Streamlining Customer Support Processes

The number 1 driver of customer satisfaction for accountants: Not: Technical ability Not: Your tech stack Not: Your website aesthetic It's answering the d*** phone. Responding to emails. Following through on [your desired comms channel] within [timeframe your client expected]. It isn’t a high bar these days, but simply being responsive and following through puts you ahead of the pack. 13 tips to standardize comms & align expectations: 1. Communicate a standard turnaround time. Doesn’t matter what it is, it just needs to exist. 2. Not all requests are created equal. Your turnaround time is your *acknowledgment* time. Many requests will take longer to resolve. 3. The only exception is when you’re OOO. So don’t forget your OOO, or pull in a teammate when you’re out. 4. Over-communication beats under-communication. Never be afraid to send a quick update, or a no-update update. 5. Don’t leave turnaround time up to your team to decide. For each role there’s a rule, and we’re all held to our turnaround time rules. 6. Your availability is the most scarce in your firm. If it isn’t, why won’t clients just go to you every time? 7. Design turnaround times to drive client behavior. For example an immediate call with an admin, same-day call with a staff, 72 hour call with the big boss. 8. No channel should jump the line. If you respond to that text, they’ll never follow the flow again. 9. Where possible trade synchronous work - let’s have a quick call to discuss - with asynchronous work - here’s a loom outlining my proposed solution, and a scheduling link if we still need to discuss. 10. In almost no situation should the big boss be taking unscheduled client comms of any kind. 11. Enable self-service wherever possible. Fetching docs from their own portal, paying an invoice online etc. 12. Integrate response times into how team members are incentivized. Be careful rewarding over-responsiveness, but keep a close eye on under-responsiveness. 13. Wrangle rogue channels. You make the rules about how clients can interact with your firm. Any comms outside those channels must be redirected. Clients are no different than mice in a maze. If they can get the cheese by texting you, calling you directly, they’ll never go through your team again.

AI in Customer Support isn’t new. I’ve been rethinking how we actually use it. Customer Support is moving past basic "faster replies" and learning to implement Claude as a core part of our workflow. The goal? Shifting from reactive firefighting to structured, scalable systems. It’s a work in progress, but here is the blueprint we’re using to turn Claude into a true CX reasoning engine: 1️⃣ It’s not about speed. It’s about structure. Yes, you can draft replies faster. But the real value comes from setting it up properly: → align it with your tone and guidelines → connect it to your knowledge base → define clear boundaries (what it can and can’t say) → train it to understand context, not just keywords That’s how you get consistent, reliable output across the team. 2️⃣ It helps move Support from reactive → proactive Used well, it’s not just answering tickets. It’s helping you: → detect sentiment and urgency → identify recurring friction points → surface gaps in self-service → spot early churn signals That’s where Support starts influencing the whole customer experience. 3️⃣ It fits into your existing workflows (not replaces them) The most effective setups I’ve seen are simple: → Claude + Zendesk → ticket analysis → Claude + Zapier → automate workflows → Claude + Gong→ review calls → Claude + Intercom → inbox support → Claude + n8n → workflow automation → Claude + Notion → knowledge management No complex rebuilds. Just better use of what you already have. 4️⃣ The quality of output = quality of input Small things make a big difference: → assign a role (support agent, CX lead, analyst) → provide context (customer, goal, constraints) → iterate with examples (good vs bad responses) Without this, you get generic answers. With it, you get something your team can actually use. From a leadership perspective, this isn’t about “adding AI.” It’s about designing how your Support team operates at scale. Because the goal isn’t to answer more tickets. It’s to build a system where fewer things break, and when they do, the experience still feels consistent. If you’re already using AI in Support, what’s actually working for you? 👇

We recently analyzed our support tickets at Yoodli AI Roleplays, and the results challenged a lot of our assumptions. Our team felt stretched and the instinct was to hire. But first, we asked a very important question: What is actually happening in our support data? At Yoodli: Great support isn’t about zero wait times, it’s about solving customer problems quickly and painlessly. Here’s what we found: 1. Tuesday, not Monday, is the real spike. Ticket volume jumps ~19% on Tuesdays, making it the busiest day of the week. Monday feels worse because of backlog, but the real surge comes after. 2. Support demand follows a global rhythm . Peak volume hits at 10am ET, but there’s a second spike around 7pm ET, driven by international users starting their day. Support isn’t just a 9 - 5 problem. 3. Early-morning demand is real, and growing. ~14% of tickets arrive before the US workday even begins. If you’re only staffed locally, those tickets are aging before anyone sees them. 4. Weekends aren’t quiet. ~13% of tickets come in on weekends. Ignore them, and you’re starting every Monday behind. 5. Growth shows up in support first. We saw a ~38% week-over-week increase in tickets. If you don’t plan for that, your team feels it before your dashboards do. The takeaway: We don’t need more people everywhere. We need the right coverage at the right times. That means: → Staffing for peak hours and global surges → Planning for weekends so Monday isn’t chaos → Treating midweek spikes as a distinct problem If your support team feels underwater, don’t just hire without studying the shape of your demand first. Shoutout to our support team for the incredible work their doing to give our customers a great experience. Alan Camperson, Christian, Dewey.

