AI-Driven Customer Insights for Better Service

"Learning to walk again, I believe I've waited long enough"  🎤 "Walk" by Foo Fighters Had a fascinating conversation with a group of CS leaders last week about AI. The dialogue reminded me of how we learn to ride a bike - wobbly at first, but gradually our brain forms new patterns until it becomes second nature. AI learns similarly, and it's transforming how we think about #CustomerSuccess. Here's what's blowing my mind: 🔎 Pattern Recognition: Just like how great CSMs spot customer health issues before they become problems, AI is identifying patterns humans miss. At Gainsight, we recently saw this firsthand when Staircase AI detected brewing sentiment issues in email threads that weren't even copied to our CS team. It caught subtle tone changes that signaled future churn risk. 🎯 Learning from Mistakes: Remember your first customer call? AI also improves through trial and error. One thing we've learned from implementing Staircase is that relationship patterns often hide in unexpected places - casual Slack messages sometimes reveal more about customer health than formal QBRs. 🌱 Unexpected Discoveries: The most exciting part? AI is finding patterns we never knew existed. Last week, our system identified a customer at risk not from negative sentiment, but from a sudden shift to overly formal communication - a pattern that often precedes vendor reevaluation. 🤝 Human + Machine Partnership: The future isn't about AI vs humans. It's about how we work together. Our best CSMs are using AI to analyze thousands of customer interactions instantly, freeing them to focus on building deeper relationships. One CSM told me last week: "AI handles the patterns, I handle the people."But here's what keeps me up at night: Are we moving fast enough? While we debate whether to embrace AI, our customers are already experiencing AI-powered experiences everywhere else. What unexpected patterns has AI helped you discover in your customer relationships?

On paper customer success is a great use case for GenAI, but this post does a good job articulating why the solution needs to be highly customized to every business and a purely horizontal solution might not be very useful. 5-Step Framework for AI-Powered Customer Insight Instead of the "dump and hope" approach, here's a systematic framework that's worked for me and many organizations I've consulted with: 1. Start with a problem hypothesis. 2. Build a taxonomy that maps to action. 3. Count what matters. 4. Analyze temporal trends. 5. Look for what’s missing For example, if churn is high after the first week, your hypothesis might be: "Users aren’t finding value fast enough." Now you can use AI to test that. You're no longer asking the model to "summarize feedback" — you're asking it to find signal related to that specific hypothesis. You’re guiding the model’s attention. Too often I see taxonomies that are intellectually clean but practically useless. They categorize feedback into buckets like "usability," "performance," or "pricing". Nice in theory. But where do you go from there? Instead, design categories that align with things you can actually change. For example: "Signup friction (UI issues)" "Signup friction (copy confusion)" "Lack of onboarding guidance" "Missing core feature expectations" "Breakdowns in customer support loop" Each label should suggest who needs to act and what they need to look at. If a tag doesn’t help someone make a decision or prioritize work, it doesn’t belong.

🧠 AI-First Use Cases for Customer Success, Account Management & Support It's not just sales that can benefit from AI-powered automation. We're also thinking on the customer experience and how we can better serve our customers leveraging AI in our workflows at Vanta: 🆕 Onboarding & Activation - Agentic AI-led Customer Onboarding – An autonomous AI agent walks customers through onboarding, dynamically adjusting based on user behavior, role, and progress. - Automated Customer Onboarding – AI sends tailored welcome messages, interactive walkthroughs, training content, and milestone reminders, with personalized progress tracking. - Onboarding Risk Prediction – AI flags customers likely to stall during onboarding based on usage signals, role, and industry, prompting human intervention at the right moment. 📊 Customer Health, Retention & Expansion - AI-generated Customer Health Scores – AI continuously monitors product usage, NPS scores, ticket volume, and sentiment to produce a dynamic, predictive health score. - AI-powered Renewal & Expansion Insights – Predictive models surface customers likely to churn or ready to expand based on product adoption, engagement signals, and historical behavior. - Automated QBR Generation – AI creates tailored quarterly business review decks using real-time usage data, benchmarks, and suggested action items for growth or risk mitigation. 🗣️ Feedback & Voice of the Customer - AI-powered Customer Feedback Collection & Tracking – AI gathers structured feedback from NPS, CSAT, support tickets, onboarding surveys, and calls, and categorizes it into themes for PM and GTM teams. - Product Feedback Loop Automation – When a customer submits a product request, AI logs and categorizes it, tracks request status, and automatically follows up when the request is fulfilled or addressed. 💬 Support & Issue Resolution - AI-driven Support Ticket Triage – AI prioritizes and routes incoming tickets by urgency, topic, and customer tier, suggesting answers or tagging the appropriate team. - Self-service AI Knowledge Assistant – A conversational AI assistant that provides customers with instant, contextual answers based on docs, past tickets, and product updates. - Auto-Response Suggestions – AI drafts first-response templates to support tickets, tailored to ticket context and customer profile, saving agents significant time. 🎯 Proactive Engagement - AI-Powered Play Recommendations – AI suggests proactive outreach plays for CSMs and AMs based on customer lifecycle stage, feature usage, or risk indicators. - Milestone Celebration Automation – Automatically send personalized emails or in-app messages when customers hit key milestones (e.g., passed audit, integrated first vendor), boosting engagement. - Usage Pattern Anomaly Detection – AI spots abnormal drops or spikes in usage and alerts the account team to investigate. Interested in solving these problems with us? Check out our Founder in Residence role opening! 🚀