server-side or edge-rendered changes, not client-side flicker Important, personalize above the fold first. A returning high-value customer sees new arrivals and a faster path to checkout. A first-time visitor from paid sees a clearer offer and fewer choices. A deal-driven shopper sees bundles and savings upfront. Everything else comes later. If you want to start without overengineering: • Pick the two audiences that matter most • Personalize only the hero and first product grid • Measure lift on conversion rate and revenue per session • Add complexity only after this works Start simple: focus on one working example that proves the storefront can adapt in real time in a way customers actually feel.

So many companies are still stuck in “data rich, insight poor” mode. The reality is there is no shortage of data at any company. Now, it's also important to note that data doesn’t guarantee insight. So how do we get from data to insight? Data often lives in silos, whether that's in your CRM, support tickets, survey platforms, chat transcripts, etc. It also likely sits behind legacy systems. Accessibility means you'll need an integrated data architecture: a unified semantic layer, consistent schemas, and real-time pipelines driven by event streaming. You will also need data governance: clear ownership, stewardship, lineage, and quality checks. If you're using AI models to surface insights without architecture and governance, you'll just surface noise instead of true patterns. Formatting and context also matter. Raw logs and PDFs aren’t analytics-ready. You need ETL/ELT processes to transform unstructured feedback (text, voice) into tokenized, enriched datasets. Metadata like timestamps, customer segments, and interaction channels gives structure to AI training. Plus, you have to manage model drift, retraining schedules, and data versioning so insights stay accurate as customer behavior evolves. Finally, it should be no surprise that people and processes are as important as platforms. So your CX team should ultimately need: 1. Data architects design pipelines, select storage technologies and enforce governance 2. Data engineers and MLOps specialists to build, deploy and monitor feature stores and models 3. Analytics translators (CX analysts) who map business questions into technical requirements 4. UX researchers and change leaders to integrate AI-driven recommendations into frontline workflows This convergence defines the CX-as-Engineer archetype. It blends deep knowledge of customer and employee journeys with hands-on technical capability. The CX-as-Engineer archetype builds end-to-end workflows: from raw event data through AI-powered root-cause detection to automated orchestration engines that trigger proactive interventions. It's pretty clear that, today, speed and precision can determine leadership. So having this hybrid role can move your organization from “insight poor” to predictive CX and EX. It will be a key marker of your team's and company's evolution and commitment to the customer. If your team is still focused only on dashboards, even if "AI" is built into the platform, it’s time for you to ask yourself: are we using AI to explain what happened or to prevent it from happening again? #customerexperience #employeeexperience #cxasengineer #ai

People value what they create 63% more. Yet most digital experiences treat customers as passive recipients instead of co-creators. This psychological principle, known as the "Ikea Effect", is shockingly underutilized in digital journeys. When someone builds a piece of Ikea furniture, they develop an emotional attachment that transcends its objective value. The same phenomenon happens in digital experiences. After optimizing digital journeys for companies like Adobe and Nike for over a decade, I've discovered this pattern consistently: 👉 Those who customize or personalize a product before purchase are dramatically more likely to convert and remain loyal. One enterprise client implemented a product configurator that increased conversions by 31% and reduced returns by 24%. Users weren't getting a different product... they were getting the same product they helped create. The psychology is simple but powerful: ↳ Customization creates psychological ownership before financial ownership ↳ The effort invested creates value attribution ↳ Co-creation builds emotional connection Three ways to implement this today: 1️⃣ Replace dropdown options with visual configurators 2️⃣ Create personalization quizzes that guide product selection 3️⃣ Allow users to save and revisit their customized selections Most importantly: shift your mindset from selling products to facilitating creation. When customers feel like co-creators rather than consumers, they don't just buy more... they become advocates. How are you letting your customers build rather than just buy?

