Choosing The Right Customer Segmentation Approach

Segmentation is one of those concepts that sounds simple until you actually try to do it properly. Most teams start with broad categories like age, location, or gender, but the real insight comes when you start looking at how users act - how often they visit, how recently they engaged, how much value they bring, and which patterns naturally form across those dimensions. The goal of segmentation isn’t to label users, it’s to understand the structure of their behavior. That’s what data-driven segmentation methods allow us to do. K-Means, for example, helps you find natural patterns hidden in behavioral data. You decide how many groups you want to explore, and the algorithm does the heavy lifting, assigning each user to the cluster that best represents their behavior. It’s simple, efficient, and powerful for large datasets where you want to explore engagement trends without predefining who belongs where. When you need to see relationships instead of just results, hierarchical clustering becomes more useful. It builds a tree-like view showing which users are similar and where meaningful divisions exist. You don’t need to commit to a single number of segments. You can cut the tree at different points to explore how granular your understanding should be. It’s particularly helpful for moderate datasets where interpretability matters as much as precision. Then there’s DBSCAN, a method designed for reality - where user behavior is messy, irregular, and full of noise. Unlike K-Means, DBSCAN doesn’t assume clusters are neat or circular. It groups users by density, identifying natural clusters and automatically separating outliers. This makes it especially valuable for complex behavioral or clickstream data where some users behave in ways that don’t fit any conventional pattern. If you want something more business-focused and immediately actionable, RFM segmentation (Recency, Frequency, Monetary) remains a classic for a reason. By scoring how recently and how often users engage, and how much they contribute, you can pinpoint who’s loyal, who’s at risk, and who’s gone silent. It’s simple but effective for linking behavior to ROI and retention strategies. Finally, once you have meaningful segments, classification models can keep them alive. You can train a model to automatically assign new users to the right segment as data flows in, turning segmentation from a static exercise into a living system that adapts as behavior changes.

Are you segmenting users by who they are, or why they use your product? This week I had Nesrine Changuel, PhD on the Product Thinking podcast to discuss her new book, Product Delight. One insight completely shifted how I think about user segmentation. Most teams segment by demographics (age, company size) or behavior (usage patterns, feature adoption). But Nesrine argues the most impactful segmentation is motivational: understanding why users actually choose your product. As she puts it, "it's really important to list both the functional motivators and the emotional motivators so that we can create solutions that honor for both." Two enterprise customers might look identical demographically, but one uses your product to reduce manual work (efficiency-driven) while another wants complete visibility into every process (control-driven). Same demographic, completely different product needs. This connects to her three pillars of delight: removing friction, anticipating needs, and exceeding expectations. When you understand the emotional and functional "why" behind user behavior, you can build features that truly resonate. How are you currently segmenting your users? Are you capturing the motivational drivers that actually influence their decisions?

Your segmentation strategy is probably (read: statistically) wrong. Not because you're targeting too narrowly. Not because you're going too broad. But because you're asking the wrong question entirely. The entire industry is trapped in a false binary: hyper-targeting versus mass reach. Meanwhile, we're hemorrhaging money at both extremes. Narrow segments that can't achieve statistical significance. Broad campaigns that speak to no one. And everyone's pretending their chosen poison is medicine. Here's what nobody wants to admit: We've outsourced our strategic thinking to algorithms that were never designed to think strategically. Consider this uncomfortable truth. Google and Meta's algorithms optimize for users already likely to purchase. Not the ones you could actually influence. They're hitting targets with stunning accuracy while completely missing the point. You're paying premium prices to reach people who were already going to buy from you. The platforms love this. More segments mean more ad spend and more expensive placements. Agencies bill for the complexity. Consultants need problems to solve. Everyone profits from the confusion except the brands actually footing the bill. Segmentation isn't a targeting problem. It's a portfolio management problem. Think about your investment portfolio. You don't put everything in growth stocks or everything in bonds. You balance risk and return across asset classes. Your segmentation strategy should work the same way. 40-50% in core holdings: broad, stable segments that build brand and gather intelligence. 30-40% in growth plays emerging segments with higher risk but transformative potential. 10-20% in speculative positions: experimental micro-segments that teach you what you don't know. 5-10% in hedges: counter-segments that challenge your assumptions before the market does. Those "wasted" impressions on the "wrong" people? They're actually investments that compound over time. TransUnion's research on "Movable Middles" proves this. Brands that identified and targeted these neither-loyal-nor-competitor-loyal segments achieved 9.5x more conversions and 23x ROI in some cases. Not through narrow targeting. Not through broad reach. Through strategic portfolio thinking. The solution isn't better tools or more data. It's accepting that segmentation is a tension to be managed, not a problem to be solved. #MarketingStrategy #Segmentation #DigitalAdvertising #MarketingROI #BrandStrategy #GrowthMarketing #AdTech #StrategicThinking #MarketingEffectiveness

