Customer Segmentation Approaches

An ecommerce company recently approached my team to do an email audit as they were facing challenges with low open and click-through rates. After analyzing their email account, here are our main recommendations to revive their email marketing channel: 1. Strategic Email Segmentation: Currently, your emails lack personal relevance due to a one-size-fits-all approach. This is a crucial area to address. Action Plan: Implement segmentation based on purchase history, engagement levels, browsing behavior, and demographic information. 2. Personalized Content Creation: Generic content won't cut it. Your audience needs to feel that each email is crafted for them. Action Plan: Develop emails specifically tailored to the different segments. This includes curated product recommendations, personalized offers, and content that aligns with their interests. 3. Subject Line A/B Testing: Your current subject lines aren't doing their job. You need to be implementing ongoing A/B subject line tests, as this is low-hanging fruit to improve your open rates. Action Plan: Regularly test different subject line styles and formats to identify what resonates best with each segment. Keep track of the metrics to inform future campaigns. 4. Mobile Optimization: A significant portion of your audience reads emails on mobile devices. Neglecting this is causing a decrease in your email engagement rates. Action Plan: Ensure all emails are responsive and visually appealing on various screen sizes. Test your emails on multiple devices before sending them out. Additional Campaign Strategies We Recommend: - Launch a Monthly Newsletter: This should include new arrivals, style guides, and user-generated content. It’s an excellent way to keep your brand in the minds of your customers. - Seasonal Campaign Integration: Tailor your campaigns to align with holidays and seasons. This approach can significantly boost engagement and sales during key periods. - Re-Engagement Campaigns: Specifically target subscribers who haven't interacted with your brand recently. Offer them unique incentives to rekindle their interest. Next steps: 1. If you found this helpful, please leave a comment and let me know. 2. If you own/run/work at an Ecommerce company doing at least $1 million in annual revenue, message me so my team can audit your email channel to see if there's a good fit for working together.

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 I was running ABM at a fast-growing security company (like Wiz, Snyk, or Netskope), here's how I'd avoid wasting money on bad-fit accounts. 👇 AI Segmentation. Most companies segment by industry. They say something like: "We target Tech, Retail, and Hospitality companies with 1,000+ employees." Motel 6 and Airbnb show why this breaks. Same firmographic profiles. But very different business situations, needs, and priorities when it comes to information security (or any tech purchase). You wouldn't sell to them the same way. AI Segmentation helps you uncover and target the highest value segments for your business, beyond basic industries. Here's how I would do this for a security company: 1.) Segment on business situation (not industry). -- Analyze your best customers (high NRR, high ACV). -- Group by specific situations that align to your value prop. e.g. Security Maturity Level, Security Use Cases, Compliance Sensitivity, etc.  -- Find the *natural* clusters based on value, not generic industry labels. 2.) Identify segments with AI. -- Use Keyplay AI to categorize every account in your market. -- Backtest segments against historical data to find which segments have the highest NDR, ACV, and Win Rates. -- Find new ICPs, outside generic vertical groups. 3.) Action the data -- Create ABM plays at intersections with highest win rates. -- Develop content specific to each segment combination (e.g., "Cloud Security for Advanced DevSecOps Teams in Retail") -- Refine your segmentation models as you grow. This process can reduce non-ICP Spend (waste) by 20-30% and help you find thousands of net new target accounts. Don't just throw your budget at industries. Find the segments where your solution resonates most, where you win often, win fast, and win big. That's strategic segmentation. p.s. If you want me and my team to kick-start this process for you, we're offering a free strategic segmentation analysis to CMOs at SaaS security companies with >$20M ARR. Get your report here --> https://lnkd.in/gMezS4Zk #ABM #ICP

