Identifying High-Risk Customer Segments for Cancellation

The best dashboards don't tell you how much money you made. They tell you which customers are about to stop making you money. That's the difference between reporting and business intelligence. I recently built this customer and business intelligence dashboard around one question: If you could predict customer churn before it happens, what would you do differently today? Most businesses spend time explaining why revenue dropped. Very few spend enough time identifying the customers who are most likely to leave before that drop happens. This dashboard does exactly that. It combines customer behavior, revenue trends, payment patterns, and RFM analysis into one executive view. A few insights stood out immediately: • The dashboard identifies the top three customers at the highest risk of leaving, allowing the sales team to intervene before revenue disappears. • It separates already churned customers from those still recoverable, making retention efforts more focused. • Revenue, quantity, customer, and country performance are tracked simultaneously across day-over-day and week-over-week trends, helping leaders distinguish between temporary fluctuations and genuine performance issues. • Payment method analysis highlights where revenue concentration and customer behavior create hidden business risks. One thing I've learned from working on analytics projects is this: Revenue rarely disappears without warning. Customers usually leave clues first. Fewer purchases. Longer gaps between transactions. Lower engagement. Smaller order values. Those signals often appear weeks before the business feels the financial impact. That's why I believe RFM analysis remains one of the most practical customer intelligence frameworks available. It turns thousands of transaction records into clear business priorities. Some of the calculations behind this dashboard include: • RFM Score = Recency + Frequency + Monetary rankings used to classify customer segments. • Customer Churn Rate = Lost Customers ÷ Total Customers. • Revenue at Risk = Revenue associated with customers classified as At Risk. • Advanced DAX measures using RANKX(), CALCULATE(), DIVIDE(), DATESINPERIOD(), DATEADD(), SWITCH(), VAR, and dynamic filter context to identify customer segments, compare period performance, and monitor revenue trends. For me, dashboards become valuable when they change the next business decision. Knowing who your best customer was last month is useful. Knowing who is about to leave next month is far more valuable. PS: If your dashboard could answer only one question, would you rather know who bought the most, or who is most likely to stop buying next? My name is Eniola Oluwashola, a Snr Data & Business Analyst. I do not approach data as reports. I approach it as a decision system. Every dataset I work with is anchored to a business question: where are we losing money, what is working, and what do we do next?

A few months ago, a friend working in customer experience analytics struggled with a high customer churn rate. The retention team kept offering discounts and loyalty perks, but cancellations continued to rise. Instead of blindly increasing promotions, we used SQL and data analysis to understand why customers were leaving. Diagnosing Churn with SQL 1️⃣ Identifying At-Risk Customers We analyzed recent activity trends to find users showing signs of disengagement before canceling. SELECT customer_id, COUNT(order_id) AS total_orders_last_3_months, MAX(order_date) AS last_order_date FROM orders WHERE order_date >= DATEADD(month, -3, GETDATE()) GROUP BY customer_id HAVING COUNT(order_id) < 2 ORDER BY last_order_date ASC; 🔹 Insight: Customers with fewer than 2 orders in the last 3 months were more likely to churn. 2️⃣ Detecting Service-Related Churn Triggers We checked if churn was linked to delivery delays, refund requests, or bad ratings. SELECT c.customer_id, COUNT(DISTINCT o.order_id) AS total_orders, COUNT(DISTINCT CASE WHEN d.delivery_delay > 15 THEN o.order_id END) AS delayed_orders, COUNT(DISTINCT CASE WHEN r.refund_status = 'Approved' THEN o.order_id END) AS refunded_orders, AVG(feedback.rating) AS avg_rating FROM customers c LEFT JOIN orders o ON c.customer_id = o.customer_id LEFT JOIN deliveries d ON o.order_id = d.order_id LEFT JOIN refunds r ON o.order_id = r.order_id LEFT JOIN feedback ON o.order_id = feedback.order_id GROUP BY c.customer_id ORDER BY avg_rating ASC, delayed_orders DESC; 🔹 Insight: Frequent delivery delays and refund requests were the top churn drivers, not pricing issues. 3️⃣ Predicting Future Churn Risks Using historical data, we identified patterns of disengagement before cancellation. SELECT customer_id, AVG(DATEDIFF(day, order_date, GETDATE())) AS avg_days_since_last_order, COUNT(DISTINCT order_id) AS total_orders FROM orders GROUP BY customer_id HAVING avg_days_since_last_order > 30 AND total_orders < 5; 🔹 Insight: Customers who hadn’t ordered in 30+ days and had fewer than 5 lifetime orders were high-risk churn candidates. Challenges Faced False Positives: Some customers naturally had long purchase cycles, so we refined segmentation. Operational Constraints: Fixing delays required logistics changes, not just marketing efforts. Data Fragmentation: Churn data was spread across multiple systems, making analysis complex. Business Impact ✔ 20% reduction in churn after prioritizing service quality improvements over discounts. ✔ More effective retention campaigns by targeting at-risk customers before they left. ✔ Better cross-team alignment, helping operations, marketing, and CX teams work on the real issues. Key Takeaway: Churn isn’t just a marketing problem—it’s a business-wide issue that requires data-driven insights. Have you used SQL to reduce churn? Let’s discuss!

