Ways to Use Customer Data for Personalization

Here’s a common myth about personalization: All you need is a customer’s name to make it effective. True personalization goes much deeper, it’s about understanding behaviors, preferences, and needs to create meaningful experiences. Collecting the right data isn’t just about volume, it’s about relevance. You can’t offer genuine personalization without truly knowing your audience. Here’s how I’ve approached it: ➜ Identify key data points. Don’t collect data just for the sake of it. Focus on what will actually help you understand your customers better, things like purchase history, browsing behavior, and engagement patterns. ➜ Leverage tools wisely. Using the right tools is crucial. We’ve integrated platforms (like HubSpot) to ensure we’re gathering and utilizing data that matters, not just creating noise. ➜ Respect privacy. Personalization should never come at the cost of privacy. Being transparent with your audience about what data you collect and how you use it builds trust. ➜ Test and refine. Data isn’t static, and neither should your approach to personalization be. Continuously test what works and refine your strategy to meet your customers' evolving needs. ↳ By focusing on relevant data, not just more data, we’ve been able to create personalized experiences that resonate, leading to stronger customer relationships and better results. What’s been your biggest challenge in collecting data for personalization? How are you overcoming it? #data #personalization #hubspot

Brands are lighting money on fire - and calling it email marketing. Here’s how the top 1% of brands use identity resolution to 3x repeat sales: 🧵 The problem isn't your subject lines or send times. It's that you don't know who you're talking to. With iOS updates and cookie deprecation, traditional tracking is broken. Yet most DTC brands still send generic emails to everyone. Smart brands use identity-aware flows that adapt in real time. They know Sarah bought 3 times vs. Mike who's browsing for the first time. Dynamic personalization examples: • Returning customer sees UGC from previous purchase • New visitor gets shipping reassurance + incentive • High-value buyer gets personalized offers • VIP customers skip generic flows entirely Many brands don't know if their cart abandoner is a first-timer or repeat buyer. This means they're leaving money on the table every day. Smart brands resolve unknown visitors into complete customer profiles. They connect session behavior to purchase history instantly. Here are zero-party data strategies that actually work: • Interactive quizzes that sync to email profiles • Post-purchase surveys that reveal acquisition source • Smart popups that personalize based on user resolution • Progressive profiling that builds customer understanding over time For retention, stop treating customers like strangers. Route power users to bundle promos instead of 10% discounts. Show loyal customers exclusive products, not generic sales. At Opensend, we turn unknown visitors into CRM-ready identities. They enrich profiles automatically and sync behavior across platforms. If your email strategy relies on guesswork, performance will be limited. When you build owned channels on identity intelligence, everything changes. Higher open rates, better conversion, stronger retention. All because you finally know who you're talking to. The brands winning in 2025 treat personalization as infrastructure, not tactics.unk

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

4 practical ways to use AI in Product Marketing with prompts ⬇️ Most PMMs already use AI for content/idea generation and meeting summaries. But what are some other ways? There are a million applications, but for a brief LinkedIn post, here are some ideas and how-to steps: 1️⃣ Customer segmentation and messaging personalization How-to steps: ➖Collect customer information from CRM, website analytics, and sales data. ➖Establish criteria for segmentation (demographics, purchase history). ➖Use AI to sort customers into distinct groups and recommend tailored messaging. Prompts: ✅ “Analyze this customer dataset to identify segments based on behaviors like purchase history, engagement level, and product preferences.” ✅ “For each segment, create a profile with demographics, interests, and recommended messaging strategies.” 2️⃣ Sentiment analysis for products/features How-to steps: ➖Collect feedback from various sources, such as social media, product reviews, and customer service interactions. ➖Use AI to assess customer sentiment around products or features. ➖Highlight positives and improvement areas to inform messaging and product strategy. Prompts: ✅ “Analyze recent customer reviews of [Product Name] for sentiment around features like [Feature 1] and [Feature 2].” ✅“Identify top positive and negative themes in customer feedback, focusing on usability, performance, and support.” 3️⃣ Competitive intelligence How-To Steps: ➖Set up AI to monitor competitor websites, social media, and industry news. ➖Define key metrics for tracking. ➖Use AI to summarize competitive data, identifying trends. Prompts: ✅ “Analyze recent marketing campaigns from our top competitors. Identify common themes and unique selling points.” ✅“Compare our product features with those of [Competitor A] and [Competitor B]. Highlight areas where we excel or need improvement.” 4️⃣ Customer journey mapping How-To Steps: ➖Gather data on customer interactions across touchpoints (website, email, social media, support). ➖Use AI to identify common paths to sale and pain points in the customer journey. ➖Use recommendations to improve touchpoints and customer experience. Prompts: ✅ “Analyze our customer interaction data to identify the most common paths to purchase for [Product X].” ✅ “Based on customer behavior data, suggest improvements for our onboarding process to increase user activation.” To achieve this, you need more than ChatGPT or Claude. So, for tools to assist; I like Pendo for customer journey, Klue for competitor intelligence, and SalesForce Einstein for CRM segmentation help. 