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

𝐅𝐨𝐫 𝐲𝐞𝐚𝐫𝐬, 𝐦𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐫𝐚𝐧 𝐨𝐧 𝐡𝐢𝐧𝐝𝐬𝐢𝐠𝐡𝐭. Dashboards told us what already happened—open rates, MQLs, churn numbers. By the time we saw the problem, it was too late. 𝐋𝐞𝐚𝐝𝐬? 𝐃𝐞𝐚𝐝. 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬? 𝐆𝐨𝐧𝐞. 𝐁𝐮𝐝𝐠𝐞𝐭? 𝐁𝐮𝐫𝐧𝐞𝐝. But AI and predictive analytics are flipping the game. 𝐌𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐢𝐬𝐧’𝐭 𝐫𝐞𝐚𝐜𝐭𝐢𝐯𝐞 𝐚𝐧𝐲𝐦𝐨𝐫𝐞. 𝐈𝐭’𝐬 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞. 🔹 𝐋𝐞𝐚𝐝 𝐅𝐨𝐫𝐞𝐜𝐚𝐬𝐭𝐢𝐧𝐠 Traditional lead scoring is broken. A whitepaper download? That’s not intent—it’s noise. When we actually analyzed behavioral data using platforms like HubSpot, we found that multiple pricing page visits and engagement with onboarding content predicted conversions 3x better than generic lead scores. 𝐖𝐢𝐭𝐡 𝐦𝐮𝐥𝐭𝐢-𝐭𝐨𝐮𝐜𝐡 𝐚𝐭𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧 𝐦𝐨𝐝𝐞𝐥𝐬 and 𝐛𝐞𝐡𝐚𝐯𝐢𝐨𝐫𝐚𝐥 𝐜𝐨𝐡𝐨𝐫𝐭 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 ✔ Leads with 𝐫𝐞𝐩𝐞𝐚𝐭 𝐯𝐢𝐬𝐢𝐭𝐬 𝐭𝐨 𝐭𝐡𝐞 𝐩𝐫𝐢𝐜𝐢𝐧𝐠 𝐩𝐚𝐠𝐞 had a 𝟑𝐱 𝐡𝐢𝐠𝐡𝐞𝐫 𝐥𝐢𝐤𝐞𝐥𝐢𝐡𝐨𝐨𝐝 𝐨𝐟 𝐜𝐨𝐧𝐯𝐞𝐫𝐬𝐢𝐨𝐧 ✔ Prospects engaging with 𝐢𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐯𝐞 𝐝𝐞𝐦𝐨𝐬 moved through the funnel 𝟒𝟐% 𝐟𝐚𝐬𝐭𝐞𝐫 ✔ Combining 𝐢𝐧𝐭𝐞𝐧𝐭 𝐬𝐢𝐠𝐧𝐚𝐥𝐬 𝐰𝐢𝐭𝐡 𝐟𝐢𝐫𝐦𝐨𝐠𝐫𝐚𝐩𝐡𝐢𝐜𝐬 increased lead quality 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐢𝐧𝐟𝐥𝐚𝐭𝐢𝐧𝐠 𝐚𝐜𝐪𝐮𝐢𝐬𝐢𝐭𝐢𝐨𝐧 𝐜𝐨𝐬𝐭𝐬 We stopped chasing the wrong leads. And our pipeline? Tighter than ever. 🔹 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐑𝐞𝐭𝐞𝐧𝐭𝐢𝐨𝐧 A churn report tells you what you lost. But by then, it’s a post-mortem. Advanced platforms flag disengagement before it happens. A simple tweak—triggering check-ins for inactive accounts—cut churn by 15% in six months. A simple intervention—𝐭𝐫𝐢𝐠𝐠𝐞𝐫𝐢𝐧𝐠 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐫𝐞-𝐞𝐧𝐠𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 when customers showed 𝟑+ 𝐝𝐢𝐬𝐞𝐧𝐠𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐭𝐫𝐢𝐠𝐠𝐞𝐫𝐬—led to a 𝟏𝟓% 𝐫𝐞𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐢𝐧 𝐜𝐡𝐮𝐫𝐧 𝐢𝐧 𝐬𝐢𝐱 𝐦𝐨𝐧𝐭𝐡𝐬. 🔹 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐅𝐢𝐭 Guessing what users want is a waste of time. Predictive analytics showed us which features had a 𝟒𝟎% 𝐥𝐢𝐤𝐞𝐥𝐢𝐡𝐨𝐨𝐝 𝐨𝐟 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧 before launch. The result? No wasted dev cycles, no misfires—just 𝐝𝐚𝐭𝐚-𝐛𝐚𝐜𝐤𝐞𝐝 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬. If you’re still relying on past data to drive strategy, 𝐲𝐨𝐮’𝐫𝐞 𝐩𝐥𝐚𝐲𝐢𝐧𝐠 𝐲𝐞𝐬𝐭𝐞𝐫𝐝𝐚𝐲’𝐬 𝐠𝐚𝐦𝐞. 𝐌𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐢𝐬𝐧’𝐭 𝐚𝐛𝐨𝐮𝐭 𝐥𝐨𝐨𝐤𝐢𝐧𝐠 𝐛𝐚𝐜𝐤. 𝐈𝐭’𝐬 𝐚𝐛𝐨𝐮𝐭 𝐤𝐧𝐨𝐰𝐢𝐧𝐠 𝐰𝐡𝐚𝐭’𝐬 𝐧𝐞𝐱𝐭. #PredictiveAnalytics #MarketingStrategy #DataDriven #Growth

