Customer Segmentation for Improved Engagement

POST-4/7👉 Email used to be a megaphone. In 2025, it’s a whisper in a very specific ear. Gone are the days when “blast to all” could pass as a strategy. In fact, that approach in 2025 is actively hurting your deliverability. Email Service Providers (ESPs) like Gmail, Yahoo, and Outlook are no longer just evaluating your IP health—they’re scoring your sender behavior at the recipient level. That means if 40% of your list is cold or disengaged, Gmail sees you as the problem—not just the user. ⚠️ Real Consequence: 1. We audited an ecommerce fashion brand with 220K contacts. Over 92K of them hadn’t clicked a single email in 90+ days. Gmail flagged them for bulk spam behavior, and inboxing fell from 78% to 46% overnight. 2. They were running promos weekly. Nothing was technically broken—but nothing was relevant. That’s what got them crushed. What Micro-Segmentation Solves in 2025: ✅ Reduces spam complaints ✅ Increases engagement velocity ✅ Signals positive intent to inbox providers ✅ Unlocks higher revenue per send with smaller cohorts Micro-Segmentation Tactics That Work Now: 1. Behavior-Based Journeys: Forget static tags. If someone viewed winter boots but didn’t buy, your next 3 emails better talk about warmth, snow, or style—not your general spring lookbook. ✅ Klaviyo + Shopify data lets you trigger flow branches based on: Last viewed product category Cart abandonment by SKU group Pages viewed in session (via UTMs or on-site behavior) Pro Tip: Use dynamic content blocks inside campaigns to adjust hero sections based on browse activity without cloning entire flows. 2. Lifecycle Automation by Spend Velocity This isn’t “new vs returning” logic anymore. In 2025, flows shift based on: Time since last order AOV trends SKU replenishment cycles Example: First-time customer who hasn’t returned in 30 days → “2nd purchase incentive” High-value buyer within 7 days → “VIP early access” Customer inactive 60+ days → Winback + dynamic offer block + channel sync suppression 3. AI-Supported Clustering Tools like RetentionX, Lexer, and even Klaviyo’s predictive analytics are now building multi-dimensional customer clusters using: Purchase frequency Channel source Time to second order Category loyalty It’s loyal mid-value buyers who shop monthly but only when free shipping is offered. ✅ What to do: Export these clusters to your ESP Build messaging that maps exactly to their past actions Suppress low responders from paid channels and warm email instead. Ready to Execute? Create 5 foundational micro-segments: 1. High spenders 2. First-time buyers 3. VIPs (CLV > 2.5x avg) 4. Dormant >90 days 5. Active clickers, no conversion Test 2 cadences per segment: VIPs: 4x/month + early access Dormant: 1x/month reactivation with content—not promos Use Recency, Frequency, and Monetary score buckets to tag customers and let your automations react to movement between them. #EmailMarketing #email

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.

𝗧𝗵𝗲 𝗼𝗻𝗲 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 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

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

The hardest lesson in Customer Success? Not every account needs the same attention. I've seen too many CS teams burn out trying to give white-glove service to every single customer. Meanwhile, their highest-value accounts aren't getting the strategic partnership they need to expand. Here's the framework that works for me: 📍MAINTAIN (Low Risk, Low Value) Your efficiency plays. Automated onboarding, self-service resources, and health-check emails. Keep them healthy without burning CSM hours. 📍RETAIN (High Risk, Low Value) Your fire drills. Rapid risk diagnosis, short-term recovery plans, executive escalation. Get them stable or let them go gracefully. 📍EXPAND → 𝐇𝐢𝐠𝐡 𝐕𝐚𝐥𝐮𝐞, 𝐋𝐨𝐰 𝐑𝐢𝐬𝐤 Your growth engine. This is where the magic happens -QBRs, strategic roadmap discussions, champion programs, and co-marketing opportunities. → 𝐇𝐢𝐠𝐡 𝐕𝐚𝐥𝐮𝐞, 𝐇𝐢𝐠𝐡 𝐑𝐢𝐬𝐤 Your rescue missions have a massive upside. Jump in fast, diagnose issues, build recovery plans, then shift to expansion mode. 𝐌𝐚𝐭𝐜𝐡 𝐲𝐨𝐮𝐫 𝐂𝐒 𝐢𝐧𝐯𝐞𝐬𝐭𝐦𝐞𝐧𝐭 𝐭𝐨 𝐭𝐡𝐞 𝐚𝐜𝐜𝐨𝐮𝐧𝐭'𝐬 𝐯𝐚𝐥𝐮𝐞 𝐚𝐧𝐝 𝐫𝐢𝐬𝐤 𝐩𝐫𝐨𝐟𝐢𝐥𝐞. Your CS team shouldn't be stretched thin - they should be strategically deployed. What's your approach to CS segmentation? Drop a comment - I'd love to hear what's working (or not working) for your team

“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.