Imagine a doctor walks in, hands you pills, and says "take these." You ask: "For what?" Doctor says: "You will figure it out." You would walk out. That is what most reps do on discovery. They pitch the product in the first five minutes. Before asking one real question. I ran a diagnostic with an MSP client last week. Their reps were jumping to a demo in call one. Win rate was sitting in the high teens. We built a three-step rule. Diagnose. Confirm. Then prescribe. Nothing about the product until you have done the first two. Diagnose means ask questions that surface pain. "What is this costing you each month you do not fix it?" "What have you tried that did not work?" "What happens if nothing changes in 90 days?" Confirm means repeat what you heard. "So what I am hearing is... Is that right?" If they do not say yes, you diagnosed wrong. Go back. Prescribe means tie the solution to the exact pain they just confirmed. Skip the feature dump. Point the fix directly at what they agreed was costing them money. A mid-market HR tech client ran this playbook. Win rate climbed 47% in one quarter. Same price. Same product. Different conversation. Most reps skip to step three. Because step one and two feel slow. Slow in a first call is fast in a closed deal. P.S. If your team is jumping to a demo in call one, that is a process gap. Coaching alone will not fix it. Find out where the biggest leaks are across the team: https://lnkd.in/gypjv8gn

Let’s say your support center is getting hammered with repeat calls about a new product feature. Historically, the team would escalate, create a task force, and maybe update a knowledge base weeks later. With the tech available today, you should be able to unify signals from tickets, chat logs, and social mentions instead. This helps you quickly interpret the root cause. Perhaps in this case it's a confusing update screen that’s triggering the same questions. Instead of just sharing the feedback with the task force that'll take weeks to deliver something, galvanize leaders and use your tech stack to orchestrate a fix in real time. Don't have orchestration in that stack? Start looking into this asap. An orchestration engine canauto-suggest a targeted in-app message for affected users, trigger a proactive email campaign with step-by-step guidance, and update your chatbot’s responses that same day. Reps get nudges on how to resolve the issue faster, and managers can watch repeat contacts drop by a measurable percentage in real time. But the impact isn’t limited to operations. You energize the business by sharing these results in a company-wide standup and spotlighting how different teams contributed to the OUTCOME. Marketing sees reduced churn, operations sees lower cost-to-serve, and leadership sees a team aligned around outcomes instead of activities. If you want your AI investments to move the needle, focus on unified signals, real-time orchestration, and getting the whole business excited about customer outcomes....not just actions. Remember: Outcomes > Actions #customerexperience #ai #cxleaders #outcomesoveraction

Support teams face constant pressure to resolve cases faster without overloading engineering. For one Glean customer, valuable resources were tied up in avoidable tickets, MTTR (mean time to resolution) hovered at nearly two days, and agents spent hours manually triaging cases. Their goal: boost self-solves, improve MTTR, and reduce R&D reliance – without adding more tools. So they embedded Glean in Zendesk, giving agents prompts to quickly gather knowledge across all company data. In triage, agents use Glean to find similar tickets, summarize runbooks and past Jira investigations, and compile clear updates for customers or well-packaged escalations. That streamlined process now drives faster resolutions, smoother knowledge transfer, and consistent workflows—leading to: • 34% increase in self-solves with more future automation planned - this is incredible progress • 24% faster MTTR (1.9 → 1.5 days) • 2–4 hours saved per week for 85% of users (13–26 business days/year) • Reduced R&D involvement in lower-tier tickets By streamlining resolutions, knowledge transfer, and process consistency, the team achieved remarkable results – proof of what’s possible when AI is embedded into everyday workflows. Stories like this are energizing – showing how teams are using Glean to reimagine what they can accomplish.

