Customer Segmentation Reports

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

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

You can't treat every customer the same. Does every customer deserve a great experience? Absolutely.  Should every customer have the same engagement model? Absolutely not. I said it. I'll die on this hill. Something I've seen in many Series A/B companies, the customer engagement model is the same for a $5K customer as a $500K customer. Same onboarding. Same check-in cadence. Same QBR format. Small customers are over-serviced—too many meetings, too formal for their needs.Teams are on a literal hamster wheel. Large customers are under-served—not enough strategic partnership, you can’t get the execs into a conversation because you’re not having the right conversations or delivering the right value. CS teams are exhausted trying to be everything to everyone. And efficiency is in the toilet. This approach isn't sustainable but many companies default into it while waiting for the “right” time to tackle it. Ready is a decision, not a feeling. Every customer deserves the right engagement model to maximize the value of their investment in your product. But that model has to vary. Different customer sizes, complexity levels, maturity stages, and industries have fundamentally different needs. And economically, it doesn't make sense to deliver the same experience across the board. When you get honest about segmentation, everything changes. In one company I worked with, this is how we approached the first phase of segmentation—we kept it simple: Strategic Accounts (Top 20% of ARR): Named CSM with <30 accounts. Quarterly business reviews with executive sponsors. Custom success plans tied to their business goals. Proactive roadmap discussions. Growth Accounts (Next 30% of ARR): Named CSM with ~60 accounts. Digital engagement supplemented with personal touch. Bi-annual strategic check-ins. Standardized playbooks with customization. Scale Accounts (Remaining 50% of ARR): Pooled support with specialized experts. Digital-first engagement. Automated health monitoring with human escalation when triggered by risk or opportunity. We made the changes and we made no excuses. Customers appreciated the honesty. In the company I mentioned above, customer satisfaction improved across ALL segments. Strategic account retention hit 97%. Scale account retention improved from 86% to 91%. CS costs as a percentage of revenue dropped 35%. CS team engagement scores went up. They were no longer context switching all day every day. Your customer engagement model should be developed and iterated based on what actually works for each customer group. Segmentation isn't about treating customers unfairly—it's about serving them appropriately so each one can achieve maximum value. The model you design today won't be the model you need in 18 months. Customer mix changes. Product evolves. Market shifts. Your engagement approach has to evolve with it. #CustomerSuccess #CustomerExperience #CustomerJourney #RevenueGrowth

Segmenting your customers is the easiest lever you could pull to maximize profit. There are 5 segments you need specifically: 1/ First-time buyers: They need reassurance they made the right choice. Send: - How-to guides - Care instructions - Gentle intro to other products Don't push hard sells immediately 2/ Repeat customers (2-3 purchases): They trust you but need reasons to buy more. Send: - Early access to launches - VIP pricing - "Since you loved X, try Y" 3/ VIP Customers (4+ purchases or high spend): These are your brand advocates. Send: - Behind-the-scenes content - Ask for product input - Exclusive experiences - Personal touches 4/ Lapsed Customers (90+ days inactive): Remind them why they loved your brand. Send: - "We miss you" campaigns - Compelling reason to come back - Special win-back offer 5/ High-Value Segment (Top 20% spenders) Send: - Premium customer service - Exclusive access - Personal check-ins I implemented this for a supplement brand with 25K customers. Result: Email revenue increased 160% in 90 days.

As promised, I’m sharing the first project from my series on the Top 5 Data Science Projects that every data scientist should tackle to gain a deeper understanding of the retail industry: Customer Segmentation using the K-means approach 🛒 Before diving into my approach, let’s first explore why customer segmentation is so important for retail businesses. Customer segmentation empowers businesses to better understand their customers, personalize experiences, and optimize strategies. For retail, this is key as it allows companies to: a. Target Specific Customer Groups: Customize marketing campaigns, products, and promotions based on customer preferences and behavior. Improve Retention: Identify loyal or high-value customers and offer personalized rewards to encourage repeat purchases. b. Optimize Resources: Adjust inventory, staffing, and other resources based on the distribution of customer segments. c. Create Personalized Experiences = Higher Conversions: Tailor experiences for different customer groups, leading to increased sales and customer satisfaction. The Approach In this project, I used Machine Learning to segment customers based on their purchasing behavior with the KMeans clustering algorithm. The goal was to identify patterns in customer data and classify them into meaningful segments for targeted strategies. Here’s a quick breakdown of the approach I followed: 1. Data Collection: I used retail transaction data from the UCI Machine Learning Repository. 2. Preprocessing: Cleaned and transformed the data, handled missing values, and dealt with outliers. After that, I standardized the features through scaling. 3. Clustering: I applied the KMeans clustering algorithm to group customers into 3 distinct segments, based on their RFM model (Recency, Frequency, and Monetary value): Recency: How recent was the customer’s last transaction? Frequency: How often does the customer make a purchase? Monetary value: How much has the customer spent in total? 4. Visualization: I visualized the segmentation using box plots, elbow curves, and cluster snapshots to better interpret the patterns within each group. Explore the Full Project Check out the full project on GitHub, where I’ve shared the code and detailed steps for replicating the analysis: Link to GitHub Project- https://lnkd.in/gKFXkFN2 Also sharing some cluster visualizations snaps below to see the results of the segmentation! ✨ Stay tuned for the next project in this series! I’ll be diving deeper into more advanced data science techniques that drive success in the retail industry. Don't miss out—follow me to get notified! #CustomerSegmentation #DataScience #MachineLearning #KMeansClustering #RetailAnalytics #DataScienceInRetail #CustomerInsights #DataAnalysis #RFMModel #MarketingOptimization #RetailStrategy

