Customer Churn Insights

Controversial opinion: Reconciliation is a strategic advantage in the payments space. Having advised the payment reconciliation platform Optimus for more than 5 years, it’s become clear that it’s possible to build a capable, modern payments platform with fast onboarding, developer-friendly APIs and global reach. But in many cases, the back office lets it down. Instead of integrating a tool like Optimus to manage the data, there’s chaos. At month end, ops and finance teams are knee-deep in spreadsheets. Ledgers don’t align. Merchants are chasing missing settlements. Support queues grow. And when that happens, it affects trust, revenue and scale. Reconciliation used to be considered admin. Today, it’s a measure of whether your platform can be trusted at scale. When a merchant is evaluating providers, they ask: ▪️Will I be paid in full and on time? ▪️Can I trace each transaction to settlement? ▪️When there’s a discrepancy, how quickly is it resolved? If the answers are unclear, trust slips and you may not even hear about it. You’re simply not shortlisted. What goes wrong? Often, reconciliation systems are bolted on to the front-end, not built into payment systems. Signs include: ▪️ Data spread across multiple systems. ▪️ Reporting delays, where issues are spotted days later. ▪️ No consistent source of truth. ▪️ Ops teams firefighting with Excel, custom scripts and late nights. It works for a while. But as volumes increase, so do the gaps. The impact starts small, then compounds: ▪️Merchant churn due to payout delays. ▪️Audit risks from missing or mismatched records. ▪️Revenue leakage from missed fees or misreported shares. ▪️A headcount-heavy model that doesn’t scale, just to keep on top of the data. If reconciliation doesn’t scale, the business can’t either. What do the best firms do? They treat reconciliation as a product capability, not a cost centre. 1. Architecture built for traceability Immutable ledgers. Event-driven data sync. Reconciliation-friendly APIs. 2. Tools built for clarity Systems that flag mismatches, trace data across sources and support real audits. 3. Processes start early Reconciliation is considered at the design stage. 4. A culture that values accuracy Payout accuracy, ledger integrity and audit readiness are tracked. Checks are automated and success is celebrated. Reconciliation is how you prove your platform works. Trust is accumulated through consistent accuracy. When a merchant asks, “Can you show me when and how this transaction was settled?” The answer should already be clear and accurate. It shouldn’t rely on 3 systems and a message to ops. As competition in the payments space increases, those who invest in reconciliation are the ones who will be trusted to scale. In the end, you’re not just moving money. You’re proving that it moved and did so as promised. I’m curious to hear how you’re thinking about this: Is reconciliation part of your conversations? #FinTech #Payments #DigitalTransformation

POST-2/7👉 Three Real 2025 Email Strategies Ecommerce Brands Are Using Right Now. And What You Must Change to Stay Out of the Spam Folder. AI isn’t coming for your email marketing—it already took it. But most brands are still treating it like a shiny new tool instead of the tectonic shift it actually is. 2025 has proven one thing: AI-powered automation is not just shaping content. It’s directly influencing your deliverability, click-throughs, and revenue per subscriber. Let’s break down exactly what’s changed—and what to do about it: EXAMPLE #1: Klaviyo's Smart Send Time feature aims to optimize email delivery by analyzing customer engagement over a 24-hour period. However, if not managed carefully, it can lead to erratic volume surges and inconsistent engagement per segment, potentially impacting deliverability. ✅ Takeaway: AI isn’t a set-it-and-forget-it tool. You must still monitor send timing consistency manually—especially across Gmail and Yahoo. EXAMPLE #2: A study conducted with 450 participants found that AI-generated emails—designed with plain text, clear subject lines, and minimal formatting—consistently reached inboxes across Gmail, Yahoo, and Outlook, with zero percent flagged as spam. ✅ Action: Neverthless, don’t rely solely on AI-generated content. Maintain a balance between dynamic and static content to preserve your brand's identity and ensure consistent deliverability. LIVE EXAMPLE #3: Klaviyo's predictive analytics features are powerful tools for optimizing marketing spending and personalizing customer communication. By leveraging predictive insights, brands can focus their efforts on high-value customers and proactively prevent churn. ✅ Actionable Setup: Use Shopify + Klaviyo predictive analytics to segment high-value churn risks. Build replenishment flows triggered by consumption timelines, not just purchase dates. Suppress these from ad spend via integration tools to reduce overlap and CAC. 2025 Email Strategy Mistakes to Avoid: ❌ Relying on open rates as trigger logic—Apple’s MPP and Gmail filters render them unreliable. ❌ Allowing AI to fully automate send times without IP-level throttle logic. ❌ Over-personalizing dynamic content blocks across multiple cohorts without creative guardrails. Smarter AI, Smarter Email Marketing in 2025: Here’s what to implement now if you want to keep scaling without tripping Gmail’s alarms: 1. Train AI models on intent-rich data, not just past opens or clicks. Use on-site behavior, quiz responses, zero-party data. 2. Create a consistency layer in every campaign—same footer, tone, and structure, even when content is dynamic. 3. Use AI tools for ideas, not full campaign copy. Add human editing to every send. Final word? AI can’t fix a bad strategy. But it will expose one. In 2025, the best-performing ecommerce brands aren’t just using AI. They’re managing it with surgical discipline. Is your brand training your AI—or letting it train your reputation? #email #emailmarketing

