Understanding User Segmentation In Subscription Models

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.

you're a pm at nykaa. repeat purchase rate dropped from 42% to 28% in the last quarter. what do you do? step 1: understand what "repeat purchase dropped" means → are customers buying once and never coming back? → or are loyal customers buying less frequently? → or are you acquiring a flood of low-intent new users who were never going to repeat? → repeat rate = users with 2+ orders / total users. a spike in one-time buyers tanks this number even if loyal customers didn't change. step 2: segment the drop → by cohort: did jan signups repeat less than oct signups? or is every cohort declining? → by category: is it makeup? skincare? haircare? if skincare held but makeup dropped, it's category-specific, not platform-wide. → by acquisition source: did users from instagram ads repeat less than organic users? → by first purchase: users whose first order was a Rs 200 lipstick vs. Rs 2000 skincare set have very different repeat behavior. step 3: check what changed recently → did you run a massive sale last quarter? heavy discounting attracts deal hunters who buy once at 60% off and never return at full price. your "growth" killed your repeat. → did you change the post-purchase email flow? → did subscription/auto-replenish options break or get removed? → did loyalty points expire or devalue? if 1000 points used to = Rs 100 off and now it's Rs 50 - repeat motivation drops. step 4: dig into the post-purchase journey → what happens after someone's first order? → check: do they get a personalized recommendation within 7 days? or silence until the next sale blast? → check: what % of first-time buyers opened the app again within 30 days? → check: of those who came back, what % added to cart but didn't purchase? price sensitivity or just browsing? → the gap between "came back to browse" and "came back to buy" is where the answer is. step 5: form a hypothesis example: if repeat purchase dropped specifically among users acquired during the big billion sale whose first order was under Rs 300 using a 70% off coupon → hypothesis: discount-led acquisition brought users who anchored to sale pricing. at full price, nykaa feels expensive compared to meesho or amazon beauty. → test: for first-time sale buyers, send a personalized "second purchase" offer within 14 days - not a flat discount but a bundle: "complete your skincare routine: cleanser + moisturizer at 20% off together." → measure: 60-day repeat rate for this cohort vs. control over 8 weeks. the answer is not to run another sale. the answer is understanding who you attracted and why they didn't stay. what would you fix first? #productmanagement #productsense #nykaa #d2c #retention #pminterview

Most CS teams segment customers the wrong way They start with the obvious categories: Company size Industry Business model It looks neat on a dashboard. But it doesn’t help you drive outcomes. If you want segmentation that actually reduces churn, improves adoption, and makes your work as a CSM ten times easier… You need to build it from the inside out. Here are the 5 pillars every winning customer segmentation stands on: 1️⃣ GOALS – The Why Every customer buys your product for one main reason: ✅ Save time ✅ Save money ✅ Make more money Your job is to define the specific version of that for their business. Reduce payroll errors Speed up onboarding Increase conversion rates Shorten reporting cycles This is always your first segmentation layer. Everything else sits on top of this 2️⃣ USE CASES – The How Two customers with the same goal may use your product in completely different ways. So you segment by: 👉🏾 The features they rely on 👉🏾 The workflows they’ve built 👉🏾 The level of complexity 👉🏾 Their definition of success This is where misunderstandings vanish. It explains why one customer thrives while another struggles with the same tool 3️⃣ REVENUE VALUE / ARR TIER – The Worth Not every customer needs equal attention. This layer helps you prioritise: Contract value Strategic importance Revenue model (subscription, usage, hybrid) it tells you where to invest your time without burning out 4️⃣ MATURITY LEVEL – The Readiness A customer’s ability to succeed depends on their operational sophistication You measure this through: Team size Tech stack Data hygiene Internal processes Change management ability Experience with similar tools A mature customer moves fast. A less mature customer needs heavier support and tighter handholding 5️⃣ LIFECYCLE STAGE – The When Where they are in the journey changes what they need from you Onboarding Adoption Value realisation Renewal Expansion At-risk This gives you context. The right action at the wrong time becomes the wrong action Most teams start with external labels. But the strongest CSMs segment by what actually drives outcomes: Why they bought How they use the product How ready they are What they’re worth Where they are today Start here, and everything else gets clearer: Playbooks Health scores Prioritisation Renewal strategies Expansion opportunities These 5 pillars are how early-stage SaaS teams build segmentation that actually works in the real world 👉🏾 If you like this post, you'll love my newsletter. Subscribe on my profile

