We've reached a turning point in how we gather customer data. For years, our efforts have been focused on inference: using clickstream data, browsing history, and demographics to guess at customer intent. But let's be honest, that approach is 1) imprecise for experience designers and marketers, 2) invasive for customers, and 3) increasingly limited by privacy regulations. One idea I’ve been thinking about is DECLARATION and the use of first-party data in LLM interactions to co-create context with customers. Interfaces now increasingly have an AI layer, and LLMs make it possible for more two-way dialogue between brands and customers. These conversational UIs can be designed to invite customers to declare their context in their own words. They can articulate their goals, constraints, and needs in natural language…it’s a kind of data that’s higher-signal and lower-noise than anything we can infer. Declared data > inferred data. My perspective is that the brands that build for this kind of trusted, two-way dialogue, and not just one-way data extraction will get far better data (+ quality) and build far deeper trust. The key isn't just to ask for context in LLM experiences, it’s to show your work. This idea is what I'm calling the "Context Ledger": a transparent, turn-by-turn narrative that proves the AI is remembering and acting on what the user shared. Imagine a travel booking scenario: 🧑💼: "I'm looking for a weekend trip with my family. We have a toddler, so we need something stroller-friendly." 🤖 : "Got it. I'll prioritize options that are a short walk from amenities and have nap-friendly schedules. I'll also pre-filter for places with elevator access. How does that sound?" 🧑💼: "Perfect, thanks. And what about a tight budget?" 🤖: "I hear you. I’ve just updated the search to show the most cost-effective options, including some with free admission for kids. I used your toddler + stroller + budget preferences to narrow things down. Would one of these options work for you?" This kind of “context receipt” converts a moment of user disclosure anxiety into voluntary sharing and trust. It shifts our CX from transactional to truly collaborative. Dialogue can be the enabler of real strategic advantage - collecting customer data needs to follow quickly with proving you’ve understood and showing how you’re using that data to deliver value quickly. #ExperienceDesign #DataStrategy #LLM #CX
From Inference to Declaration: The Future of Customer Data
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Bridging the gap between AI’s potential and real transformation in customer experience comes down to two qualities we don’t talk about enough in business: empathy and imagination. > Empathy — to align technical and business teams around shared strategies and purpose. > Imagination — to reframe customer experience in light of radically new capabilities. The enterprises that win in the AI era will have both at their core. Those who settle for automating existing workflows and back-office tasks may save costs but they’ll also lock themselves into slow, inevitable decline.
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AI and customer service, will it ever get good enough ? If #Anthropic can't get it right, when they own the model, who can ? I admit that my experience is statistically irrelevant and not important and I only pay Anthropic $240 a year. But I pay them all the same I don't use Claude Code to do paid work (otherwise it would be a no brainer to go all in on max). I also appreciate there have to be usage limits so because of that I want to keep an eye on my usage, I think that's a fair ask. I have been trying to get the matter resolved for more than a month it could be longer. I have had one human message closing off the case with the wrong answer and several agent conversations which always result in a hallucinated answer and then nothing of value once I point out the mistake ( the agent actually goes into a bit of an inaccuracy spiral repeating irrelevant sections from docs which I have already told it are not relevant). All I want is the Claude Code dashboard in Console (for non api use) to have some data in it. I don't think that is unfair. There are probably some lessons, Here's a few I made up: * If I churn big deal, but what if there are many people with some niggles then it gets materially measurable and churn can suddenly be a problem * They've got the best tech to analyse every chat and establish the cause when things aren't resolved. Monioring and insight are vital and I don't mean self serving summaries for the board saying everything is great, I mean proper root cause analysis and tweaking continuous tweaking. Even if I am an anomaly or outlier there could be a useful adjustment. Don't build self congratulary dashboards, look for the unhappy paths * What has my customer effort been like ? I Chat to the agent, if I have missed something in docs, great problem solved, but when you have the same conversation again and again and it doesn't remember what it proposed and what didn't work customer effort increases, over a protracted time period. * Whats the overall customer context like - this should influence handling and routing * I don't want instant service for this, I just want to know it will be fixed, I wouldn't even care if I got a message from a human saying "sorry but it'll be fixed in two weeks", I'd be happy. * Human handoff - what is a good service level ? so far it's been two days and silence * And finally - this one made me laugh out loud. I have posted 3 messages in the chat window asking for an update... nothing, silence. I think I know why but really, on Anthropic's support chat ?? Folks, make sure you test all possible random user journeys when you implement this stuff. * Finally, don't implement chatbots for deflection - implement them to fix things!! The board will be happier if churn decreases and revenue increases. Being a bit more serious, slapping a bit of technology over a problem and hoping for the best has never, ever worked. GenAI for customer service is no different.
