How to Create a Customer Retention Strategy

Every sales org has a pipeline: Stage 1. Stage 2. Forecast. Commit. You can recite it in your sleep. But when it comes to renewals? Crickets.... There’s no real-time view of customer risk. No weekly trendline. No forward-looking model that projects retention. Isn't that kinda bonkers? You FORECAST growth, but you DISCOVER churn...usually when it’s too late to fix. The big mistake is presuming retention is a lagging indicator, when it would be more accurate to define it as a neglected one. Think about it...most CS dashboards read like a rearview mirror: - Usage metrics. - Ticket volume. - NPS. Useful? Sure. Predictive? Nah. Because churn doesn’t start when a customer says they’re leaving. It starts months earlier when their exec sponsor stops showing up. If you can spot the risk early, you can unlock more levers: - Re-engage power. - Reset the value narrative. - Escalate to commercial. - Offer roadmap visibility. - Re-map success metrics post-reorg. But none of that works if your team can’t see it coming, which is why you need a churn forecast...just like you do for revenue. Build a churn pipeline. Treat it like sales. Some things to build into this: 1. Define churn stages. Just like deals, risk evolves: - Stage 1 (Early): No exec alignment, lagging adoption, stakeholder turnover. - Stage 2 (Mid): Value not realized, weak champion, limited engagement. - Stage 3 (Late): Renewal live, buyer pushing back, pricing/legal blockers. Assign clear criteria. Surface it in Gainsight, CZ, or Salesforce. Manage it like opportunity stages. 2. Assign prevention plays. Each stage should trigger a play: - Stage 1 = exec outreach + roadmap session. - Stage 2 = commercial review + value plan. - Stage 3 = C-level call + tailored retention offer. Codify this. Don’t let reps improvise. 3. Forecast it weekly. Revenue leaders inspect pipeline weekly. CS should do the same with churn. Ask: - How many accounts moved stages? - What’s projected retention by segment? - What % of at-risk logos have exec visibility? Also, share your churn forecast with the CFO. If the CFO sees new logo pipeline every Monday, but doesn’t see churn risk until Q4? That's no bueno. Start sending a weekly churn report with: - Total ARR at risk. - ARR by churn stage. - Stage progression. - Active recovery plays. - Forecasted retention (with confidence levels). This reduces surprises while also forcing accountability. Why? Because churn is rarely a CS failure, but it's often a product gap, pricing mismatch, or sales promise that aged poorly. When churn is forecasted, it becomes EVERYONE'S problem. Which it is. tl;dr = you don’t “discover” churn. You forecast it. You stage it. You act on it. Just like sales.

🚨 Stop guessing why customers churn. Start predicting and preventing it—with AI. Retention isn’t just a KPI. It’s a competitive moat—if you know how to build it. I’ve seen firsthand how retention turns from reactive to predictive when you fuse advanced data science with sharp business strategy. 🚀 5-Step AI/ML Retention Playbook 🔍 1. Integrate CLV-Powered Data Architecture 🔗 Unify transactional, behavioral, and sentiment data. 📉 Double down on features driving lifetime value erosion. 💼 Value Prop: Aligns spend with long-term profitability. 🤖 2. Build Explainable Churn Models 🌳 Use SHAP values with gradient-boosted trees. 🧪 Validate with causal inference, not just correlations. 💡 Value Prop: Creates defensible IP through interpretable AI. 🎯 3. Dynamic Risk Segmentation ⚡ Score users in real-time across engagement, fit, and payment health. 🚨 Trigger interventions at 85%+ confidence. 📊 Value Prop: Reduces CAC payback by 22%. 💡 4. Prescriptive Retention Engines 🧠 Reinforcement learning > static rule sets. 🎁 Test personalized win-backs based on elasticity modeling. 📈 Value Prop: +400bps lift from hyper-targeted nudges. 🔄 5. Closed-Loop Analytics Flywheel ♻️ Let intervention results train your models. 💰 Measure marginal ROI per dollar across segments. ⚙️ Value Prop: Retention becomes a growth engine, not just a metric. 💬 Want to put this playbook into action? Let’s connect—I'm always up for a deep dive into AI-driven growth. 👇 What’s one unexpected retention tactic that worked wonders in your org? #AI #MachineLearning #CustomerRetention #CTOInsights #SaaS #GrowthStrategy #GenerativeAI #PredictiveAnalytics #Leadership #DigitalTransformation #ProductStrategy #DataScience #BusinessGrowth #RetentionStrategy #B2BTech #TechLeadership #MLops #CustomerSuccess

Everyone talks about onboarding new customers. Nobody talks about reonboarding your old ones. If you're lucky enough to have customers who’ve been with you for 2+ years, it’s time to hit pause and audit. Because while your product evolved, many of them didn’t. They're still operating with old workflows, outdated training, and missed opportunities for impact. Who should you prioritize? Start with customers who: ➡️ Haven’t expanded in the last 12+ months ➡️ Haven’t logged into new features ➡️ Show signs of stagnant adoption ➡️ Have new stakeholders who weren’t around at onboarding ➡️ Were onboarded before your CS org/process matured Here’s what I've done to kick this off: 1️⃣ Run a deployment audit Identify feature usage gaps and map against current best practices. 2️⃣ Re-engage stakeholders Confirm if the original champions are still involved and who needs a reset. 3️⃣ Tailor updated training Highlight new features, improved workflows, and relevant use cases. 4️⃣ Reset goals + KPIs Align on where they’re going next, not where they’ve been. 5️⃣ Rebuild your success plan Give them a “Day 1” experience, grounded in today’s strategy. Use this as a guide but adjust based on your business. This isn’t just retention, it’s renewal insurance. This is expansion through enablement. This is Customer Success in motion. Are you giving your oldest customers your newest thinking?

3 customer success tools that work for high-touch (and how to implement them simply) After working with hundreds of CS organizations, I've identified three fundamental components that consistently drive retention and expansion: 1. A maturity model that reflects reality, not wishful thinking Why it matters: Without an accurate starting point, you can't create a realistic path forward. When you push customers too far too fast, they disengage and question your understanding of their business. Simple execution: --> Start with their current capabilities and constraints --> Identify the specific gaps preventing them from reaching the next level --> Focus on one achievable advancement at a time 2. A success plan that stakeholders care about Why it matters: Generic success plans get ignored because they don't connect to what decision-makers are personally measured on. When success plans align with stakeholder priorities, they become valuable business tools, not CS paperwork. Simple execution: --> Map each key stakeholder's individual goals and timelines --> Connect your solution directly to those specific objectives --> Present the plan in their language, not your product terminology 3. Business reviews that connect dots, not just show charts Why it matters: Usage data alone doesn't demonstrate value. Customers renew when they can see how your solution impacts their business outcomes. Without this connection, even high adoption can still result in churn. Simple execution: --> Pair each usage metric with a corresponding business result --> Highlight progress against their original success plan goals --> Include clear next actions that advance their maturity When these three components work together, feeding into each other, customer success becomes systematic rather than reactive. Maturity models inform success plans, success plans guide business reviews, and business reviews update maturity assessments. The result? Renewals happen naturally because the value is evident, not because of last-minute saves or discounts.