No engagement = churn risk. You think an unanswered check-in is "neutral." It's not. It's a soft downvote on your relationship. And the more of those you collect, the faster you slide into churn territory. The pattern is almost always the same: Customer disengagement isn't an event - it's a gradual fade. First, they're a bit slower to respond. Then, they start rescheduling meetings. Then, they stop showing up altogether. By the time your CRM flags them as "at risk," you've already lost them. This is especially true in today's product-led economy, where switching costs are lower than ever. Your champion might be updating their LinkedIn profile as you read this. The fix? "Silent rescue campaigns" → Target accounts before they hit 45-60 days of declining engagement metrics → Don't panic with escalations or discount offers. Try recalibration: "Let's reset: what does success look like for you NOW vs. when we started?" "I noticed your team's usage pattern has changed. Have your priorities shifted?" Give them control = acknowledge their evolving needs = improve retention. The key insight? Almost no customer wakes up one day and decides to churn. They disengage gradually. Your customer relationship is a living entity that requires constant adaptation. If you treat all accounts with the same playbook regardless of their engagement signals, you're programming churn into your model. The most brutal truth in CS: By the time they tell you they're leaving, they made that decision 3-6 months ago. What's your strategy for catching that decision window before it closes?

Three years back, One of my friend faced a drop in repeat purchases in a fast-growing online marketplace. Instead of blindly increasing discounts, the team turned to SQL and data analytics to uncover the real reasons behind customer churn. SQL-Driven Approach 1. Identifying Lapsed Customers SELECT customer_id, COUNT(order_id) AS total_orders, MAX(order_date) AS last_order_date FROM orders GROUP BY customer_id HAVING COUNT(order_id) > 1 AND DATEDIFF(day, MAX(order_date), GETDATE()) > 60; 🔹 Insight: Target customers who haven’t ordered in 60+ days. 2. Discounts vs. Organic Purchases SELECT customer_id, COUNT(CASE WHEN discount_used = 'Yes' THEN 1 END) AS discount_purchases, COUNT(CASE WHEN discount_used = 'No' THEN 1 END) AS organic_purchases FROM orders GROUP BY customer_id; 🔹 Insight: Identify if customers only buy with discounts—these may not be loyal customers. 3. High-Value Customers Who Stopped Ordering SELECT customer_id, SUM(order_value) AS total_spent, MAX(order_date) AS last_order_date FROM orders GROUP BY customer_id HAVING total_spent > 500 AND DATEDIFF(day, MAX(order_date), GETDATE()) > 90; 🔹 Insight: Focus retention efforts on high-value customers. Challenges & Solutions Slow Queries? ✅ Added indexes on customer_id & order_date. Who to target? ✅ Used cohort analysis to find optimal re-engagement timing. Retention vs. Profitability? ✅ Ran A/B tests—loyalty perks worked better than heavy discounts. Business Impact ✔ 18% increase in repeat purchases with targeted campaigns. ✔ Optimized loyalty program to reward engagement, not just discounts. ✔ Reduced churn by identifying & acting on key retention signals. 💡 Key Takeaway: SQL isn’t just for reporting—it’s a powerful tool for understanding customer behavior and making smarter business decisions. What data-driven strategies have you used to boost retention? Let’s discuss!

