Ways To Use Data Analytics For Customer Support Insights

I’m not asking my CSMs to resolve support tickets. I’m asking them to leverage them. Support tickets aren’t just a backlog of problems; they’re customer truth bombs waiting to explode. If you’re not mining them for insights, you’re flying blind—and that’s exactly how churn sneaks up on you. Every Customer Success team I’ve ever led has been trained to use Support tickets strategically. Why? Because they’re packed with insights that make us better at our jobs. ✅ We learn more about the product. ✅ We spot trends before they become problems. ✅ We understand our customers’ use cases more deeply. If you’re not tapping into support data, here’s what you’re missing: 🔥 Emerging Pain Points Recurring issues expose friction in the customer journey. Ignore them, and those minor frustrations turn into churn-worthy headaches. 🔥 Product Gaps Customers vote with their tickets. If the same feature requests or usability complaints keep surfacing, your roadmap is practically writing itself. 🔥 Engagement Risks A spike in tickets isn’t just noise—it’s a flare. Users don’t submit tickets when they’re thriving; they do it when they’re stuck, frustrated, or in need of more enablement. Here are a few ways my team and I are using these insights: ✅ Spot & Engage Struggling Users A surge in ticket volume? Proactively reach out before frustration turns into a cancellation. ✅ Create Targeted Content If the same questions keep coming up, turn those insights into help docs, webinars, or office hours. ✅ Surface Expansion Opportunities Seeing frequent feature requests? Build them—or better yet, use them to tee up expansion conversations. ✅ Map Out User Behavior Support tickets tell you who’s onboarding, who’s adopting new features, and who’s stuck. Use that data to drive deeper engagement. ✅ Collaborate with Product Your product team needs this intel. Share support trends regularly to influence meaningful fixes and features. High ticket volume isn’t necessarily a bad thing—but you need to know how to use it to your advantage. Bottom line? CSMs don’t need to fix support tickets. But the best ones know how to use them to drive retention, expansion, and adoption. _____________________________ 📣 If you liked my post, you’ll love my newsletter. Every week I share learnings, advice and strategies from my experience going from CSM to CCO. Join 12k+ subscribers of The Journey and turn insights into action. Sign up on my profile.

Surveys can serve an important purpose. We should use them to fill holes in our understanding of the customer experience or build better models with the customer data we have. As surveys tell you what customers explicitly choose to share, you should not be using them to measure the experience. Surveys are also inherently reactive, surface level, and increasingly ignored by customers who are overwhelmed by feedback requests. This is fact. There’s a different way. Some CX leaders understand that the most critical insights come from sources customers don’t even realize they’re providing from the “exhaust” of every day life with your brand. Real-time digital behavior, social listening, conversational analytics, and predictive modeling deliver insights that surveys alone never will. Voice and sentiment analytics, for example, go beyond simply reading customer comments. They reveal how customers genuinely feel by analyzing tone, frustration, or intent embedded within interactions. Behavioral analytics, meanwhile, uncover friction points by tracking real customer actions across websites or apps, highlighting issues users might never explicitly complain about. Predictive analytics are also becoming essential for modern CX strategies. They anticipate customer needs, allowing businesses to proactively address potential churn, rather than merely reacting after the fact. The capability can also help you maximize revenue in the experiences you are delivering (a use case not discussed often enough). The most forward-looking CX teams today are blending traditional feedback with these deeper, proactive techniques, creating a comprehensive view of their customers. If you’re just beginning to move beyond a survey-only approach, prioritizing these more advanced methods will help ensure your insights are not only deeper but actionable in real time. Surveys aren’t dead (much to my chagrin), but relying solely on them means leaving crucial insights behind. While many enterprises have moved beyond surveys, the majority are still overly reliant on them. And when you get to mid-market or small businesses? The survey slapping gets exponentially worse. Now is the time to start looking beyond the questionnaire and your Likert scales. The email survey is slowly becoming digital dust. And the capabilities to get you there are readily available. How are you evolving your customer listening strategy beyond traditional surveys? #customerexperience #cxstrategy #customerinsights #surveys

❌ Smart CX Leaders Don’t Read a Million NPS Comments—They Model Them ✅ CX Opportunity: Use AI to Make Millions of Voices Actionable Too many CX leaders especially those in B2C fall into this trap: They launch an NPS survey to millions of customers… Then try to read through open-text comments manually or rely on spreadsheets and gut feel. 🚨 The result? Delays, missed trends, and zero scalability. Here’s the truth: 📊 When you have thousands—or millions—of NPS responses, manual review is NOT customer-centric. It’s a bottleneck. 🔧 The Better Way: Build an AI-Powered Text Analytics Engine Here's what leading CX teams are doing instead: 1. Data Collection: Centralize all NPS feedback (across web, app, email, etc.) in one place. 2. Text Preprocessing: Clean the data—remove noise, standardize language, and strip out irrelevant content. 3. Theme Detection (Unsupervised ML): Use clustering or topic modeling (e.g., LDA) to uncover emerging themes—without needing to predefine them. 4. Sentiment & Emotion Analysis: Layer in NLP models to detect tone and intensity—distinguishing between frustration, confusion, and delight. 5. Custom Tagging Model (Supervised ML): Train AI to tag comments by product areas, issues, personas, or root causes using historical data and human-labeled examples. 6. Trend Monitoring + Alerting: Get real-time signals when negative themes spike or high-value customers comment on broken moments. 7. Dashboards that Drive Action: Turn unstructured feedback into structured insight that product, ops, and CX teams can act on—weekly. 💡 The result? You go from drowning in feedback to scaling insights. From reactive reading… to proactive resolution. 👉 If your NPS program feels like a reporting tool, not a growth engine—AI might be the missing piece. #CustomerExperience #CXStrategy #NPS #AI #VoiceOfCustomer #TextAnalytics #CustomerInsights #CustomerCentricity #CXLeadership