Last week, I said that AI can’t replace marketing fundamentals. Today, I’ll tell you where AI can actually help: 𝐬𝐞𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐛𝐫𝐚𝐢𝐧𝐬𝐭𝐨𝐫𝐦𝐢𝐧𝐠 Let me explain: When I’m working on an unfamiliar category, I might use AI as a bouncing ball. My prompt: What are the possible ways to segment this market? It’ll give me a range of possibilities, likely by: → Company size → Project type (residential, commercial, infrastructure) → Procurement decision-making process → Price sensitivity vs. quality requirements → Order frequency & volume Now I have a starting point. I can decide which angles are worth exploring, then talk to my research partners to validate. This is valuable. AI has compressed what might’ve been hours of whiteboard into minutes. But here’s what AI can’t do: 1️⃣ 𝐊𝐧𝐨𝐰 𝐰𝐡𝐞𝐧 𝐭𝐨 𝐛𝐫𝐞𝐚𝐤 𝐭𝐡𝐞 𝐫𝐮𝐥𝐞𝐬 AI might suggest demographic segmentation for durian buyers. But if you’ve ever worked in this market, you'll know that demographics don't matter - rituals & timing do. 2️⃣ 𝐌𝐚𝐤𝐢𝐧𝐠 𝐭𝐫𝐚𝐝𝐞-𝐨𝐟𝐟𝐬 𝐰𝐢𝐭𝐡 𝐢𝐦𝐩𝐞𝐫𝐟𝐞𝐜𝐭 𝐝𝐚𝐭𝐚 Real-world data is often incomplete / outdated. You need to decide when to stop slicing because too much segmentation can kill scale. 3️⃣ 𝐒𝐞𝐞𝐢𝐧𝐠 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐭𝐡𝐞 𝐧𝐮𝐦𝐛𝐞𝐫𝐬 Sometimes, segments are mathematically clean but strategically useless. Which means that while the segments might look different on paper, they behave the same in the market. And in the real world, business impact > optimised statistics 4️⃣ 𝐒𝐩𝐨𝐭𝐭𝐢𝐧𝐠 𝐭𝐡𝐞 𝐡𝐮𝐦𝐚𝐧 𝐭𝐫𝐮𝐭𝐡 𝐛𝐞𝐧𝐞𝐚𝐭𝐡 𝐭𝐡𝐞 𝐝𝐚𝐭𝐚 Sometimes, a “value seeker” might actually be a “status seeker on a budget” This distinction changes everything - your messaging, your product design and even your pricing strategy. But to recognise this distinction, you need both experience & empathy. AI won’t suffice. * So how do you use AI for segmentation? Use it to: ✓ Brainstorm segmentation approaches you might not have considered ✓ Quickly generate hypotheses to test ✓ Organize your thinking when entering unfamiliar categories DON’T use it to: ✗ Treat the output as your first & final answer ✗ Skip talking to actual market experts ✗ Replace real research & lived market experience * In short, I consider AI to be a great starting point but not an endpoint. Don’t be lazy and think that it’ll do all the heavy lifting for you! ♻️ Reshare this if you found it helpful. 📩 DM me Long Yun Siang if you're working on segmentation and want to leverage the expertise of a real human (I’ve been doing this for 32+ years), DM me. I’d love to help.