📊 𝗗𝗮𝘁𝗮 𝗜𝘀 𝘁𝗵𝗲 𝗡𝗲𝘄 𝗖𝘂𝗿𝗿𝗲𝗻𝗰𝘆: 𝗛𝗼𝘄 𝗕𝗮𝗻𝗸𝘀 𝗖𝗮𝗻 𝗨𝘀𝗲 𝗜𝘁 𝘁𝗼 𝗥𝗲𝘀𝘁𝗼𝗿𝗲 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗲𝗱 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 The personal touch in banking is fading, and it’s clear that customers are feeling the loss. About 70% of consumers, and an even higher 77% of top earners, are craving those personalized interactions that make them feel valued and understood. When banks 🏦 overlook this need, they risk more than just a dip in satisfaction—they’re putting customer loyalty on the line. High earners, in particular, are likely to take their business elsewhere if they don’t feel a personal connection. Can banks really afford to ignore this? Here’s how to rebuild that personal touch using data: 🏹 Data-Driven Profiles: Create detailed customer profiles to understand individual preferences, spending habits, and goals, tailoring every interaction. 🏹 Predictive Analytics: Use predictive analytics to anticipate needs and offer personalized services like savings plans or investment options. 🏹 Customized Communication: Identify each customer’s preferred communication channel and tailor your outreach to meet them where they are. 🏹 Dynamic Recommendations: Suggest products and services based on life stage, financial situation, and personal interests, making customers feel understood. 🏹 Real-Time Personalization: Implement real-time data analysis to deliver instant personalized experiences, like special offers during branch visits or online banking sessions. By leveraging data in these ways, banks can reignite the personal touch that customers are craving, strengthening relationships and fostering loyalty. How are you using data to bring back the personal touch in banking? Let’s discuss. ⬇️ #customerrelations #bankingindustry #personalization #innovation #smallbusiness #finance

80% of people prefer to buy from brands that personalize. Yet most businesses still send generic campaigns. Here’s how I use AI to change that 👇 Step 1: Build Your Data Foundation → Consolidate customer data from all sources → Clean and structure your data → Create unified customer profiles → Map customer journeys Step 2: Choose the Right AI Tools → Start with predictive analytics → Add dynamic content generation → Implement real-time personalization engines → Focus on tools that integrate with your stack Step 3: Create Personalization Frameworks → Segment audiences by behavior → Design content templates → Set up trigger-based workflows → Define success metrics Real examples that work: 1/ E-commerce: → AI analyzes browsing patterns → Predicts next likely purchase → Personalizes email timing ↳ Result: 40% higher conversion rates 2/ B2B Marketing: → AI scores leads in real-time → Customizes content by industry → Automates follow-up timing ↳ Result: 3x faster sales cycles 3/ Content Marketing: → AI suggests trending topics → Personalizes content recommendations → Optimizes posting schedules ↳ Result: 2x engagement rates Warning: Avoid these common mistakes: → Implementing AI without clean data → Focusing on tools over strategy → Forgetting the human element → Ignoring privacy concerns Remember: AI amplifies your marketing. It doesn't replace your strategy. Start small, measure results, scale what works. What's your biggest challenge with marketing personalization? Comment below. Sign up for my newsletter for more marketing and AI content: https://lnkd.in/gSi-nA2F Repost or follow Carolyn Healey for more like this.