𝐒𝐮𝐛 𝟏𝟎% 𝐨𝐩𝐞𝐧 𝐫𝐚𝐭𝐞𝐬 𝐚𝐧𝐝 𝐮𝐧𝐝𝐞𝐫 𝟎.𝟓% 𝐂𝐓𝐑. 𝐀 𝐧𝐢𝐠𝐡𝐭𝐦𝐚𝐫𝐞 𝐟𝐨𝐫 𝐦𝐚𝐫𝐤𝐞𝐭𝐞𝐫𝐬 𝐭𝐫𝐲𝐢𝐧𝐠 𝐭𝐨 𝐝𝐞𝐩𝐥𝐨𝐲 𝐜𝐚𝐦𝐩𝐚𝐢𝐠𝐧𝐬 𝐚𝐧𝐝 𝐨𝐟𝐟𝐞𝐫𝐬 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐞𝐦𝐚𝐢𝐥. Something had to change! Here’s what we did 🌟  ↴ Community Insights: ↳I reached out to operators in ClubPF ⚓︎, Pavilion, and other communities, and LinkedIn to learn from their email campaign strategies. Focused Segmentation: ↳ We noticed that the top marketers and ops professionals were creating highly engaged audience segments. We reduced our audience size by 90%, focusing on: → Last email opened from sales/marketing → Recent website visits → Event registration/attendance → Asset downloads or form submissions Building Engagement: ↳ We enriched contact details and evaluated titles, companies, goals, challenges, and content engagement across emails and our website. Gathering Feedback: ↳ We contacted 20+ engaged contacts to understand their email preferences, knowledge gaps, and content consumption habits. Strategic Expansion: ↳ We expanded our list by 5-10% weekly, monitoring performance closely. Within 3 weeks, we saw: 20%+ open rates 1.5%+ CTRs By the end of the quarter, our segmented email campaigns achieved a 30%+ open rate! Key Takeaways 💡: 👉 People-First Approach: Engage internally with the best team and externally with your audience. 👉 Data-Driven Decisions: Use engagement signals to create focused segments. 👉 Continuous Improvement: Regularly gather feedback and adjust strategies. ✨ This journey was about putting people first, aligning our team, and delivering value to our audience. The results speak for themselves! S/o to Ritakshi J. Sagar Mishra and Soumyajit Chakladar - we worked week after week to make this happen! Picture Context - Winter in Boston in 2010. Sometimes this is what being a GTM Operator feels like 🤣 #EmailMarketing #GTM #datadriven

10 customer segments that hurt your margin — and how to spot them ⤵️ Most segmentation playbooks obsess over “best customers” and “win-back lists.” Cute. But there’s another side of the map: people who quietly eat your margin. They spam support, ...leave 1-star reviews, ...chase promo codes, and turn your store into a fitting room. Let’s talk about the customers who ruin the party. 1. Discount Hunters Buy only with promos/sales 2. Serial Returners Buy often, send a lot back 3. Bracketers Order many variants, keep just 1 or 2 4. Single-SKU Repeaters Rebuy the same lead product in bulk, ignore the rest of the catalog 5. One-and-Done Buy once and vanish 6. Support-Heavy Lots of support requests for little revenue 7. Welcome-Abusers Farm “first-order” perks with new IDs 8. Cancel-Happy Place orders then cancel or no-show 9. Loyalty Gamers Buy mainly when points are available 10. 1★ Amplifiers Post lots of harsh reviews, spend little Not every customer is a good customer. Some groups look active but quietly cost more than they bring. That’s why segmentation isn’t just about VIPs — it’s also about naming the segments that drain margin and setting clear boundaries for them.

Why your “persona” is sabotaging your marketing strategy 😫 When creating a marketing strategy, marketers often talk about doing a segmentation exercise to know who your target segment is. I have no issue with this. What I DO have an issue with is when people equate segmentation with constructing a “persona” of your target market such as: “Meet Amy Tan - she’s 35, lives in Bangsar, drinks oat lattes & scrolls Instagram for an hour looking at dog reels and recipes before bed”. I think it’s rubbish. Fictional Amy Tan tells me nothing about the actual market segment! Will “Amy Tan” tell me anything about how and how much Amy's segment spends on eggs every month? Will I understand the purchase frequency for Amy’s segment? Know what specific product features drive their buying decisions? Whether Amy's segment prioritises particular features, e.g. eggs that have omega-3, vitamin E, or selenium content? How often do they buy eggs, and what triggers that purchase? NO, I won’t, which is dangerous because these are the very questions that you should be answering in a segmentation exercise. Without a solid answer, you’re operating on ‘gut feel’. That’s not creating a ‘marketing strategy’; that’s just pure guesswork! Now you might ask, what does true segmentation look like? For starters, true segmentation goes beyond the demographics & psychographics to include segment size, category spending, growth rates, and behavioral patterns. When you understand that Segment A spends $200 monthly while Segment B spends $50 with declining interest, you can make informed decisions about where to focus. This foundational work enables everything strategic that follows: pricing structure, distribution channels, messaging strategy & product development priorities. It tells you not just who your customers are, but how much they're worth and what motivates their purchasing decisions. Sadly, most companies skip this rigorous analysis because it's "simple but not easy." They default to personas because they feel more tangible & creative. But personas without solid segmentation data underneath are just elaborate guesswork. Meanwhile, companies that invest in proper segmentation research - using methodologies like latent class analysis to identify distinct consumer groups based on category motivators, purchase behavior & spending patterns - gain a competitive advantage that compounds over time. They know which segments to target, what products would fit & what messages will resonate. The irony is that this foundational work, while requiring upfront investment, actually makes all subsequent marketing decisions faster and more effective. You stop debating which target is priority based on gut feelings & start making decisions based on market reality. But you must first do your segmentation exercise properly. ♻️Reshare to help someone in your network. DM me your company needs help building an effective marketing strategy.

