Strategies For Handling High Volume Customer Inquiries

I recently sat down with the former VP of Global Customer Support at a $5B org. While there, she cut ticket volume by 25%. I asked her how she did it - and I'm shocked more CX leaders aren't doing the same. BACKGROUND Emily Ebersole, Customer Success Exec was the VP of Global Customer Support at Zapier. She helped reimagine customer support at the org, introducing live chat, automation, and AI - en route to slashing ticket volume by 25% while maintaining a completely flat headcount. But none of this happened with the flip of a switch. Most CX leaders either try to reinvent the wheel all at once or are paralyzed by the thought of innovating their processes. Both lead you to the same place: a dead-end. Instead, Emily took a fundamentally simple yet completely overlooked approach: She focused on one thing first, mastered it, then moved onto the next. First, she optimized for self-service by linking help articles to a contact form that led to a 15% decrease in ticket volumes. Then she made changes to their free user support, which cut another 10% of tickets. With more time and space available, she and her team then, and only then, tackled live chat by reworking the team structure and allocating more people toward high-value service offerings. Only their highest-paying customers had access to live chat to start - ensuring her team could handle the workload while making sure customers would get real value from it. Once proven out, they expanded live chat to mid-tier customers. And then, boom - generative AI hit the masses. Instead of waiting around to see what may or may not be possible with it, Emily and her team dove in head-first to experiment and see how it could help with support. In short order, a team member created a self-serve tool with ChatGPT and embedded it inside of Zendesk. Soon after, they scaled this tool throughout the support organization to drive further efficiency within their operations. TAKEAWAY If you want to improve customer support, you can't boil the ocean. You'll get nowhere fast. But if you isolate one area and optimize it, you gain the momentum, time, and space necessary to move onto another area and do the same. And when you get into the habit of doing this, you not only drive improvement in efficiency and effectiveness - you set yourself up to be ready to act quickly when a massive opportunity (like generative AI) comes along. I recently spoke about this with Emily Ebersole, Customer Success Exec on the CX Innovation Playbook Podcast. Listen on Spotify or Apple Podcasts.

✴️ Improving response accuracy with LLMs and Agents may not result in ticket deflection. 🔷 We learned that the hard way—with a recent customer. As early adopters of AI, they introduced chatbots. They iterated on various retrieval patterns. We brought in Knowledge Graphs, Graph RAG, and Agents. We boosted answer accuracy from 60% to over 80%. 🔴 But ticket deflection? Still under 5%. 🔬 So we rolled up our sleeves and dug in. 💡 What we uncovered was eye-opening. They didn’t need better AI. They needed a better system. We ran a range of analyses. Here’s what we found: 📈 Ticket Analysis 48% of tickets were repetitive, “how-to” queries. 📈 Content Analysis But 50% of those queries couldn’t be resolved—due to missing content, missing context, or poor curation. 📈 That left 24% of tickets that could have been deflected—if only there had been contextual content. 📈 User Journey Analysis 80% of users never engaged with existing content. They didn’t: Read the documentation Complete onboarding Search the help center 🔍 They Googled. Found little. Raised a ticket. 📈 That’s why self-serve initiatives resulted in less than 5% deflection— despite search accuracy improving to 80%. The results still didn’t change. 🛑 What shocked us was that users who did search... still raised tickets. 📈 Search Result Analysis 40% of users said the results seemed “sensible but not specific.” This is after UAT with SMEs rated search at 80% accuracy. 🔴 The system returned generic answers. Missed context. Ignored intent. And get this: They were 50% right— 50% of them were still doing keyword searches using one or two words in 2025! The gaps? 🔷 User personalisation 🔷 Contextual precision 🔷 Intent disambiguation It’s like hiring a genius— then locking them in a room with no clues. So we changed the system. 🔷 Introduced intent detection agents, personalization agents, Agentic RAG. 🔷 If the detected intent was “informational only,” we routed to RAG. Otherwise, we raised a ticket immediately. 🔷 Built continuous analyzers—for tickets, content, queries, logs, search quality, and user behavior. 🔷 These identified whether the issue was with content, queries, user behavior, or search itself. 🔷 Automated real-time escalations to content authors, search engineers, and UX designers—backed by hard data. In short, we built a system—not just an answering machine. The result? 🔷 Deflection jumped from under 5% to over 28%. 🔷 Content coverage, search accuracy, and user experience improved—with intervention attribution. 🔷 Most importantly, we now have a system we can monitor and manage. We’re not there yet— But we’re on a clear and a confident path to 50% deflection. 🟢 The truth? Great AI in a broken system still fails. But even average AI in a smart system can win. 🟣 So before you ask, “Why isn’t our AI working?” Ask this instead: Have we built the right world for it to work in?” Happy to share details on DM. ServiceNow

The best CX issues? The ones that never happen. Proactive CX isn’t about waiting for tickets—it’s about anticipating problems and solving them before they arise. It’s not just support. It’s strategy. At TUSHY - hellotushy.com, we built systems and a culture that made common issues disappear before they even surfaced— even during a 10,000+ ticket backlog and major supply chain breakdowns. Here’s the exact 4-step playbook we used to shift from reactive to proactive CX 👇 1. Build systems that prevent problems 💪 Proactive CX starts with data and visibility across the customer journey. We monitored: - NPS feedback - Return/refund reports - CSAT reviews These insights helped us identify root causes early and take action fast. For example: - Improved onboarding to reduce returns - Delivery transparency to cut “Where is my order?” tickets - Pre-purchase education to manage expectations 2. Train your team to think like strategists 🧠 We don’t hire “yes” people — we hire squeaky wheels. Our hiring process includes intentional flaws to see who flags them. 🚩 Training includes: - Product knowledge and plumbing basics - Customer communication skills - Shadowing senior agents We even named our onboarding teaching program the “TUSHY Ass-Room.” Yes, it’s weird. Yes, it works. 🍑 3. Use AI to enhance — not replace — the human touch ✨ AI helps us move faster without losing personalization. We use it to: - Draft replies from past macros and conversations - Mirror customer tone (even Shakespearean verse, if needed!) - Build internal reports and presentations in minutes But it’s never fire-and-forget — every agent tailors the response. AI supports. Humans lead. 💖 4. Turn CX into a revenue and retention engine 🤑 We track what really matters: - Repeat purchases as our north star - CSAT, imperfect but gives strong health check - First-response time across the team - Conversion rates tied to individual and team incentives And we go beyond support: - Live video install support - Proactive SMS and email flows - Campaigns that build trust before a single ticket is created Proactive CX doesn’t just reduce tickets — it drives loyalty, retention, and brand love. ❤️ We're not just aiming to sell a product; we're actively pushing and changing culture in North America.💦 It's a long play, and that relies on a long term strategy. TUSHY is 10 years old this year, I've been leading CX and building this playbook here for 5—and in many ways feels like we're just getting started! Are you building a support team that prevents problems — or just reacts to them?