Esta semana tuve la oportunidad de ser invitado por Bain & Company para compartir algunas de las prácticas que venimos impulsando desde el área de CX en Banco de Crédito BCP . En servicios financieros, donde la diferenciación de productos es limitada, la verdadera diferenciación ocurre en la experiencia que construimos con nuestros clientes. Ahí es donde se gana —o se pierde— la preferencia. Algunos de los temas que conversamos: 1. Escucha no intrusiva: entender al cliente sin depender exclusivamente de encuestas, utilizando señales provenientes de sus interacciones y transacciones para capturar fricciones y oportunidades en tiempo real. 2. Momentos que importan: identificar, con evidencia, los puntos del journey con mayor carga emocional para priorizar inversiones donde el impacto en lealtad es mayor. 3. Predictive NPS, utilizando modelos de lookalike analysis para estimar el sentimiento de quienes no responden y así ampliar nuestra capacidad de gestión. ———————————————————————- This week I had the opportunity to be invited by Bain & Company to share some of the practices we are driving from the CX area at Banco de Crédito BCP . In financial services, where product differentiation is limited, the real competitive edge lies in the experience we build with our customers. That’s where preference is won — or lost. Some of the topics we discussed: 1. Non-intrusive listening: understanding customers without relying exclusively on surveys, leveraging signals from their interactions and transactions to capture frictions and opportunities in real time. 2. Moments that matter: identifying, with evidence, the points in the journey with the highest emotional load in order to prioritize investments where the impact on loyalty is greatest. 3. Predictive NPS: using lookalike analysis models to estimate the sentiment of customers who do not respond, expanding our management coverage and enabling proactive action.