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Machine Learning in Customer Behavior Prediction: Transforming Business with Data-Driven Insights In today’s highly competitive marketplace, understanding customer behavior is no longer a luxury-it’s a necessity. With the rapid advancements in artificial intelligence (AI) and data science, machine learning (ML) has emerged as a powerful tool that enables businesses to predict customer actions, preferences, and future needs with remarkable accuracy. This article explores how machine learning in customer behavior prediction is reshaping industries by driving more informed marketing strategies, improving customer engagement, and boosting overall ROI. What is Machine Learning in Customer Behavior Prediction? Machine learning refers to algorithms that improve automatically through experience by analyzing vast amounts of data. When applied to customer behavior prediction, ML models sift through historical data-such as purchase history, browsing patterns, and demographic information-to identify trends and forecast future behaviors. This predictive power enables businesses to anticipate customer needs and tailor their offerings accordingly. Key Components Data Collection: Gathering behavioral data from various sources like websites, CRM software, and social media. Feature Engineering: Transforming raw data into informative features that machine learning models can understand. Model Selection: Choosing appropriate algorithms such as decision trees, neural networks, or clustering for prediction tasks. Model Training & Validation: Feeding labeled data to the model while testing its accuracy on unseen data. Prediction & Deployment: Implementing the model to offer real-time insights into customer behavior. Benefits of Using Machine Learning for Customer Behavior Prediction Integrating machine learning into customer analytics opens tremendous opportunities for businesses of all sizes. Some key benefits include: Enhanced Personalization: ML models enable hyper-personalized marketing campaigns by predicting individual preferences and purchase intent. Improved Customer Retention: By identifying churn risks early, companies can proactively engage customers and reduce turnover. Optimized Marketing Spend: Predictive analytics help allocate advertising budgets more effectively by targeting high-potential leads. Increased Sales and Revenue: Anticipating cross-selling and upselling opportunities drives higher conversion rates. Better Inventory Management: Predict customer demand patterns to balance stock levels and reduce wastage. Popular Machine Learning Techniques for Customer Behavior Prediction Depending on the business goal and data type, different ML methods are leveraged to forecast customer actions: Technique Description Use Cases Classification Algorithms Models like Logistic Regression, Random Forest classify customers into groups (e.g., likely to buy/don’t buy). Churn prediction, customer segmentation
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Machine Learning in Customer Behavior Prediction: Transforming Business with Data-Driven Insights In today’s highly competitive marketplace, understanding customer behavior is no longer a luxury-it’s a necessity. With the rapid advancements in artificial intelligence (AI) and data science, machine learning (ML) has emerged as a powerful tool that enables businesses to predict customer actions, preferences, and future needs with remarkable accuracy. This article explores how machine learning in customer behavior prediction is reshaping industries by driving more informed marketing strategies, improving customer engagement, and boosting overall ROI. What is Machine Learning in Customer Behavior Prediction? Machine learning refers to algorithms that improve automatically through experience by analyzing vast amounts of data. When applied to customer behavior prediction, ML models sift through historical data-such as purchase history, browsing patterns, and demographic information-to identify trends and forecast future behaviors. This predictive power enables businesses to anticipate customer needs and tailor their offerings accordingly. Key Components Data Collection: Gathering behavioral data from various sources like websites, CRM software, and social media. Feature Engineering: Transforming raw data into informative features that machine learning models can understand. Model Selection: Choosing appropriate algorithms such as decision trees, neural networks, or clustering for prediction tasks. Model Training & Validation: Feeding labeled data to the model while testing its accuracy on unseen data. Prediction & Deployment: Implementing the model to offer real-time insights into customer behavior. Benefits of Using Machine Learning for Customer Behavior Prediction Integrating machine learning into customer analytics opens tremendous opportunities for businesses of all sizes. Some key benefits include: Enhanced Personalization: ML models enable hyper-personalized