“If you’re not thinking segments, you’re not thinking.” - Theodore Levitt Here’s a brief history of market segmentation: 1950s: Segmentation started with basic demographics—age, location, gender—because that was the easiest data to collect and analyze. 1960s: Marketers began adding psychographics, gathering insights into customer attitudes and traits to create more specific profiles. 1970s: The rise of large transaction databases enabled real-time point-of-purchase data collection, leading to segments based on purchase behavior. 1980s: Needs-based segmentation emerged, driven by powerful computers and advanced clustering techniques. This allowed researchers to group customers based on desired product features and benefits. While needs-based segmentation was a step forward, it often missed the mark because customers aren’t product engineers. They struggle to articulate what specific products or features they need. But here’s the thing: Customers excel at describing the outcomes they want to achieve when using a product to get a "job" done. When discussing their desired outcomes, they can identify 100 to 150 different metrics to describe success at a granular level. Today's most effective market segmentation? It focuses on understanding how customers rate the importance and satisfaction of each outcome. This insight allows marketers to craft targeted messages and develop products that resonate deeply with each segment. Here’s 3 examples of Outcome-Based Segmentation in action: 1. J.R. Simplot Company identified a segment of restauranteurs who needed a French fry that stays appealing longer in holding, leading to a tailored product solution. 2. Dentsply found a segment of dentists who believed that the quality of a tooth restoration depended on consistently achieving solid bonds, allowing them to tailor their products to this need. 3. Bosch discovered a segment of drill–driver users who primarily wanted a tool optimized for driving, rarely using it as a drill. This insight helped Bosch create targeted and effective marketing strategies. Outcome-based segmentation represents a significant leap forward. It focuses on real opportunities... ...and measurable activities that are underserved by the competition. Outcome-based segments provide a clear path to innovation and market success.

Are We Segmenting the Wrong Thing? We've been segmenting customers by age, income, and homeownership status for decades. Not because it's the best data. Because it's the easiest data. Here's the problem: a 35-year-old homeowner who hasn't logged into mobile banking in six months is fundamentally different from a 35-year-old homeowner who checks their account daily and just clicked through your HELOC content three times this week. Same demographic bucket. Completely different banking relationship. What if we flipped it? What if we segmented by behaviors instead? By engagement patterns, transaction velocity, channel preferences, and product exploration signals? The data is harder to access. The systems are more complex. The organizational muscle memory fights against it. But the banks and credit unions that figure this out will stop marketing to who customers are and start responding to what customers are doing. They'll send the right offer the week after the third mortgage calculator session. They'll recognize dormancy before it becomes defection. They'll spot the small business hitting a growth inflection and have the banker call before the business owner starts shopping. Demographics tell you who someone is. Behaviors tell you what they're ready to do next. Question for the community: Are we still using demographic segmentation because it's better, or because we've always done it that way? #BankingTransformation #FutureOfBanking #CXStrategy #LeadershipInBanking #CustomerExperience #PersonalizationAtScale #FinancialMarketing Fabio Biasella Joe Dugan Virginia Heyburn

Most of your customers have silently unsubscribed from you in their minds. Every customer-facing team thinks its update is critical. Product has release notes. Marketing has webinars. CS has best practices. But to the customer, it's all vendor noise competing for their limited attention. Given today's tools, customer success teams aren't using segments enough. I'm not talking about your segmentation model. I'm talking about micro-segments based on usage patterns or business challenges or the reason they purchased rather than arbitrary tiers. When you start to use segments in the right way, you can get crystal clear on your comms with 3 questions that cut through the noise: (read these as the customer) 1️⃣. Why should you specifically care about this? 2️⃣. What immediate value will this deliver to your unique situation? 3️⃣. What's the simplest way to realize this value? When you answer these questions for the right micro-segments at the right time, something remarkable happens - customers start listening again. Not because you've found some magic communication formula but because you've finally aligned your message with what truly matters to them. The path to cutting through the noise isn't about shouting louder—it's about speaking directly to what your customers actually care about.

Most SaaS companies are optimizing for the wrong users. They focus on segments with the most people instead of the most value. In our recent segmentation study for a client, we discovered their highest-value users weren't their biggest segment - or their loudest complainers. Instead, it was users who performed 3+ exports per week AND invited 2+ team members within 30 days. This small segment was 4.5x more likely to upgrade to enterprise within 6 months. The uncomfortable truth: Your most valuable segments may not be your largest, loudest, or newest. We've developed a 7-step framework to systematically identify these high-value segments: → Set clear goals beyond revenue → Collect both behavioral and qualitative data   → Use factor analysis to find value drivers → Apply cluster analysis to form segments → Quantify true segment value → Map segments to optimization opportunities The result? Our client shifted from generic improvements to laser-focused optimizations for their power users. Full methodology and case study breakdown in the article below.