Most teams think churn starts at cancellation. It doesn’t. Churn starts quietly. In behavior. In silence. In stalled momentum. The reality? 🚫 Customers rarely leave suddenly 🚫 Risk shows up before renewal 🚫 Usage drops before complaints 🚫 Silence is louder than feedback Here’s how retention risk actually shows up, by customer segment: 1. New Customers (0–30 Days) ↬ Low activation and shallow feature use ↬ No “aha” moment ↬ Support tickets signal confusion, not bugs ↬ Fix: Tight onboarding, in-app nudges, early personal check-ins 2. SMB / Mid-Market ↬ Usage slowly declines ↬ One core user disappears ↬ Logins drop as renewal approaches ↬ Price objections appear late ↬ Fix: Re-anchor value to outcomes, expand team usage, share quick wins 3. Enterprise Accounts ↬ Champion goes quiet or exits ↬ Decision-makers stop showing up ↬ Procurement delays renewals ↬ Expansion conversations freeze ↬ Fix: Multi-thread relationships, exec check-ins, document ROI continuously 4. Power Users ↬ Sudden drop in advanced feature usage ↬ Feedback turns silent ↬ Advocacy disappears ↬ Fix: Ask why immediately, invite into roadmap, offer betas or exclusives 5. Long-Term Customers ↬ “Set and forget” behavior ↬ No upsell interest for long periods ↬ Flat engagement despite product evolution ↬ Fix: Re-sell the vision, highlight what’s changed, refresh success metrics Retention risk ≠ a cancellation notice. It’s behavior + silence + stalled momentum. If you wait for churn… You’re already late. Which customer segment is showing the loudest warning signs right now? 👉 Start with the Growth & Profitability Scorecard https://lnkd.in/ekcgYfGe

Netflix found that users who cancel without saying a word are 2x harder to win back than those who first raise a complaint. Most companies track who complains. But you should be more worried about who doesn’t. If you’re only looking at support tickets to spot churn risk, you’re missing the bigger picture. Some of your most at-risk users are the ones not using the product and not reaching out. They’re quietly slipping away. The real insight comes from joining support data with product usage. But Customer Success teams often don’t have access to usage data. Not because they don’t want it, but because it’s buried in tools they can’t query, stuck behind engineering bandwidth, or siloed away from their daily workflow. Low usage and no complaints? That’s your danger zone. High complaints with high usage? That’s actually a sign they still care. Track complaints, yes. But also track silence. Silence is often the first step to goodbye.