I wanted to refresh my data science skills with something practical, not just another tutorial notebook. So I built an end-to-end customer segmentation project using a real e-commerce behavioral dataset (millions of events from 2020–2021). What I did step by step: • Filtered raw clickstream data down to purchase events • Engineered RFM features (Recency, Frequency, Monetary) per customer • Scaled features and used K-Means clustering • Chose the number of clusters using elbow + silhouette analysis instead of just guessing • Profiled the clusters into high-value, at-risk, new/occasional, and low-value segments • Built a Tableau dashboard on top so a non-technical stakeholder can actually use it Why I like this project: • It starts from messy behavioral data, not a tiny toy dataset • It focuses on customer value and retention, not just “does the model run” • It ends with a dashboard that could realistically be handed to marketing / CRM teams GitHub repo (code + notebook + README): https://lnkd.in/gTQN7PSt Tableau dashboard: https://lnkd.in/geFsSv6P Next step: I want to extend this into churn / retention modelling and keep building a small portfolio of projects like this. If you work in data science, analytics, or marketing analytics and have ideas for what you’d add or change here, I’d love feedback. #datascience #analytics #machinelearning #tableau #customersuccess #segmentation

📊 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗔𝗕𝗖 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗦𝗲𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 An ABC analysis helps identify which 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀, 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀, 𝗼𝗿 𝗼𝘁𝗵𝗲𝗿 𝗱𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝘀 drive most of your revenue or margin and which contribute the least, providing insight into their value and relative importance. This dynamic version shows one way to make the analysis interactive and flexible, but it can easily be adapted to different business contexts and needs. In this setup, users can: ✅ Choose the classification period (𝗟𝗮𝘀𝘁 𝟲, 𝟭𝟮, 𝗼𝗿 𝟮𝟰 𝗺𝗼𝗻𝘁𝗵𝘀) calculated backward from the selected month ✅ Adjust thresholds for 𝗖𝗮𝘁𝗲𝗴𝗼𝗿𝘆 𝗔 and 𝗖𝗮𝘁𝗲𝗴𝗼𝗿𝘆 𝗕 using sliders (you can also choose 80/20, which aligns with the Pareto principle) ✅ Choose whether to classify by 𝗥𝗲𝘃𝗲𝗻𝘂𝗲 or 𝗚𝗿𝗼𝘀𝘀 𝗣𝗿𝗼𝗳𝗶𝘁 ✅ Filter by 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗨𝗻𝗶𝘁 💡 Quickly answer questions like: – “Is a small group of Category A customers responsible for the majority of total revenue (the 20/80 principle)?” – “How dependent is our revenue on our largest (Category A) customers?” – “What share of all customers falls into Category C, the ones contributing the least to total revenue?” ⚙️ Setup highlights ▪️Disconnected table for 𝗔𝗕𝗖 𝗰𝗮𝘁𝗲𝗴𝗼𝗿𝗶𝗲𝘀 ▪️Parameters for 𝗰𝗮𝘁𝗲𝗴𝗼𝗿𝘆 𝘁𝗵𝗿𝗲𝘀𝗵𝗼𝗹𝗱𝘀 (𝗔 %, 𝗕 %) ▪️Disconnected table for 𝗽𝗿𝗲𝘀𝗲𝘁 𝗽𝗲𝗿𝗶𝗼𝗱𝘀 ▪️𝗦𝘂𝗽𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝗹 𝗰𝗮𝗹𝗲𝗻𝗱𝗮𝗿 used for Revenue, Gross Profit, and GM% calculations based on preset periods ▪️𝗗𝗔𝗫 𝗹𝗼𝗴𝗶𝗰 for ABC classification using cumulative revenue or gross profit % (and closest thresholds), combined with a derived visual-level filter This version was inspired by techniques from the 𝗗𝗮𝘁𝗮 𝗩𝗶𝘇 𝗙𝗼𝗿𝗴𝗲 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆 and discussions with Achmad Farizky and Gustaw Dudek 🙌 💬 How do you segment your customers? What approach works best?

Understanding your customers shouldn’t be guesswork… This customer behaviour analysis was carried out for an E-commerce firm that seeks to examine how customers interact with their product, service, or platform to understand their actions, preferences, and decision-making processes. To address this, I followed a structured data analysis process: 📍 Data Collection & Cleaning – I gathered customer demographic, browsing, and purchase data, then cleaned it to remove duplicates, handle missing values, and ensure consistency. 📍Exploratory Data Analysis (EDA) – Through summary statistics and interactive visuals, I explored key metrics to identify patterns and anomalies. 📍Segmentation – I segmented customers based on behaviour and demographics (e.g., high-value buyers, age groups) to reveal distinct personas. 📍 Behavioural Analysis – I tracked customer journeys, identifying drop-off points and common conversion paths to understand what drives engagement and sales. 📍Insight Communication – Using Power BI, I translated findings into clear dashboards and visuals, enabling stakeholders to grasp trends and make data-driven decisions quickly. Each step brought us closer to the 'why' behind the numbers, so we could move from data to strategy. The result? A more data-informed understanding of their customers, and concrete strategies to improve engagement and retention. Curious how data can unlock hidden customer value? I’m always open to a conversation. Let’s connect and share insights. Have a lovely weekend!! #datafam