𝗧𝗵𝗲 𝗼𝗻𝗲 𝗮𝗻𝗮𝗹��𝘀𝗶𝘀 𝗜 𝗰𝗮𝗻’𝘁 𝗴𝗲𝘁 𝗲𝗻𝗼𝘂𝗴𝗵 𝗼𝗳? Customer segmentation by size, industry, and geography. Why? Because when you stop treating all customers the same, you start growing 𝗳𝗮𝘀𝘁𝗲𝗿, more 𝗽𝗿𝗼𝗳𝗶𝘁𝗮𝗯𝗹𝘆, and with fewer 𝘀𝘂𝗿𝗽𝗿𝗶𝘀𝗲𝘀. This analysis is the unlock for: 📈 Smarter growth strategies 💰 Healthier margins 🤝 Happier customers 𝗪𝗵𝘆 𝘀𝗲𝗴𝗺𝗲𝗻𝘁 𝗯𝘆 𝘀𝗶𝘇𝗲, 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝘆, 𝗮𝗻𝗱 𝗴𝗲𝗼𝗴𝗿𝗮𝗽𝗵𝘆? ✅ 1. Sales & service effectiveness • A $250M CPG distributor in the Midwest doesn’t need or want the same approach as a $7bn manufacturer in Germany. • Segmentation helps you sell and support the right way - for the right customer. ✅ 2. Better strategic & operational decisions • Want to know which customers are high-effort but low-margin? Which industries are expanding the fastest? Which region has the stickiest customers? • Segmentation brings that clarity. ✅ 3. Improved customer experience • Customers don’t expect to be treated equally - they expect to be treated relevantly. • When all your teams understand the nuances of the customer they're serving, retention and satisfaction go up. 𝗛𝗼𝘄 𝘁𝗼 𝗱𝗼 𝗶𝘁 𝘄𝗲𝗹𝗹: 1️⃣ Group customers by: • Size (revenue or headcount) - a useful proxy for complexity • Industry (manufacturing & industrials, tech, services, life sciences & healthcare, CPG, etc.) • Geography (region, market, country) 2️⃣ For each segment, analyze: • Profitability • Support/service effort • Sales cycle and retention • Volumes, expansion or upsell potential 3️⃣ Find your high-leverage segments 4️⃣ Align GTM, finance, ops, and support around them 5️⃣ Refresh regularly - your base will evolve 𝗧𝗵𝗲 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲 • Customer segmentation isn’t just a data exercise. It’s a strategic advantage hiding in plain sight. • When you know who your best customers really are - you build better, sell smarter, and scale faster. #CustomerStrategy #Operations #Finance #Growth #Segmentation #BusinessStrategy #fpanda

The best gift your RevOps team could give you this Christmas is a clear view of account segmentation within your business. It’s a somewhat confusing topic, so let me break it down a bit. Most B2B companies can rattle off their Total Addressable Market (TAM). But far fewer have a clear definition of their Ideal Customer Profile (ICP)—and that’s where the real opportunity lies. TAM is everyone you could sell to. ICP is who you should sell to. Getting specific about your ICP means focusing your time, energy, and resources on the right customers—the ones who bring the most value to your business and who benefit the most from your product. Once you know your ICP, the next step is account segmentation: breaking down your customer base into tiers or personas to refine your approach further. Think of it like sorting through a pile of gifts to find the perfect ones for the people who matter most. This is where metrics come into play. You need to analyze each segment to understand how they behave, how much value they bring, and how costly they are to serve. Here are a few to focus on: • Customer Acquisition Cost (CAC): Which segments are the most expensive to acquire? Are they worth it? • Lifetime Value (LTV): Which segments bring the highest long-term value? • Deal size: Do certain segments tend to bring larger deals that justify more investment? • Sales velocity (sales cycle length): How quickly do segments move through the pipeline? Faster cycles mean faster revenue. • Churn rate: Which segments are the most likely to stay, and which ones churn out too quickly? Based off these metrics, you can prioritize the segments that deliver the best return on your time, energy, and dollars. You’ll be able to craft sharper messaging, target more effectively, and focus your resources where they’ll have the biggest impact. Yes, account segmentation takes time. It requires clean, reliable CRM data and a clear strategy. But the payoff? Massive. You’ll target better, lower churn, and build stronger relationships with the customers who matter most. As you wrap up the year, consider making account segmentation a priority for next year. It’s not the easiest gift to give your business, but it’s one that keeps paying off, year after year. Merry Christmas—and here’s to a successful 2025! 🎄🎁