🚀 The Era of One-Size-Fits-All Events Is Over. Stop Doing It. Personalization isn't a single action, it's a series of intentional, strategic choices that come together to make every attendee feel genuinely valued. We’re not just organizing events anymore — we’re crafting journeys. 🧭 In today’s marketplace, attendees expect more than just a badge and a schedule. They want curated content, meaningful connections, and real-time relevance that makes them feel seen. That’s where hyper-personalization comes in. And no, it’s not just using someone’s name in an email. It’s about using data and technology to design experiences that feel custom-built for each person. 🧠📊 As an event marketer, I’m all in on data-driven strategy. This is where we move beyond logistics and design every touchpoint to be personal, memorable, and valuable. Here's some ways that can look like across the attendee journey: Before the Event: 🎯 Targeted Invitations & Content: Use behavioral data to send invites that speak directly to someone's interests. A marketer might get a blog post on campaign strategy, while a developer receives a product case study. 📝 Dynamic Registration: Ask tailored questions based on the attendee’s role or industry to build rich attendee profiles from the start. During the Event: 🤖 AI-Powered Agendas & Recommendations: Event apps can recommend sessions, speakers, and exhibitors based on real-time behavior, interests, and profiles — reducing decision fatigue and maximizing impact. 🤝 Smart Networking: Go beyond job titles. Use AI to match attendees with shared goals, values, or expertise for deeper, more meaningful conversations. 🎉 Personalized On-Site Experiences: Greet attendees by name on welcome screens, print session tracks on badges, or use RFID to tailor in-person interactions. 📽️ Customized Content Delivery: Make booth visits unforgettable. When someone scans their badge, show a video personalized to their company, role, or industry — turning a quick interaction into a memorable moment. 🧢 Personalized Swag: Skip the generic t-shirt. Offer attendees the ability to choose colors, styles, or even print their name on a water bottle or notebook. After the Event 📬 Tailored Follow-Up: Instead of a generic “thanks for coming,” send curated content based on sessions they attended, people they connected with, and their unique interests. 📚 Personalized Content Hubs: Create a portal where attendees can revisit the event — with homepages tailored to their track, interests, or role. 📊 Custom Surveys: Don’t ask vague questions. Personalize post-event feedback forms to reflect their specific journey. 🤔 What's one thing you're doing to add a touch of personalization to your events? Or, as an attendee, what's a personalization strategy that has truly impressed you? Let's share some ideas in the comments! #EventProfs #EventMarketing #HyperPersonalization #EventTech #ExperienceDesign #EventStrategy #PersonalizedExperiences

Personalizing AI Recommendations: A Leap Forward in User Experience ... The research, titled "Reinforced Prompt Personalization for Recommendation with Large Language Models," introduces a novel approach to tailoring AI recommendations for individual users. 👉 The Challenge of Personalization We've all experienced the frustration of staring at a blank search box, trying to articulate our needs to an AI system. Whether searching for a product, movie, or content, it's often difficult to convey our unique preferences and context. Current AI systems typically use a one-size-fits-all approach, which can lead to generic or irrelevant recommendations. 👉 Introducing Instance-wise Prompting The researchers propose a shift from task-wise prompting (using the same prompt template for all users) to instance-wise prompting. This means personalizing the AI's input for each individual user, allowing for more nuanced and accurate recommendations. 👉 The RPP Framework: Tailoring AI Interactions At the heart of this innovation is the Reinforced Prompt Personalization (RPP) framework. Here's how it works: 1. Multi-agent reinforcement learning optimizes prompts for each user 2. Four key prompt patterns are personalized:   - Role-playing: Adapting the AI's persona to match user preferences   - History records: Utilizing relevant past interactions   - Reasoning guidance: Customizing the AI's analytical approach   - Output format: Tailoring how recommendations are presented  👉 Efficiency and Quality Improvements The RPP framework brings two significant advancements: - Sentence-level optimization: Instead of tweaking individual words, the system works at the sentence level, dramatically improving efficiency. - Carefully crafted action spaces: This ensures high-quality prompts while keeping computational demands manageable. 👉 Versatility Across AI Models One of the most promising aspects of this research is its broad applicability. The RPP framework has shown effectiveness across various types of large language models: - Open-source models (e.g., LLaMa2) - API-based models (e.g., ChatGPT) - Fine-tuned models (e.g., Alpaca) 👉 Real-World Impact The potential applications of this technology are vast: - E-commerce: More accurate product recommendations based on individual shopping patterns and preferences - Content streaming: Personalized movie, music, and video suggestions that truly reflect a user's taste - Digital marketing: Tailored ad experiences that resonate with each consumer's interests and needs 👉 Breaking the One-Size-Fits-All Barrier The researchers demonstrate that RPP significantly outperforms traditional recommender systems, few-shot methods, and other prompt-based approaches. By moving beyond generic prompts, AI systems can now provide recommendations that feel truly personalized. The paper in comments.