Dynamic Customer Segmentation Models

Hot take: If you're still segmenting customers solely by ARR and company size, you're leaving money on the table. After a painful realization, we completely overhauled our segmentation model: Our highest-paying enterprise customers weren't necessarily the most profitable or successful. Traditional segmentation missed these critical factors: Product usage patterns Growth potential (not just current spend) Support cost-to-revenue ratio Implementation complexity Use case maturity The result? We were over-serving some accounts and under-serving others based on flawed assumptions. Our new dynamic segmentation model includes: User adoption velocity Feature utilization depth Growth readiness score Technical maturity index Success potential metric The impact? 47% reduction in time-to-value 32% increase in expansion revenue More precise resource allocation Happier customers (and CS team!) A startup paying you $30K might have better product-market fit and growth potential than an enterprise paying $200K but struggling with adoption. Modern customer segmentation should be fluid, multi-dimensional, and focused on success potential, not just current value. What factors do you consider in your segmentation model? #CustomerSuccess #SaaS #GrowthStrategy #CustomerExperience

𝐒𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐂𝐥𝐮𝐬𝐭𝐞𝐫𝐢𝐧𝐠: 𝐰𝐡𝐞𝐧 𝐬𝐞𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐬𝐭𝐚𝐫𝐭𝐬 𝐰𝐢𝐭𝐡 𝐦𝐨𝐝𝐞𝐥 𝐥𝐨𝐠𝐢𝐜, 𝐧𝐨𝐭 𝐫𝐚𝐰 𝐝𝐚𝐭𝐚 We usually think of SHAP values as a way to interpret how a model makes its predictions: for a single case or for a batch of them. But their use goes far beyond explainability. 💡 Imagine you have a customer segmentation problem: for churn, growth, or upsell analysis. You could apply a standard unsupervised clustering approach using selected features. But 𝐢𝐭 𝐝𝐨𝐞𝐬𝐧'𝐭 𝐫𝐞𝐟𝐥𝐞𝐜𝐭 𝐡𝐨𝐰 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐞𝐚𝐜𝐡 𝐟𝐞𝐚𝐭𝐮𝐫𝐞 𝐢𝐬 𝐟𝐨𝐫 𝐲𝐨𝐮𝐫 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐨𝐮𝐭𝐜𝐨𝐦𝐞, nor how features interact in the given problem's context, and especially not on an individual level. That's where SHAP values come to rescue. They let you look at each customer's state vector through the lens of the predictive model, grouping customers by similar prediction paths rather than by raw data values. This idea is known as 𝐒𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐂𝐥𝐮𝐬𝐭𝐞𝐫𝐢𝐧𝐠, a concept introduced in the paper "𝐂𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐭 𝐈𝐧𝐝𝐢𝐯𝐢𝐝𝐮𝐚𝐥𝐢𝐳𝐞𝐝 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐀𝐭𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐓𝐫𝐞𝐞 𝐄𝐧𝐬𝐞𝐦𝐛𝐥𝐞𝐬" (Lundberg et al., 2018). Instead of clustering raw data, we 1️⃣ train a predictive model (e.g., gradient boosting), 2️⃣ then compute SHAP values: how much each feature contributed to the prediction for each observation. Then, we cluster these SHAP values, so each cluster 𝐠𝐫𝐨𝐮𝐩𝐬 𝐨𝐛𝐣𝐞𝐜𝐭𝐬 𝐭𝐡𝐚𝐭 𝐠𝐨𝐭 𝐬𝐢𝐦𝐢𝐥𝐚𝐫 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐬𝐢𝐦𝐢𝐥𝐚𝐫 𝐫𝐞𝐚𝐬𝐨𝐧𝐬. 𝐖𝐡𝐲 𝐢𝐭'𝐬 𝐩𝐨𝐰𝐞𝐫𝐟𝐮𝐥: ✅ Same scale for all features as SHAP values are measured in the model's output units, solving the feature weighting problem. 🧭 Better interpretation as clusters reflect model logic, not just data similarity. 🧩 Actionable insights: you can identify subgroups (customers, patients, transactions) that the model treats in a similar way. 𝐓𝐡𝐢𝐬 𝐭𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞 𝐰𝐨𝐫𝐤𝐬 𝐞𝐬𝐩𝐞𝐜𝐢𝐚𝐥𝐥𝐲 𝐰𝐞𝐥𝐥 𝐰𝐡𝐞𝐧: ▶️ You already have a strong predictive model. ▶️ You want to explore patterns behind the predictions. ▶️ You need interpretable clusters for strategy or communication. 👉 Supervised Clustering is a great way to connect prediction and segmentation, especially in business and healthcare use cases. #MachineLearning #DataScience #AdvancedML #ExplainableAI #SHAP #Clustering #PredictiveAnalysis