Steps to Solve Support Tickets in SAP Success isn’t fixing fast, it’s fixing right the first time. Every unresolved ticket costs time, trust, and sometimes entire workflows. The real difference between good and great SAP support lies in method not luck. Here’s how the most efficient SAP teams handle tickets end-to-end: ✅ 1. Acknowledge the Ticket -Respond quickly. -Assign the right priority (Critical, High, Medium, Low). -Set expectations with users, because clarity upfront builds credibility. ✅ 2. Gather All Details -Ask for transaction codes, error messages, user IDs, and exact reproduction steps. -Screenshots and logs are your evidence, never work without them. ✅ 3. Analyze the Issue -Replicate it in a test or sandbox client. -Check key transactions: -ST22 → Short dumps -SM21 → System logs -ST03N → Workload analysis -Patterns here often expose the real story. ✅ 4. Identify the Root Cause -Missing or inconsistent master data? -Authorization or role issues? -Customization gaps? -Or a genuine SAP standard bug? -Define before you design. ✅ 5. Propose & Implement the Solution -If user side, give guided correction steps. -If system-side, raise a change request for configuration or development fixes. ✅ 6. Test & Validate -Perform all changes in QA before moving to production. -Capture screenshots, log outputs, and approval sign-offs. -Quality assurance isn’t a formality, it’s your safeguard. ✅ 7. Communicate & Close -Explain the resolution in plain business terms, no technical echo chamber. -Update the ticket with evidence, then confirm closure with the user. SAP support isn’t just technical, it’s operational storytelling. Each log you analyze, each fix you document, builds resilience into the enterprise. P.S. Don’t just solve SAP tickets, master the system that creates them. Save 💾 ➞ React 👍 ➞ Share ♻️ Follow Alok Kumar for more such amazing content on SAP & Enterprise Tech Transformation.

We removed IVR completely for 90 days. No press 1. No press 2. No "listen carefully, as our options have changed." Just a human answering the phone. Leadership was nervous. The data was not. Handle time went up 18%. Customer effort score dropped 34%. Repeat contacts dropped 28%. First call resolution went up 22%. Here is what surprised us most. Agent satisfaction went up, too. Turns out people who chose a career in customer service actually want to help customers. The IVR was not protecting our agents from volume. It was protecting our metrics from the truth. We brought IVR back eventually. But we rebuilt it around the customer, not around call deflection. The difference between the two is not technology. It is intention. What would your CX look like if you designed it for the customer first and cost reduction second? #cx #cxtechnology #cxtransformation

Here’s the roadmap for the first 90 days as a Customer Support leader: 1️⃣ Quantitative Support Analysis - Identify all areas where support resources are being misallocated or wasted. This might include overstaffed low-value channels, inefficient workflows, or poor escalation management. Re-allocate those resources to high-impact areas (eg. FCR) - Audit and optimize reporting systems to ensure clean, actionable data. Close gaps in ticket categorization, response time tracking, and CSAT/NPS data. 2️⃣ Qualitative Feedback from Customers AND Agents 🙋 Customer Perspective: - Conduct qualitative interviews with your top 10 happiest customers and your top 10 most dissatisfied customers. Unpack what drives satisfaction (or dissatisfaction) in their interactions with support. Spot trends and root causes in the support journey. - Shadow at least 5 live support interactions per week across channels (email, chat, phone) to identify recurring customer needs and operational friction points. 🧑💻 Agent Perspective: - Run qualitative interviews with your support agents. Ask them: * What are the most frustrating tools or workflows you deal with daily? * Which processes cause unnecessary delays or duplicate work? * What changes would make it easier for you to deliver great support? - Observe how agents use your support tools during live interactions. Look for inefficiencies like switching between too many platforms, unclear documentation, or delays in accessing customer context. 3️⃣ Quick Wins to Drive Impact Within 90 Days - Improve ticket routing and prioritization to ensure that critical issues are handled faster and by the right team. Many support teams leave SLAs unmet simply due to poor routing logic. - Simplify the self-service experience. Review and update your KB content to make it more reflective of the questions customers actually ask. - Streamline internal handoff processes between support tiers or other teams like product and engineering. Reduce resolution time by eliminating unnecessary back-and-forths. - Create an agent empowerment program. Provide quick wins for agents by removing common blockers, like slow systems or overly complicated approval processes. An empowered team = faster resolutions. - Highlight support’s wins. Build a repository of customer stories where support played a key role in success. Share these stories internally to drive alignment with sales, product, and customer success. 4. Set the Right Expectations Many companies expect a new support leader to focus solely on efficiency (e.g., reducing costs or ticket volume) in the first 30 days. This often backfires, leading to burnout, poor team morale, and degraded customer experience. Instead, focus on building the foundation: improving workflows, understanding customers AND agents deeply, and optimizing the team’s ability to drive meaningful resolutions. 💡 What’s your go-to strategy for the first 90 days in a new support role? 💪