AI in Post-Sale Support

If I were the VP of Support at an enterprise company dealing with repetitive customer support tickets, here’s how I’d use AI to power KCS and improve ticket resolution while turning my support agents into “heroes”: First, some context: - Most support tickets are recurring, yet agents have to field every single one of them individually (this is unscalable).  - Agents are only rewarded based on the number of tickets resolved and have a hard time improving support quality (can be unrewarding) The best way to go about this problem? Enabling agents to externalize documentation on their own and improve support quality with every logged request, using AI to power Knowledge-Centered Support (KCS) Here’s how I’d implement this at an enterprise company: 1) Democratize knowledge creation Support agents know customer issues best, so it doesn’t make sense to wait for technical writers (who are already swamped) to create knowledge articles. With the help of AI, you can enable support agents to generate knowledge articles on their own, just by clicking a button. 2) Externalize new knowledge All new knowledge articles can be pushed to your external customer help center/knowledge hub right away. With that, customers can either resolve issues on their own or ask an AI Chatbot (that has immediate access to all knowledge articles). 3) Iterate & improve knowledge Now that recurring tickets are handled, support agents can dedicate their time to tickets that *actually* need human help. AI can then help them update existing articles as similar requests come in. This is WAY more efficient than relying on technical writers because your agents are already “on the ground.” 4) Gamify support process On the backend, AI can track & display: - Which customer issues were resolved  - Which knowledge articles were referenced - How many customers were assisted by each agent - How many tickets were resolved or deflected This makes it easier to boost support morale because agents see the REAL impact of what they’re doing for customers and the company – in short, they become “heroes.” (We do this ourselves at Ask-AI) TAKEAWAY An AI-powered KCS will help you improve your overall customer experience. You can resolve customer issues faster, your support agents are empowered – and the VP of support can report better TTR and CSAT metrics. Any thoughts on this?

I used to work as a (human) customer service agent. My biggest learning: my customer service colleagues understood the customer better than anyone else, but their perspectives were rarely listened to. We'd try to tell our leaders about the recurring problems our product was facing, or the very real upsell opportunities we had if we only modified our approach slightly. Crickets. That's because customer support was seen largely as a cost center — a Band-Aid for customer problems — rather than a *hub to inform company decision-making* and, with it, unlock new revenue. This continues to be a missed opportunity for most businesses. But thankfully, this dynamic is changing for companies who are adopting the right AI Customer Experience (ACX) strategy. Here are some concrete ways I've seen companies use ACX to turn customer service from a cost center into a revenue-generating hub: - With AI, every customer touchpoint can now be transcribed, analyzed, and turned into real insights at almost no cost. Instead of conversations being recorded and never analyzed, companies can now extract valuable learnings with minimal effort and bring their customer-facing teams far closer to their R&D. - Smart companies are using AI to identify recurring issues and address them proactively. That confusing sign-up process that trips everyone up? It's now possible to flag and resolve it for good. Soon, you'll trust your AI software developer agent to make the change and push it to production autonomously. But the most effective AI-powered software development will be scaffolded by strong customer service AI. - New product development: Insights from customer service are already uncovering untapped revenue streams thanks to AI. Your next breakout product? It's probably the one your customers are ALREADY asking for either directly or indirectly. - Serve to sell: the best customer service AI recognizes when buying more of a product or service is genuinely helpful to the customer. There are few things more annoying than being sold something you don't want — but when buying more of your product is a solution to the customer's problem, an upsell or cross-sell is great for the customer and the business. There are more and more examples every day of how AI is turning what was once an overlooked part of the business into a goldmine that generates revenue opportunities across the org. I'm going to try an experiment and start sharing more of these learnings from building at the frontier of customer service AI. Subscribe to my new newsletter, AI Customer Service Insights, where I'll be sharing my learnings from the field to help you stay ahead of the curve and let me know what you think!

Your Revenue Engine Has a Silent Leak. Here’s How to Find It (Before It Drains $1M+) CEOs and CROs of scaling unicorns: Your growth metrics may dazzle on paper, but beneath the surface, revenue is escaping, not through failed products or weak teams but through gaps in systems you’ve outgrown. Consider this: ❇️ In handoff delays between marketing and sales, 20% of your sales pipeline vanishes. ❇️ 35% of churn originates from customers who never fully adopted your product post-sale. ❇️ 50% of content created by marketing goes unused because sales can’t locate it when buyers ask. These are not hypotheticals. They’re patterns observed across companies scaling past $100M ARR. AI as a Diagnostic Tool, Not a Quick Fix The promise of AI isn’t automation—it’s visibility. ✅ Predictive Diagnostics: Machine learning models analyze CRM, support, and product usage data to identify at-risk accounts 30 days before churn. At a cybersecurity scale-up, this led to an 18% reduction in cancellations through preemptive, personalized interventions. ✅ Precision Lead Routing: Algorithms match lead behavior (e.g., engagement with technical documentation vs. pricing pages) to sales reps’ expertise. Using this approach, a fintech client reduced lead-to-opportunity cycle time by 22%. ✅ Post-Sale Engagement: AI tracks onboarding milestones and triggers tailored guidance. One SaaS company decreased early-stage churn by 25% by automating “success nudges” based on usage gaps. Three Leaks That Undermine Scale: ✴️ The Handoff Blind Spot Marketing qualifies leads; sales own closures. Yet neither team measures the cost of delayed follow-up. Example: A 4-hour response delay can decrease conversion likelihood by 60%. ✴️ The Mirage of “Happy” Customers NPS scores of 9/10 mask accounts at risk. AI cross-references sentiment data with usage declines (e.g., logins dropping from 10/week to 2/week) to flag hidden dissatisfaction. ✴️ Content Debt Marketing produces assets sales can’t deploy. AI-driven content hubs (searchable by pain point, vertical, or deal stage) ensure reps access relevant materials in real-time. Building a Leak-Proof Engine ☑️ Unify Data Silos Integrate CRM (Salesforce), support (Zendesk), and product analytics (Mixpanel) into a single dashboard. A logistics unicorn attributed $2M in recovered revenue to this step alone. ☑️ Rewire KPIs Shift marketing’s focus from MQLs to Pipeline Influence Rate (% of opportunities touched by marketing) and sales’ focus from closed deals to Time-to-Value Acceleration. ☑️ Deploy AI Sparingly Start with one high-impact leak (e.g., onboarding drop-offs). Use AI to diagnose, then build processes to address root causes. The Strategic Question for Leadership “Does our CRO have a real-time map of where revenue escapes, or are we relying on backward-looking reports?” If you are struggling with Revenue Leakages, contact Roarr Consulting Group (RCG). We can help fix them. #SaaS #sales #b2b #marketing #technolgy

🧠 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! 🚀