🚀 Rethinking Segmentation: AI & Analytics in Modern Banking 🏦✨ Many retail and commercial banks are at a crossroads: How do we move beyond legacy segmentation models — the ones driven mainly by product sales, static demographics, or broad tiers — to truly understand customers in real time? This is where AI and advanced analytics step in as game changers. 🔍 What’s changing? ✅ Data-driven segmentation: Instead of static segments, AI can cluster customers based on actual behaviors, transaction patterns, channel usage, and lifecycle signals. ✅ User flow insights: Banks can now analyze how customers navigate digital channels — from onboarding to everyday banking — identifying friction points and tailoring journeys in real time. ✅ Predictive engagement: Advanced models don’t just describe segments — they predict which customers are likely to churn, upsell, or need proactive support, enabling timely and personalized outreach. 💡 Why does this matter? For retail banking, it means moving from generic campaigns to micro-targeted offers that feel relevant and timely. For commercial banking, it means understanding the nuanced needs of SMEs and corporates based on transaction networks and industry shifts — not just size or revenue bands. 📈 The result? More relevant interactions, higher trust, better conversion, and a clear competitive edge in a crowded market. As banks race to become more customer-centric, AI-powered segmentation is no longer optional — it’s a strategic imperative. Curious to hear: How is your bank evolving its segmentation playbook? 🤝👇 #Banking #AI #Analytics #CustomerExperience #DigitalTransformation #RetailBanking #CommercialBanking #Segmentation

🚀 What’s New for Data Cloud & Marketing Cloud? Dynamic Segments + Broadcast Flows unlock real-time, high-volume messaging. In the October 2025 Data Cloud update, two major features were introduced: Dynamic Segments and Broadcast Flows. In particular, "Dynamic Segments" suddenly appeared in the new segment creation screen, leaving many users wondering: --- "How do I use this?" 🤔 --- "What's different from a traditional segment?" 🤯 My latest article explains, as simply as possible, how these new features work, when to use them, and how to configure them step by step. Blog: https://lnkd.in/g2hSq9uU ============================== This combo unlocks powerful use cases for operational alerts, service notifications, and instant audience targeting. 🔍 Dynamic Segments — Key Points Only available inside Broadcast Flows (for now) Mix static + dynamic conditions - - - Static = fixed filter (e.g., Gender = Female) - - - Dynamic = filter changes at execution time based on API parameters No “Publish” step — always uses the latest saved version Supports up to 100,000 records per execution Count/Preview works, but you must temporarily replace dynamic parameters with static values 📡 Broadcast Flows — Key Points Segment-based version of On-Demand Flow Triggered via REST API or SubFlow Perfect for real-time bulk sends ⚠️ Limitations: - No Schedule  - No Debug - No Wait Element 🧩 Why This Matters By combining both features, organizations can: ✔ Send real-time operational alerts ✔ Notify customers instantly based on dynamic inputs ✔ Improve CX and reduce support cases ✔ Execute large-volume sends without prepublishing segments 📘 Example Use Cases 🛫 Airline gate changes sent instantly to affected passengers 🎢 Theme park weather/emergency alerts 🏠 Proactive service outage notifications 🔌 Utility maintenance alerts by postal code 🏁 Conclusion If your business needs fast, large-scale, dynamic messaging, Dynamic Segments + Broadcast Flows are true game changers. #Salesforce #MarketingCloud #DataCloud #MomentMarketer #MarketingChampion #MarketingChampions 🔗 Connect with me on LinkedIn: https://lnkd.in/gkeKyShH

Your “Segmentation” isn’t Strategy. It’s Sorting. Let’s be honest: in most organizations, “segmentation” is just sorting. Sort by channel. Sort by attributes. Sort by events. Then feed it into Meta, Braze, your CDP and hope for lift. That is not segmentation. Those are filters. Creating data rules assumes you know everything about your data. 🤣 Real segments are derived from patterns that exist in data. Not ones you create. Patterns exist regardless of what you want them to be. These patterns show you who your customers really are, how they are motivated and what they want. Best of all, segments inform how to grow their value systematically. They are the only way to align marketing investments with business outcomes. The problem? Most teams haven’t had access to segmentation at that strategic level. It’s been slow. Expensive. Hard to operationalize. Agencies deliver $500K decks filled with qualitative personas and insights that don’t map to IDs or KPIs. Or your first-party data to get matched to someone else’s idea of a segment or audience. None of these methods is the first-principle that customer segmentation should be for every business. In the absence of strategy, the center of gravity shifts to tactics. And marketing becomes reactive launching flows, optimizing CAC, chasing incrementality at edges that always regress to the mean. Meanwhile, the boards asks harder questions: 💰 → What’s driving retention? → Where’s our next high-growth market segment? → Why aren’t we seeing lift despite all this investment in data? This is where AI changes the game. 🧠 Neural networks discover segments you didn’t know existed. And thanks to data warehouses like Databricks, Snowflake and Google Cloud it can be done instantly, securely and tied to the revenue KPIs that actually matter. Even more powerful: reasoning models can map the strategies to grow those segments. Automatically. At scale. That’s what we’re building at Neuralift AI: 🚀 → On-demand neural segmentation across your customer data → Strategy generation tied to your business KPIs → Outputs your team can use across a multitude of use cases No more personas. No more rules-based audiences. No more filters that minimize data. Just intelligent segments and strategy using ALL your data. If you’re leading Growth, Marketing, or Insights and want to see what this unlocks, DM me or comment below.