Improving Customer Segmentation with Data Analytics

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

Predict, Personalize & Perform : From Leads to Loyalty Let’s be honest—customer lifecycle marketing (CLM) in B2B used to be a fancy word for “email nurture” and “CRM segmentation. But today, with AI, machine learning, and predictive data models, CLM is becoming something much more powerful: ➡️ A living, learning ecosystem that adapts to each buyer journey in real time. Here’s how we’re seeing AI and ML revolutionize CLM in B2B: 🔍 1. Predictive Journey Mapping Machine learning algorithms are helping identify where an account or contact actually is in the funnel—not just where your CRM says they are. ✅ No more generic MQL > SQL flows ✅ Dynamic scoring based on behavior, content engagement, and intent signals ✅ Real-time stage shifts based on predictive fit and readiness — 📈 2. Hyper-Personalized Nurturing (at Scale) AI models now create content clusters matched to personas, industries, and even buying committee behavior. 🎯 Email sequences, LinkedIn ads, and landing pages are personalized based on: Buyer role Past touchpoints Predicted product interest ICP match + firmographic data It’s not just segmentation—it’s micro-personalization powered by behavioral AI. — 🔁 3. Intelligent Retargeting & Re-Engagement Using ML-powered intent data and anomaly detection, you can now: Spot churn risks before they happen Trigger re-engagement sequences based on drop-off patterns Retarget accounts that show subtle buying signals across web, search, and social Retention is no longer reactive. It's predictive. — 📊 4. Revenue Forecasting + Attribution Modeling Thanks to data science, we can model: Which touchpoints actually move pipeline Which leads are likely to convert within a time window How to attribute revenue across full-funnel programs—not just the last touch This gives marketing the credibility and confidence we’ve needed for years. — 💡 The CLM Stack of a Modern B2B Org Should Include: ✔️ Customer Data Platform (CDP) ✔️ AI-powered segmentation + scoring ✔️ Predictive content engines (LLMs + RAG) ✔️ Lifecycle orchestration tools (e.g. Ortto, HubSpot, Marketo w/ ML layers) ✔️ Analytics + BI layer for optimization 🧠 Final Thought: In 2025, CLM isn’t just “marketing automation” with better templates. It’s about building an AI-powered engine that understands, anticipates, and activates each step of the buyer journey. You don’t need more content. You need smarter orchestration. 💬 Curious to hear from other B2B leaders: How are you bringing AI into your lifecycle marketing stack?

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

I’m pleased to share my recent publication, “RFM-Based Customer Segmentation: A Pedagogical Case Study for Marketing Analytics Education”, in the Journal for Advancement of Marketing Education, with my colleague Henrique Carvalho. This paper provides a step-by-step guide to implementing customer segmentation using RFM analysis and Python, working with real transactional data from preprocessing through clustering and interpretation (Python code and dataset included!). For students and early-career analysts, this can serve as a portfolio project to demonstrate skills in data cleaning, feature engineering, segmentation, and clustering. For professors and instructors, the article offers a ready-to-use case study suitable for Marketing Analytics, Data Science, or Business Analytics courses. You can download the full article from here (it is open access!): https://lnkd.in/g_i3SHZM

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

Over the last few years, I’ve become increasingly focused on the idea that not all customers are created equal, what sets them apart—and more importantly, we shouldn’t treat them as if they are. Thanks to the work of people like Professor Peter Fader, Daniel McCarthy, and Allison Hartsoe, I’ve been digging deeper into customer centricity/customer equity and how to make it actionable. Especially in a B2C environment with varied SKUs and margins. At RVshare, we set out to better understand who our most valuable customers really are. We already track CLTV and CAC, but to go deeper, we enriched our customer data to learn things like: • Whether they own a vehicle capable of towing an RV (will you need a travel trailer or a class B) • Whether they have pets 🐶 or children (# of beds and a pet-friendly rental) • Household size and income indicators (what is your price consumption range) These attributes aren’t just nice to know—they’re incredibly powerful when synced back into our marketing stack. Using Census to connect BigQuery to Iterable, we enabled segmentation and targeting based on true customer value, not just engagement behavior. The results? → Improved retention forecasting → Smarter channel bidding strategies → Lower CACs from better-aligned messaging and audiences I wrote up the process, use cases, and some practical examples in a new blog post here: https://lnkd.in/g9P6Fuzq Would love to hear how others are applying customer equity principles in their growth and targeting strategies

The best restaurant marketers know what their customers want to do before they do. Predictive analytics in marketing automation ensures  your campaigns are always one step ahead. AI-driven insights allow for micro-segmentation and behavioral analysis that allow marketers to target campaigns based on predicted actions like purchase intent or churn risk. For example, if a restaurant could accurately identify morning customers at risk of churning and another group likely to purchase breakfast items, they could then send a targeted offer for a breakfast combo to the at-risk morning customers while promoting a limited-time deal on a new breakfast item to those showing purchase intent. With real-time data, segments adjust dynamically, making campaigns personalized and relevant. Rather than relying on retroactive data, predictive segmentation equips brands with actionable foresight, shifting strategies from reactive to proactive. 

Day 4 - CRO series Strategy development ➡Audience Segmentation Most marketing campaigns fail because they try to reach everyone. Smart businesses know that not all customers are the same. Here’s how to segment your audience for better targeting: 1. Define Your Segmentation Criteria Break your audience into meaningful groups based on: ◾ Demographics → Age, gender, income, education ◾ Geographic Location → Country, city, region ◾ Behavioural Data → Purchase history, engagement levels ◾ Psychographics → Values, interests, lifestyle choices The more precise the segmentation, the more effective the targeting. 2. Collect Audience Data Use multiple sources to understand your customers: ◾ Surveys & Interviews → Direct feedback from customers ◾ Website Analytics → Google Analytics, heatmaps, session recordings ◾ CRM Systems → Customer history, interactions, and purchase patterns Data removes guesswork. 3. Analyze the Data & Identify Patterns Look for trends: ◾ Are certain groups more likely to convert? ◾ Who engages most with your brand? ◾ What common traits do your best customers share? These insights form the foundation of strong segmentation. 4. Create Customer Segments Group people based on similar characteristics. Examples: ◾ High-value customers → Frequent buyers with high purchase amounts ◾ Engaged followers → Customers who interact on social media ◾ New leads → First-time website visitors Each segment requires a different marketing approach. 5. Develop Targeted Strategies Personalization is key. ◾ Young professionals? Use social media ads & video content. ◾ Older customers? Email campaigns may work better. ◾ High spenders? Loyalty programs & VIP offers. Speak to each segment in their language, on their preferred platform. 6. Test, Measure, and Optimize Not all strategies work equally. ◾ A/B test different messages within segments ◾ Track conversion rates, engagement, and retention ◾ Refine based on what performs best Optimization is an ongoing process. Why Segmentation Matters ✔ More Relevant Marketing → Customers receive messages tailored to them ✔ Higher Engagement & Conversions → People respond to what feels personalized ✔ Optimized Marketing Spend → Invest in what works for each segment ✔ Better Customer Experience → Customers feel understood and valued Businesses that segment their audience don’t just market better— They sell smarter. Are you using segmentation in your marketing? Share your thoughts below. See you tomorrow! P.S: If you have any questions related to CRO and want to discuss your CRO growth or strategy, Book a consultation call (Absolutely free) with me (Link in bio)