How I Went From Reporting Numbers to Driving Strategy Last week, my post about failing a data analyst interview reached over 18,000 impressions. Many of you asked, "How exactly are you bridging that gap?" Here's the honest breakdown, no fluff, just what's working. THE PROBLEM I IDENTIFIED: I was stuck in descriptive analytics (what happened?) while businesses needed prescriptive analytics (what should we do?). I could tell you sales dropped 15% last quarter. But I couldn't explain: • WHY it dropped (diagnostic) • WHICH customers might churn (predictive) • WHAT actions to take (prescriptive) That's the gap I'm closing. WHAT I'M LEARNING: Instead of just mastering more tools, I'm learning strategic frameworks that change how I view data: 1. RFM Analysis (Recency, Frequency, Monetary)* Segments customers into Champions, At-Risk, Lost, and Potential Loyalists.   Example: "These 12% of customers generate 34% of revenue but haven't purchased in 60 days; a retention campaign is needed." 2. Customer Lifetime Value (CLV) Predicts the long-term value of customer segments.   Shifts focus from single transactions to relationship value. 3. Cohort Analysis Tracks customer groups over time and reveals retention patterns.   Example: "Q1 customers have 40% better retention than Q3; what did we do differently?" 4. Churn Prediction Identifies at-risk customers before they leave.   Example: Customers with 3+ support tickets and expiring contracts have a 67% churn risk. 5. Market Basket Analysis Reveals products bought together for cross-selling strategies. Example: 80% of customers who buy Product A also buy Product B within 30 days THE MINDSET SHIFT: Before: Looking at data and asking, What can I calculate? Now: Looking at business challenges and asking, What data do I need to solve this? I've learned to think in four levels: Level 1 (Descriptive): Sales decreased 15% Level 2 (Diagnostic): Top 3 customers cut orders by 40% Level 3 (Predictive): We'll likely lose 2 more major customers in Q1  Level 4 (Prescriptive): Launch a targeted retention campaign. Estimated ROI: 3.5x" Most analysts stop at Levels 1-2. The job market rewards Level 3-4 thinking. RESOURCES HELPING ME: Learning: • Kaggle Learn - Free short courses   • Mode Analytics SQL Tutorial - Advanced SQL techniques   • StatQuest YouTube - Statistics explained simply   • Google Data Analytics Certification - Solid foundation  Practice: • Kaggle datasets - Real messy data to work with   • Maven Analytics - Free datasets with business context   Currently reading Storytelling with Data by Cole Nussbaumer Knaflic  TO EVERYONE WHO REACHED OUT: Your messages reminded me that I'm not alone in this journey. My challenge: Pick ONE framework, find a Kaggle dataset, build something this weekend, and share what you learned. Let's level up together. #DataAnalytics #CareerDevelopment #LearningInPublic #DataScience #BusinessIntelligence

𝗜 𝘄𝗼𝗿𝗸𝗲𝗱 𝘄𝗶𝘁𝗵 𝗮 𝘀𝘁𝘂𝗱𝗲𝗻𝘁 𝗼𝗻 𝗮 𝘀𝗺𝗮𝗹𝗹 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗽𝗿𝗼𝗷𝗲𝗰𝘁. It turned into a surprisingly realistic case study. The problem: International students sign up for German health insurance months before arriving. Then visa delays happen. Payment issues happen. Students switch providers. And insurers are left with hidden operational and financial risk they can’t see coming. So we built a dashboard to answer one question: Where is the risk actually coming from? 𝗧𝗛𝗘 𝗗𝗔𝗧𝗔 We simulated ~5,000 student records and analyzed: → Early churn behavior → Debt accumulation before arrival → Payment failures (SEPA vs bank transfer) → Visa outcomes and inactive accounts → Cost shock segments (age + employment) → Geographic concentration of risk Real scenarios. Messy data. Actual business problems. 𝗪𝗛𝗔𝗧 𝗪𝗘 𝗙𝗢𝗨𝗡𝗗 → Early churn happens fast — average around month 3 → Arrival delays dramatically increase debt exposure → Payment failures strongly correlate with churn risk → A small group of segments drives most cancellations These aren’t abstract insights. These are operational red flags. 𝗧𝗛𝗘 𝗗𝗔𝗦𝗛𝗕𝗢𝗔𝗥𝗗 Built entirely in Excel using: → Power Pivot measures → Pivot-driven KPI cards → Segmentation analysis → Behavioral and operational risk drivers Page 1: Financial exposure (debt, churn losses, operational cost) Page 2: Drivers of churn and risk (payment behavior, visa outcomes, delay buckets, nationality clusters) No fancy tools. Just Excel and clean thinking. 𝗪𝗛𝗬 𝗧𝗛𝗜𝗦 𝗠𝗔𝗧𝗧𝗘𝗥𝗦 The goal wasn’t flashy visuals. It was building something that could realistically be presented to an insurance operations team. Something that answers: “Where do we lose money?” and “Which customers are high-risk?” This is what real analytics looks like. You don’t need Tableau or Python for every project. You need to understand the problem and connect the data to business decisions. Sometimes the most interesting analytics projects aren’t about complex models. They’re about making hidden risk visible. #DataAnalytics #Excel #BusinessIntelligence #Datafam