The most expensive AI architectural bug? The one your customers find before you do. I’ve seen it wipe millions from annual revenue. And it’s usually preventable. Some AI failures shout. Others whisper until it’s too late. In architecture, the most dangerous are silent failures. They hide inside your pipelines, pass all your tests, and only surface when customers notice. They’re not caused by bad prompts or bad models. They happen when your architecture ships without the safety nets probabilistic systems need: ✅ No real-time evaluation loops ✅ No anomaly detection ✅ No rollback triggers I’ve seen it happen. A chatbot passed staging with flying colours. In production, a subtle API change broke entity recognition. It kept replying with plausible nonsense for 3 weeks before anyone noticed. By then, churn had spiked 18% and brand trust took months to rebuild. Architectural anatomy 🔹 Where it starts - Gaps in the evaluation & logging layer of your Enterprise AI System Architecture 🔹 Why it passes unnoticed -Monitoring checks uptime, not behaviour 🔹 Where it shows up - Customer behaviour changes, KPI anomalies, revenue trends 🔹 How to fix it: • Continuous evaluation pipelines (i.e. LangSmith, Arize AI) • Automated regression tests • Anomaly scoring with alert thresholds (i.e. Evidently AI, custom monitors) • Rollback workflows tied to detection events (i.e. canary deploys with auto-revert) Quick Self-Diagnosis (2 minutes) Pick your highest-impact AI use case in production. Ask: How do we know if it’s giving wrong but plausible outputs today? If the answer involves waiting for customer feedback, you’re already exposed. Are you at risk? • No automated output evaluation after deployment • No anomaly alerts feeding into escalation • No rollback trigger connected to detection events If you tick even one, silent failures are only a matter of time. Why it matters 📊 Avg detection time without eval loops: 2–6 weeks 📊 Delayed fixes cost 5–10x more 📊 Brand recovery after trust loss: 6–18 months 💰 At 10k transactions/day, a 2% silent failure rate could leak $X/month Role callouts 🛠 AI Architects - Verify the eval & logging layer tracks behaviour, not just infra metrics 📋 AI Delivery Leads - Tie rollback triggers to behaviour changes ⚖ Compliance - Route anomaly alerts into risk gates, not just dashboards Silent failures don’t just erode performance. They erode trust. And trust is the hardest thing to rebuild. Where in your AI stack would you install your first detection loop? ➕ Follow me (https://lnkd.in/g3F_QTQb) I post daily about the hidden shifts in enterprise AI and careers. 

VC Diaries - 22 : Most first time founders and their growth teams learn this the hard way - But, the most potent growth hack in India isn't a referral code - it's a helpdesk that actually helps. I see founders losing sleep over user churn, blaming it on everything from competition to product features. But often, the real reason is hiding in plain sight: a deep, inherited mistrust of any system of redressal. We are all conditioned by decades of experience. Think of trying to claim a simple refund or question a bill. You expect a maze of automated menus, disconnected calls, and gatekeepers whose job is to tire you out. Your default setting is not hope; it's resignation. Now, imagine a first-time digital user from a Tier-3 town, who has been warned by his family to be wary of online scams. Something goes wrong with his order, and a mere Rs 100 is deducted unfairly. For the startup, it's a low-priority ticket. For that user, it's a confirmation of his worst fears - the system is rigged. He won’t complain. He will just quietly uninstall the app and warn his entire neighbourhood against it. This is the trust deficit I speak of. It’s an invisible tax on growth. Startups spend crores on customer acquisition but treat the support team as a cost centre. This is fundamentally flawed. Every complaint isn't a problem; it's a priceless opportunity to create a fan for life - Like Zomato and Swiggy did. When that user with the Rs 100 issue gets a proactive call and an instant resolution, you've done more than just solve a ticket. You've shattered a lifelong assumption. This one act of integrity is more powerful than any celebrity endorsement. This user becomes your most authentic and passionate marketing channel, telling everyone that your company is honest. Forget chasing five-star reviews. Go chase your one-star complaints. Solving a problem with empathy doesn't just recover a customer; it creates a bond that is almost unbreakable. This is how you build a user base that doesn't just transact with you but truly trusts you. What do you think? Do share below in the comments. I have started to share my learnings as a VC more proactively here, with a note coming out every morning 8.30am. And I would love to get inputs. Thanks, Anuradha | Dexter Ventures

How to analyze B2B SaaS churn like a data scientist. (in plain English) ↓ Want to actually understand why customers leave? Here's the exact 7-step framework we use ↓ 1. Analyze overall MRR movements → Track new business vs. churn → Look for imbalances 2. Monitor trends over time → Identify seasonal effects → Spot patterns early 3. Break down churn details → Average value per loss → Number of accounts 4. Segment your customers → Create action categories → Group by ARPA & churn 5. Focus on high-risk, high-value → Identify "leaky buckets" → Prioritize retention 6. Find root causes → Analyze product usage → Review exit surveys → Conduct interviews 7. Take strategic action → Refine CS playbooks → Adjust target profiles → Update product roadmap Real example: We found one client's 1-10 employee segment had 3x higher churn. Simple solution? Redirect marketing to 11-50 employee companies. The sooner you understand your churn, the more ARR you make.

