Best Practices For Handling High-Volume Support Periods

CX teams are often seen as a cost center. It’s a hard sell. Unlike marketing or sales, it’s not a “channel” you optimize. CX is messier. It's cross-functional. It touches everything. And because it’s not a channel, most companies struggle to prove its impact. CSAT & NPS become vanity metrics. Support feels reactive. And leadership struggles to tie CX to revenue. So what actually works? → Run deep customer research to find real friction: 1. Analyze high-volume support tickets to surface expensive, recurring issues 2. Audit transcripts to understand quality gaps 3. Map the customer journey to flag moments that drive escalations or churn → Build focused programs to solve specific problems: 1. Automate high-cost, low-complexity queries with AI — while monitoring quality 2. Train your team on known friction points to reduce escalation rates 3. Launch proactive outreach to deflect tickets before they happen (e.g., order delays, known bugs) → Tie each one to clear business metrics (revenue, churn, LTV) — not just feel-good KPIs: 1. Quantify ticket deflection and its direct impact on per-ticket costs 2. Measure churn reduction after resolving top friction points 3. Prove LTV uplift by improving resolution time and customer satisfaction Because here’s the truth: every business wants more revenue. And the path to revenue isn’t just through acquisition. It’s through retention. Loyalty. Word of mouth. All of which are directly shaped by your CX org. (We’re building Solidroad to help CX teams do exactly this. DM me if you’re curious.)

We built a Zendesk email assist AI agent and it's handling a full quarter’s work for one human support rep. Here's the step-by-step flow: 1. User sends a complex or nuanced product question to support@voiceflow.com 2. Tico (our AI agent) reviews the question and passes the content and intent. 3. The most fitting knowledge base is tapped via confidence level. 4. A personalized, accurate & highly-specific response is drafted. 5. The draft is slotted into Zendesk as a private comment. 6. Our team reviews, tweaks if necessary, and sends it to the user. This has slashed the onboarding and training time for support staff that's typically slowed down by the complexity of the product. The impact? ✅ Our support team is no longer just keeping up; they’re ahead, delivering faster, sharper responses. ✅ Customers feel understood, their issues addressed with pinpoint accuracy, boosting our CSAT scores. ✅ Tico’s continuous learning means every interaction makes it smarter, ready for even the most nuanced queries. So far, Tico Assist is tackling over 2000 tickets - a full quarter’s work for one human support rep, for less than the price of lunch. If you’re navigating high support volumes with a lean team, this type of Zendesk AI Assist Agent can help blend automation with quality for your customers. P.S. Tico doesn’t just fetch any answer. It pulls from the most relevant knowledge base (e.g. a technical code response for a developer question). From my post last week, this multi-knowledge base strategy is something that I think we will see much more of in CX this year.

Ticket deflection is one of the most painful challenges support leaders face. And it is costing more than just time. It is bleeding money.   The usual approach? - Knowledge articles on self-service portal - Customer and vendor training - Community groups   But here is what most teams miss: People raise tickets not because they want to They do it because they feel they have no other option.   The real question is not: “How do we resolve tickets faster?” It is: “How do we stop tickets from being created in the first place?”   The answer lies in 4 critical pillars:   1. Root Cause Analysis • Identify the hierarchy of issues raised • Analyze the time and cost of handling different types of tickets • Prioritize initiatives to deflect high-volume and high-impact tickets   2. Content Coverage • Identify content coverage gaps for high-volume topics • Escalate content gaps on top topics daily to SMEs • Proactively send content to customers likely to raise similar tickets   3. Search Accuracy • Identify the root causes of search inaccuracies • Personalize responses using auto-tagging and Graph RAG • Implement a proactive search rating system   4. In-Ticket Content Experience • Enable a multi-turn conversation experience to identify user intent • Offer real-time suggestions and contextual help before ticket submission   When these pillars come together: - Deflection becomes a system, not a guessing game - Teams get breathing room - Customers trust the process - Overwhelm turns into opportunity   That’s exactly what we’re building with Zinger Support AI. A problem-solving agent that goes way beyond answering basic questions.   We help support teams: • Reduce ticket volumes • Improve resolution times • Deflect repetitive queries without compromising experience What’s the biggest blocker you are facing in ticket deflection right now? Would love to swap notes. Let’s discuss your deflection strategy: https://lnkd.in/gQfTvFN8