I’m increasingly convinced that what separates good Customer Success from great Customer Success is this: The ability to predict customer behaviour… and act on it correctly. I read a brilliant post from Covenant Obioma (CCSS, CCSE) yesterday. She shared how Duolingo sent her a funny email begging her to come back after months of inactivity. It caught her attention. It made her smile. But it didn’t make her return. Why? Because the message didn’t meet her where she was. Technically, Duolingo wasn’t wrong. She could start a lesson. But her relationship with the product had changed. If the message had aligned with her current context maybe learning a new skill, exploring something new, or a lighter re-entry she said she would have clicked. This is the part most teams miss. Duolingo didn’t fail because the copy was bad. They failed because the signal was shallow. They treated inactivity as one thing. Instead of asking: what type of inactivity is this? And that’s where segmentation comes in. Good Customer Success isn’t about reacting to behaviour. It’s about understanding what that behaviour means. Two customers can look identical in your data inactive for 90 days but be in completely different places: • One is overwhelmed • One has outgrown the use case • One is waiting for a new reason to care • One is simply done Sending the same message to all of them is lazy. And expensive. This is why CS motions can’t be based on gut feel or generic rules. They have to be empirical. Consistently tracked. Continuously refined. Backed by both qualitative insight and quantitative data. Proper segmentation helps you answer better questions: Not just “What did they do?” But “Why did they do it?” And “What’s the most sensible next action now?” So when a customer exhibits a behaviour don’t rush to trigger a playbook. Pause. Ask what it signals. Ask what changed. Ask who this customer is now. That’s how you stop pushing the “right” message at the wrong time and start guiding customers forward instead.

I grew my client’s best email month by 148% from €525K to €1,3M. Here’s how I did it: When I first took on this client, I loved his vibe and product. But it was tricky… • They only sell 1 product • My client hates being salesy • They sell in multiple countries using native languages Step 1: Audit Every client I take on goes through a thorough list audit. I audited his list with my 66-point Klaviyo checklist to: • Identify what’s leaking revenue • Pinpoint what’s working to double down • Develop a strategy plan to increase his sales Once done and discussed, we got to work on: Step 2: Building and optimizing flows He had multiple flows set up but only his welcome flow was good. The rest was non-existent or only had 1-2 basic emails. So we redid his flows. When I was done, we instantly saw an increase in email revenue. The secret? I used founder-led content. The emails feel like a 1:1 convo, and most of my time went into the copy because… Design attracts but copy sells. The next step was… Step 3: Proper segmentation For a brand that sells one product, it’s easy to neglect the ones who’ve bought. (especially since it’s not a replenishable product) But my client is working on new products. Neglecting anyone would be a HUGE mistake - it always is. Here’s how I segmented: • Language • Engaged vs non-engaged • Customer vs non-customer All emails are properly targeted and personalized with the right intention. It makes people feel like we’re talking directly to them. But this wasn’t enough as… Step 4: Ramp up campaign volume They weren’t sending campaigns which meant a lot of revenue was left on the table. I started with re-engagement campaigns to identify the engaged segment. (if you don’t know this your deliverability will go down the drain) Then I split the engaged segment into: • Non-customers • Everyone That’s how I ensure we don’t send emails that aren’t relevant to the reader. The more campaigns you send, the more touch points and familiarity you create. Which leads to closer relationships and more sales. Many eCom founders are scared of being annoying if they send too many campaigns. If you only send: 1/ Sales and discounts 2/ Things that people don’t care about Then yeah you’re being annoying. But if you do email marketing the quiet way, people want to hear from you. It wasn’t all sunshine and rainbows. This job was brutal. But our hard work paid off because we… ↳ Topped his best sales day in Oct ↳ Grew total revenue by 69% to 4.51M ↳ Beat his best email month by 148% to 1.30M ↳ Grew campaign revenue from 0 to 414k (in Oct) ↳ Involved his customers in his new product development process And the best part? I’ve only been working with them for 3 months. We’re just getting started. -- If you’re an eCom owner who wants to scale your revenue: I’m looking to work with 2 eCom brands to help them increase their email revenue in the next 60 days. DM me “email” and I’ll get you the details.

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.