Personalization isn't just about adding a <dynamic name tag> in your follow-ups. That’s table stakes. Go deep, get relevant, and it will add rocket fuel to your paid efforts by lowering CAC and driving LTV. Here are 5 key personalization and segmentation tactics we’re running with Klaviyo this year to supercharge our growth: 📈 1. Triggered flows from high-intent actions Quiz completion, PDP views, cart hovers…we don't wait for them to just remember us. We create experiences that tie back to their interests and behavior. The setup: - Someone completes our quiz → immediate flow based on their results - Product page browsers → targeted follow-up for that specific SKU - Cart hoverers → urgency sequence before they forget Result: better conversion than universal welcome emails because they're contextual, not generic. 🔁 2. Dynamic segments that update in real-time Goal here is to build logic, not static lists. If someone browses 2+ collagen SKUs but doesn't purchase, they're moved into a "Collagen Consideration" segment automatically. If they buy, they're moved out. This keeps messaging relevant and timing tight, without needing manual intervention. 🧠 3. Predictive churn alerts + automated winbacks We use churn prediction scores to ID high-risk customers before they stop buying. Example: When someone views your 'Cancel Subscription' FAQ, they automatically get a churn prevention sequence within 24 hours. The flow: → Educational content + stronger value props → One-time discount to "pause" rather than cancel → Reminder of points or rewards they'd lose Win back a higher percentage of your churn-risk users this way (without hoping to retarget them on Meta). 🎯 4. On-site personalization from zero-party data When a customer shares goals or preferences in a quiz, we don't let that data sit. We use it to personalize everything from email subject lines and SMS follow-ups. "Looking for joint support?" → Product recommendation shows collagen SKUs, not fat burners. This creates a more relevant buying journey and lowers decision fatigue. 🔄 5. Cross-channel sequencing (email → SMS → onsite) We build orchestration into the flow logic, not just "blast and pray." Day 0: Email with their quiz results Day 1: SMS with a limited-time offer Day 3: If they return, they see a pop-up based on their quiz results This cross-channel sequence drives higher engagement while avoiding overexposure on any one channel. The tool that makes this possible is Klaviyo, and this is just a small example of what we’re building with it. Because it’s a full-on B2C CRM, Klaviyo lets us create highly personalized, high-performing flows at every stage of the funnel. If you’re still just batching and blasting, I recommend checking them out: https://lnkd.in/d7pKaQRB #Klaviyopartner

Here’s a fun (true) story with a tough ending. A small regional healthcare organization outsourced all of their IT org under the premise they’d save a $1M in that time. They signed a five-year contract, and spun it to employees that nothing would just change “other than management.” Most of the staff churned…predictably. The new managers were awful, personally and professionally. Benefits were worse. Trust was lost, instantly. Who could’ve seen this coming? The staff felt deceived and betrayed. The hardest part often isn’t hiring more people, it’s replacing the deep domain knowledge that accrues with interest over time. Trust with doctors, relationships with colleagues, understanding of systems designs; to replace all this is expensive. The worst part? Patient care SUFFERS. Things took a bad turn with the outsourced company and they churned the contract in just 18 months. Again, who could’ve seen that an offshore team with ZERO connection to the patients and community would be less effective? What’s next? They tried to recruit everyone back. But loss of trust of that magnitude doesn’t get revived, maybe ever. The good ones found better jobs and would never work in that low-trust environment again. What started as a cost-cutting project ended up costing the executive team MORE. People don’t fear technical advancements, they fear short-sightededness and rash decisioning.

I see so many 7-fig eCom brands going all in on email marketing, yet struggling to boost CLTV. It sucks because the culprit is usually embarrassingly simple: Ignoring their drop-off points. Every brand has moments when customers are less likely to buy again. If you don’t know when those moments are, your emails are just cluttering inboxes. Let’s say you've got: - 1,000 customers - $100 AOV - 40-day natural repurchase window - 85-day drop-off point And you're running: - Generic win-back at 120 days - Basic replenishment at 60 days What’s happening? You’re losing: - Natural repurchase opportunities at 40 days - Letting churn creep in before 85 days - And missing out thousands in revenue from poorly timed emails Painful, right? Here’s how to fix this: 1. Understand your timing. Use tools like Triple Whale 🐳 or Lifetimely to find your “time between orders” metrics. 2. For replenishment emails, look at the average time it takes your customers to reorder. If it’s 40 days, send those emails at 30–35 days with a solid incentive. 3. For win-back flows, figure out when your customers are unlikely to return. If most churn happens after 85 days, send win-back emails at 70–80 days to recover them before it’s too late. Bottom line? Your email marketing probably isn’t broken. Your timing is. Fix that first. Then focus on: - Subject lines - Design - Offers - Copy Because the best email in the world sent at the wrong time is just another unopened email. #emailmarketing #DTC #emailcampaigns