Labeled “mix-shift,” “price-volume,” or “mix-rate” analysis, a common analysis challenge is understanding whether metric changes are driven by segment mix shifts or by changes in segment behavior. This shows up everywhere—from growth metrics like conversion or retention rates, to price-volume analysis in logistics, to margin % calculations. Take a logistics provider expanding into new regions with lower-margin profiles. Overall margin % is declining, but how much is due to the expansion strategy? Ideally, software could automatically calculate and attribute this impact. In Trace, the “Size” column isolates the impact of changing segment sizes, while “Behavior” captures changes in segment performance. Inspecting the top markets, you quickly see how growth in lower-margin markets like Dallas and Memphis fails to offset declines in higher-margin markets like LA and Phoenix. A second example: a subscription business tracking order frequency by user mix. The size column reveals that declines in new user cohorts (0–4 and 4–12 weeks) are driving the net negative impact, while older cohorts (52+ weeks) aren’t increasing their ordering enough to compensate. This is just one of many analyses HelloTrace delivers out of the box, taking the heavy lift off users’ plates. If you save 4 hours per unit analysis, imagine how many more hypotheses you could explore!

Autodesk saw subscriptions increase 11% by understanding that there is no singular user. Most companies optimize for 'the user.' They treat everyone the same because they land on the same page. That's a problem. Autodesk came to us with high cart abandonment rates. We analyzed their data and found two completely different audiences using the same website: AUDIENCE 1: High Opportunity ↳ Users arrived via organic search. In research mode, with low product understanding, leading to long sessions exploring options. AUDIENCE 2: High Intent ↳ Users came back via paid search on desktop. They knew what they wanted, just needed confirmation, then were ready to convert. Same website. Opposite needs. Once we segmented by actual behavior instead of demographics, everything shifted. We knew which questions to ask in user testing. We understood how each group used the homepage differently. How they searched. What they needed from product pages. The insights became specific. The recommendations became targeted. Autodesk implemented our roadmap... and subscriptions increased 11% year over year. Your analytics also contain patterns to help you better define your users. Traffic source, session duration, return behavior, device type... these all signal reveal intent. Stop treating everyone the same because they land on the same page. Segment by behavior, and solve real problems for real people. ____ Wondering if user segmentation could unlock double-digit conversion growth for your business? Apply for a strategy call: https://hubs.ly/Q03S5Z1k0

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

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?

How to Monetize Digital Products Using the 𝗪𝗵𝗮𝗹𝗲 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵? While reading the latest report by IndiaQuotient , I came across an interesting insight . How Dream11 is scaling its revenue using what’s called the Whale Approach. That sparked our curiosity—and led us to dive deeper into this strategy and share our findings in this post & our blog. Here’s the core idea: 𝗡𝗼𝘁 𝗮𝗹𝗹 𝘂𝘀𝗲𝗿𝘀 𝗰𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗲 𝗲𝗾𝘂𝗮𝗹𝗹𝘆 𝘁𝗼 𝗿𝗲𝘃𝗲𝗻𝘂𝗲. In fact, 𝟭-𝟱% 𝗼𝗳 𝘂𝘀𝗲𝗿𝘀 can generate nearly 80% of the revenue. The classic Pareto Principle at play but more skewed. Yet, many digital businesses still treat all users the same—leading to wasted budgets, scattered marketing efforts, and missed opportunities to nurture high-value customers. This is where the Whales, Dolphins, and Minnows framework comes in—a powerful user segmentation model: 🐟 𝗠𝗶𝗻𝗻𝗼𝘄𝘀 – The majority, but low spenders 🐬 𝗗𝗼𝗹𝗽𝗵𝗶𝗻𝘀 – Mid-tier users who engage and spend consistently 🐋 𝗪𝗵𝗮𝗹𝗲𝘀 – Power users who drive the lion’s share of revenue Adopting this framework helps brands to: ✅ Optimize marketing spend by focusing on high-LTV users ✅ Boost retention with tailored engagement strategies ✅ Improve monetization through differentiated pricing & offers Unlike one-size-fits-all growth campaigns, this data-backed, segment-first approach enables sustainable and efficient revenue growth. We’re seeing this model work effectively across SaaS, mobile gaming, eCommerce, edtech, and subscription-based services. But here’s the real learning for me was: 𝗧𝗵𝗲 𝗪𝗵𝗮𝗹𝗲 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝗶𝘀 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗮 𝗺𝗼𝗻𝗲𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗺𝗼𝗱𝗲𝗹—𝗶𝘁'𝘀 𝗮𝗻 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗮𝗻𝗱 𝗿𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻-𝗳𝗶𝗿𝘀𝘁 𝗺𝗶𝗻𝗱𝘀𝗲𝘁. Curious to learn more? We’ve broken this down with practical examples and insights in our latest blog on DAiOM (link in comments). Would love to hear your thoughts—are you already using this kind of segmentation? And, What’s worked for you?