Consumer Segmentation 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.

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?

“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.

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?

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.

Analyzing customer data in aggregate is like wearing foggy goggles in an art gallery. You'll miss all the details that count. Lack of creativity in segmentation is why most data analysis exercises fade away with underwhelming conclusions. Confirmation bias is the other culprit. Most resort to firmographics or pricing tiers, but every product has more nuanced options. Honestly, I used to be pretty bad at this myself, but I learnt a few lessons along the way. For example, 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 typical company. We segmented the data by company industry and type (agency vs. nonagency) to get more meaningful benchmarks. 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) At the same time, over-segmentation is a curse, especially when data is sparse. An LLM like Claude can help you suggest some interesting slices for smaller data sets. But it can only play with what you feed it. If you want to be truly "data-informed", better develop a nose for segmentation.

🚀 Rethinking Segmentation: AI & Analytics in Modern Banking 🏦✨ Many retail and commercial banks are at a crossroads: How do we move beyond legacy segmentation models — the ones driven mainly by product sales, static demographics, or broad tiers — to truly understand customers in real time? This is where AI and advanced analytics step in as game changers. 🔍 What’s changing? ✅ Data-driven segmentation: Instead of static segments, AI can cluster customers based on actual behaviors, transaction patterns, channel usage, and lifecycle signals. ✅ User flow insights: Banks can now analyze how customers navigate digital channels — from onboarding to everyday banking — identifying friction points and tailoring journeys in real time. ✅ Predictive engagement: Advanced models don’t just describe segments — they predict which customers are likely to churn, upsell, or need proactive support, enabling timely and personalized outreach. 💡 Why does this matter? For retail banking, it means moving from generic campaigns to micro-targeted offers that feel relevant and timely. For commercial banking, it means understanding the nuanced needs of SMEs and corporates based on transaction networks and industry shifts — not just size or revenue bands. 📈 The result? More relevant interactions, higher trust, better conversion, and a clear competitive edge in a crowded market. As banks race to become more customer-centric, AI-powered segmentation is no longer optional — it’s a strategic imperative. Curious to hear: How is your bank evolving its segmentation playbook? 🤝👇 #Banking #AI #Analytics #CustomerExperience #DigitalTransformation #RetailBanking #CommercialBanking #Segmentation