marketing campaigns by predicting individual preferences and purchase intent. Improved Customer Retention: By identifying churn risks early, companies can proactively engage customers and reduce turnover. Optimized Marketing Spend: Predictive analytics help allocate advertising budgets more effectively by targeting high-potential leads. Increased Sales and Revenue: Anticipating cross-selling and upselling opportunities drives higher conversion rates. Better Inventory Management: Predict customer demand patterns to balance stock levels and reduce wastage. Popular Machine Learning Techniques for Customer Behavior Prediction Depending on the business goal and data type, different ML methods are leveraged to forecast customer actions: Technique Description Use Cases Classification Algorithms Models like Logistic Regression, Random Forest classify customers into groups (e.g., likely to buy/don’t buy). Churn prediction, customer segmentation
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AI has moved from the future to the present, enabling more intelligent choices. In our most recent blog post, we discuss how AI agents in customer analytics may help your company's customer engagement initiatives, provide predictive insights, and empower your teams to make data-driven decisions more quickly. 👉 Read the full article here: https://lnkd.in/dtMCWyxy #Dlytica #AI #CustomerAnalytics #ArtificialIntelligence #CustomerExperience #PredictiveAnalytics #DigitalTransformation #BigData #MachineLearning #AIinBusiness #Customer360
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In my last post, I talked about AI customer intelligence—today I want to share what it actually looks like when you can predict customer behavior with scary accuracy. "We're losing customers, and I don't know why until it's too late." Sound familiar? Traditional CRMs tell you what happened, but they can't whisper "Hey, Sarah from ABC Corp is about to cancel her contract in 45 days." That's exactly what predictive analytics does—it connects the dots your brain can't. Forget the fancy algorithms for a moment. Here's what really predicts customer behavior: * The Obvious Stuff: Purchase patterns, support tickets, email opens * The Sneaky Signals: How long they spend on your pricing page, whether they forward your emails, if they're suddenly researching competitors * The Context: Their industry's busy season, economic pressures, internal changes The magic happens when you combine these signals. A customer who stops opening emails AND hasn't logged into your platform for two weeks AND just hired a new VP? That's not a coincidence. Three Models That Actually Work: 1. The "Flight Risk" Detector Spots customers likely to churn 60-90 days before they actually leave. Think of it as your early warning system. 2. The "Ready to Buy" Radar Identifies prospects showing buying signals across multiple touchpoints. No more cold calling—warm leads only. 3. The "Hidden Goldmine" Finder Reveals which customers have the highest lifetime value potential, even if they're spending small amounts today. Here's what nobody tells you: the technology is the easy part. The hard part is getting your team to trust a machine's predictions. Week 1-8: You'll spend most of your time cleaning messy data and arguing about what "engagement" actually means. Week 9-18: Building models that don't embarrass you in front of customers. Aim for 85% accuracy—perfect is the enemy of good. Week 19-24: Getting your sales team to actually use the scores. This is where most projects fail. Month 7+: Fine-tuning based on what actually happens. Models get smarter, predictions get better. What You Need to Get Started: Data: At least 1,000 customers with 12 months of history. Less than that? Wait. Infrastructure: Ability to process data in real-time. Your customers don't wait for batch jobs. Team Buy-in: If your sales team thinks AI is replacing them, you've already lost. The Uncomfortable Truth Most predictive analytics projects fail not because of bad technology, but because organizations try to predict everything instead of focusing on the predictions that change behavior. Start small: Can you predict which customers will buy in the next 30 days with 80% accuracy? Master that before trying to predict the next five years of customer lifetime value. The companies winning with predictive analytics aren't using the fanciest algorithms—they're using the right data to answer specific questions that drive immediate action. #PredictiveAnalytics #CustomerBehavior #SalesTech #AIReality
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As more retail marketing leaders strive to achieve their north star and true 1:1 customer engagement they are looking to AI to automate much of the process. As we look to AI as a true business partner on this journey, leaders from across industries are asking questions about privacy. Our very own Mark Drasutis shares his thoughts on some of these considerations and how to approach some of these opportunities.