Inside Apple Pay : How I Approach this Case study like a Data Scientist As a Data Scientist working with Apple Pay, I have faced a challenging case study on predicting customer churn and designing retention strategies during its interview round. Here’s a detailed walkthrough of my approach and insights, which are crucial for anyone preparing for product-focused data science interviews. -> Problem Statement: “Predict which Apple Pay users are likely to churn and propose actionable retention strategies.” ->Understanding the Problem & Business Context -Define churn: inactivity for X months, unlinked cards, low transaction frequency. -Clarify business goals: reduce churn, improve engagement, increase transactions. -Identify stakeholders: Product, Marketing, Risk teams. -> Data Exploration & Feature Engineering I analyze Apple Pay data: -User demographics: Age, region, device type -Transaction behavior: Frequency, recency, average transaction value -Feature usage: Tap to Add Card, Wallet Extensions, Provisioning attempts -Support & feedback logs: Failed transactions, complaints Key engineered features: -RFM metrics (Recency, Frequency, Monetary) -Feature adoption trends: % of CVVless vs CVV-based transactions -Engagement scores: session count + feature usage + conversion -Handling imbalanced classes: SMOTE/ADASYN and class-weighted features to capture churned users ->Model Selection & Evaluation -Baseline: Logistic Regression (for interpretability) -Advanced: XGBoost Ensemble Technique (for higher performance and capturing non-linear patterns) -Evaluation metrics: ROC-AUC, Precision-Recall, F1-score (critical for imbalanced churn data) ->Insights & Actionable Recommendations -Segmentation: Identify high-risk users for targeted campaigns -Personalized messaging: Push notifications, incentives, or discounts -Feature improvements: Reduce friction in Tap to Add Card or Wallet flows ->Key Takeaways -Always connect data to product impact -Behavioral metrics often outperform demographics in churn prediction -Start simple → then move to ensemble models -Predictions must translate to actionable strategies -Domain knowledge (Apple Pay features) is essential for contextual analysis Bottom Line: This case study demonstrates the end-to-end process of turning data into actionable product insights,something every data scientist working on consumer fintech products should master. #DataScience #MachineLearning #FeatureEngineering #ChurnPrediction #PredictiveAnalytics #XGBoost #LogisticRegression #Modeling #DataDriven #DataAnalytics #Fintech #DigitalPayments #ApplePay #WomanInTech

7 early warning signals showing your customers are about to cancel their subscription: Your customers tell you they're leaving long before they hit "cancel." Here are the red flags I've spotted: 𝟭. 𝗧𝗵𝗲 𝗴𝗵𝗼𝘀𝘁 𝗽𝗮𝘁𝘁𝗲𝗿𝗻 They stop: • Opening your emails • Using key features • Logging in regularly Silent customers = future cancellations 𝟮. 𝗧𝗵𝗲 𝘀𝘂𝗽𝗽𝗼𝗿𝘁 𝘀𝘂𝗿𝗴𝗲 Sudden increase in: • Basic how-to questions • Feature complaints • Response time frustrations They're questioning their investment. 𝟯. 𝗧𝗵𝗲 𝘂𝘀𝗮𝗴𝗲 𝗰𝗹𝗶𝗳𝗳 Watch for: • Dramatic drop in logins • Fewer team members active • Core features ignored Low engagement = high risk 𝟰. 𝗧𝗵𝗲 𝘃𝗮𝗹𝘂𝗲 𝗯𝗹𝗶𝗻𝗱𝗻𝗲𝘀𝘀 They can't answer: • How much time they save • What ROI they're getting • Why they need you No clear value = easy goodbye 𝟱. 𝗧𝗵𝗲 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝘃𝗼𝗶𝗱 Red flags: • Ignore your surveys • Stop giving feedback • Don't join user calls Silence isn't golden. It's dangerous. 𝟲. 𝗧𝗵𝗲 𝗱𝗼𝘄𝗻𝗴𝗿𝗮𝗱𝗲 𝗱𝗮𝗻𝗰𝗲 Warning signs: • Ask about cheaper plans • Compare competitor pricing • Question feature value Price sensitivity spikes before churn. 𝟳. 𝗧𝗵𝗲 𝗼𝗻𝗯𝗼𝗮𝗿𝗱𝗶𝗻𝗴 𝘀𝘁𝗿𝘂𝗴𝗴𝗹𝗲 Look for: • Incomplete setup • Skipped tutorials • Missing key milestones They never really started = they'll never stay. 𝗛𝗼𝘄 𝘁𝗼 𝘀𝗽𝗼𝘁 𝘁𝗵𝗼𝘀𝗲 𝘀𝗶𝗴𝗻𝘀: ✅ Build a warning system ↳ Track these metrics weekly ✅ Set trigger points ↳ Define when to intervene ✅ Create rescue plays ↳ Have ready-to-go save strategies ✅ Measure intervention success ↳ Track what actually works The best churn strategy? Stop it before it starts.