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.

Confession: I used to suck at analyzing customer data. Until I stumbled on the missing ingredient: The art of segmenting data in ways that matter. 𝗘𝗫𝗔𝗠𝗣𝗟𝗘 1 While working as a PM for an applicant tracking software, I tried to understand trends in the number of jobs each company account published. I hypothesized that the more recruiters a company has, the more jobs it would likely post. I was wrong. Companies with 3-5 recruiters were showing super high counts as well. Why? Answer: Hiring agencies. Hiring agencies were power users where a single recruiter would post at a much higher rate than a normal company. We segmented the data by company industry and type (agency vs. non agency) to get more meaningful benchmarks. 𝗘𝗫𝗔𝗠𝗣𝗟𝗘 2 Some time ago, conversion rates on the vFairs marketing site were dipping. We segmented by page type and identified that the blogs weren't converting as well as before. We started ideating solutions to fix this. But before we went too far, we pulled out another slice. We further segmented the blogs by funnel stage (top, middle, bottom). Turned out, the the CVR on bottom-of-funnel ones were on the rise. The overall CVR was dropping because we had just been writing more top-of-funnel which, to be fair, wasn't a conversion point per se. That sliced view saved us from considering a page revamp. A couple of other realizations: [1] 𝗘𝘃𝗲𝗿𝘆 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗵𝗮𝘀 𝗮 𝘀𝗲𝗴𝗺𝗲𝗻𝘁 𝘂𝗻𝗶𝗾𝘂𝗲 𝘁𝗼 𝘁𝗵𝗲𝗺 𝘁𝗵𝗮𝘁 𝗶𝘀 𝘃𝗲𝗿𝘆 𝗿𝗲𝘃𝗲𝗮𝗹𝗶𝗻𝗴 Ex: I imagine segmenting merchants by order volume at Shopify or by Use case (sales proposals, contracts, HR docs) for Pandadoc would elicit key insights. [2] 𝗣𝗠𝘀 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗽𝗹𝗮𝘆 𝘄𝗶𝘁𝗵 𝘀𝗶𝗺𝗽𝗹𝗲 𝗮𝗻𝗱 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝘀𝗲𝗴𝗺𝗲𝗻𝘁𝘀 Simple segment = 1 parameter Complex segment = Multiple parameters at play (e.g. geo, tier, industry) I've been in situations where traffic or engagement rose across the board for simple segments. The right complex segment would often explain the anomaly. At the same time, over-segmentation is a curse. It can nullify your analysis due to sparse data slices. -- For most PMs and PMMs, becoming data-informed is a journey. I'd just recommend making segmentation the first pitstop.

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

👉 Pseudo Segmentations are Hazardous Waste Time for some marketing detox. If you’re still using personas, milieus, and archetypes in 2025, it’s time for a marketing detox and get rid of everything that doesn’t serve you. Pseudo segmentations should be at the very top of your “let go of what doesn’t serve me” list. Why? All these "segmentations" look pretty on slides, but don’t meet the most basic criteria for business relevance: (1) Value-based: They don’t tell you who drives revenue/profit. (2) Actionable: They can’t shape pricing, product, or distribution. (3) Strategic: They don’t unlock a competitive advantage. And worse: They give you a false sense of knowing your consumers when you’re relying on something as useful as toe juice AND block you from doing something truly impactful: real, value-based segmentation, which is the lever for profitable growth. That’s too much bogus, and five strong reasons to ditch them forever. 👉 What to do instead: Value-Based Segmentation Ditch the pseudo and start with value-based segmentation the foundation of your marketing strategy. This is exactly what we use in "How Small Brands Grow - A replicable framework for brand growth" designed for start-ups, scale-ups, and mature businesses. Here’s how it works in a nutshell: [1] Understand Profit Pools / Value / Cost - Size of segments - Value of segments - Growth rates - Repeat behaviour / loyalty - Acquisition cost / customer value [2] Build segments based on business value - Who are the segments with real upside potential? - What do they need? - Where are your right-to-win opportunities? - How can you address them? [3] Align 4Ps to segment needs Design pricing, product, promotion, and channels to win the most valuable and addressable segments. Bottom line: Want real growth? Dump pseudo segmentations Start with value-based segmentation Build your strategy on it Now, let’s all take a deep breath, hold hands, and say it together: “Oooohm… let’s make marketing a better place.” --- PS: If you’ve warmed up with segmentation and want to continue your marketing detox (grab a matcha first), tackle these next: Short-term performance > long-term brand Forgotten 4Ps KPI mess --- ♻️ Your network appreciates a repost. 👉 If you’d like the PDF of the "How Small Brands Grow" deck, just comment “oh yes” below. --- Frederic Fernandez