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🚀 𝐓𝐡𝐞 𝐆𝐫𝐨𝐰𝐭𝐡 𝐏𝐚𝐫𝐚𝐝𝐢𝐠𝐦: 𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐛𝐲 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐃𝐚𝐭𝐚 In today’s fast-moving digital landscape, building successful products means more than just delivering features. It’s about creating value, enhancing experience, and anticipating needs. At Yopeso, we shape our development processes around 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐃𝐚𝐭𝐚, turning insights into outcomes. 🎯𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐏𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐳𝐚𝐭𝐢𝐨𝐧: 𝐕𝐚𝐥𝐮𝐞-𝐃𝐫𝐢𝐯𝐞𝐧 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 Customer feedback, usage analytics, and behavioral data tell us what truly moves the needle. Instead of chasing “nice-to-haves”, we focus on features that deliver real business impact: UI/UX improvements, performance optimization, and seamless mobile experiences. 🖥 𝐔𝐗/𝐔𝐈 𝐒𝐡𝐚𝐩𝐞𝐝 𝐛𝐲 𝐔𝐬𝐞𝐫 𝐁𝐞𝐡𝐚𝐯𝐢𝐨𝐫 We pay close attention to friction points: where users drop off, hesitate, or abandon a flow. These signals drive redesigns, simpler journeys, and intuitive experiences that feel effortless. 🔑 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 & 𝐂𝐮𝐬𝐭𝐨𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐒𝐭𝐫𝐨𝐧𝐠𝐞𝐫 𝐄𝐧𝐠𝐚𝐠𝐞𝐦𝐞𝐧𝐭 By leveraging user preferences, context, and environment, we deliver 𝐩𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐳𝐞𝐝 𝐝𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝𝐬, 𝐭𝐚𝐢𝐥𝐨𝐫𝐞𝐝 𝐫𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬, 𝐚𝐧𝐝 𝐚𝐝𝐚𝐩𝐭𝐚𝐛𝐥𝐞 𝐬𝐞𝐭𝐭𝐢𝐧𝐠𝐬, fostering loyalty and long-term engagement. 🔮 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 & 𝐏𝐫𝐨𝐚𝐜𝐭𝐢𝐯𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 Data isn’t just about the past, it’s our lens into the future. Predictive models help us forecast potential performance issues, identify churn risks, and determine what features should be prioritized. This empowers our 𝐃𝐞𝐯𝐎𝐩𝐬, 𝐐𝐀, 𝐁𝐚𝐜𝐤𝐞𝐧𝐝, 𝐚𝐧𝐝 𝐅𝐫𝐨𝐧𝐭𝐞𝐧𝐝 𝐭𝐞𝐚𝐦𝐬 to act before problems arise. ⚙️ 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐃𝐚𝐭𝐚 𝐢𝐧 𝐃𝐞𝐯𝐎𝐩𝐬: 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐢𝐧 𝐀𝐜𝐭𝐢𝐨𝐧 𝐂𝐈/𝐂𝐃 𝐅𝐞𝐞𝐝𝐛𝐚𝐜𝐤 𝐋𝐨𝐨𝐩𝐬: Every deployment is measured, tracked, and optimized in near real-time. 𝐂𝐥𝐨𝐮𝐝 & 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠: From logs to device/browser distributions, every detail matters for reliability. 𝐈𝐧𝐜𝐢𝐝𝐞𝐧𝐭 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭: Real-world errors and anomalies fuel our QA and development cycles. 𝐀𝐈-𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐈𝐧𝐬𝐢𝐠𝐡𝐭: From chatbots to anomaly detection, AI scales customer intelligence across our operations. At the core, our philosophy is simple: listen, learn, and act. Customer data isn’t just an input, it’s the engine of continuous growth. 💡 Curious how we can help your business unlock data-driven product evolution? Let’s talk! #ProductDevelopment, #DevOps, #AI, #CustomerExperience #Cloud #CyberSecurity #MobileApps #FullStack #DataDriven
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No doubt, personalized pricing is a contentious area. It can very quickly feel unfair or exploitative if done inappropriately. But many economists think it's only going to become more commonplace as the world collects more data and AI models become more advanced. Our version of personalized pricing includes the following: Transparency: be upfront with customers about them getting a personalized discount. Even better, say why they are getting the discount - e.g. "for our loyal customers", "for our Instagram followers". Fairness: limit the types of data you are using to personalize prices, e.g. income data and its proxies are a no-go. Consistency: honor discounts for a fixed period of time once shown to a visitor. We don't want too many price fluctuations. I'm sure this list will grow, but it's a start. https://lnkd.in/gnepTwVp
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🚨 New Research🚨 AI’s potential to transform customer experiences is clear — but a recent Informatica survey reveals a critical gap: while 87% of data leaders rank AI as a board-level priority, only 33% of business professionals see it as critical to CX. In this article, Monica Mullen 🌵🌞, Director of Product Marketing at Informatica, explores why this disconnect persists and what CDOs must do to close it. From silo-busting data architectures to automated governance and continuous monitoring, Mullen outlines four foundational practices for AI-ready customer data that can turn fragmented systems into engines of customer trust and growth. 👉 Read the full article to see how leading enterprises are building AI-ready foundations and what it means for the future of CX: https://hubs.ly/Q03HZbqL0 #AI #CustomerExperience #CDO #DataGovernance #AIReadyData #Informatica
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