Some moments in life remind you that the journey is just as important as the destination. Grateful for the people who make learning, growth, and hard work enjoyable. Behind every project is the support, laughter, and encouragement that keep us going. Telecom companies spend millions acquiring new customers, yet many leave within their first six months. What if businesses could predict who is likely to leave and take action before they do? That’s exactly what I set out to solve using Machine Learning and #datascience. I built a customer retention prediction model using real-world telecom data to uncover patterns behind service cancellations and help businesses retain more customers. I started with exploratory data analysis to identify key trends influencing customer drop-off. Feature engineering played a huge role in transforming tenure, contract types, payment methods, and internet usage into meaningful insights. To improve prediction accuracy, I balanced the dataset using #SMOTE and tested multiple machine learning models, including Logistic Regression, KNN, Decision Trees, and Random Forest. After rigorous testing, Random Forest with fully engineered features delivered the best performance, achieving a ROC-AUC score of 0.845. It effectively identified at-risk customers with a recall of 79.2 percent while maintaining a precision of 51.8 percent to reduce false alarms. This project is not just about building models but about making Machine Learning work for real business problems. Turning raw data into actionable insights is where the real impact happens. GitHub Repository: [Customer Retention Prediction](https://lnkd.in/d4e6p_M4) #MachineLearning #DataScience #CustomerRetention #PredictiveAnalytics #Python #AI #FeatureEngineering #RandomForest #BusinessStrategy

Risks in your customer base are the starting point for churn. If you’re not actively tracking and addressing them, you’re leaving retention to chance. Here’s how to get a clear view of the risks that could lead to churn—and, more importantly, how to mitigate them. 1. Define the Risks You Want to Track Start by identifying the key risk factors in your customer base. A great way to do this is by analyzing your churned or downgraded customers—what were the root causes? For example: -> Decreasing product adoption (customer using your product less) -> Bugs (customer flagging issues with your product) -> No engagement (not engaging with your customers) -> Termination risks (customer -> Competitor risks (customer contemplating or testing competitors) -> Financial risks (customer not paying invoices or delayed payments) -> Champion leaving (key stakeholder leaving) Clearly define each risk so your team knows what to look for. 2. Create a System to Track These Risks -> If you're just starting out or managing a small book of business (fewer than 20 customers per CSM), a spreadsheet can work. Use your predefined risk categories and add a rationale for each risk you track. -> As you scale, manual tracking becomes unrealistic. Automate risk tracking through your CRM (like HubSpot) or a customer success platform. Use data-driven signals—such as product usage trends, support tickets, and engagement scores—to surface risks proactively. 3. Continuously Refine Your Risk Tracking The risks that caused churn six months ago may not be the same today. Regularly review churned customers, update your risk categories, and adjust your playbooks. 4. Track all these risk categories in your CRM and make a plan to tackle them For every risk, define an impact driver—a proactive action that reduces the likelihood of churn. For example: Low adoption → Define key use cases & track milestone achievement No executive engagement → C-level to c-level outreach Declining engagement → Schedule recurring meeting or find a new stakeholder