Streamlining Customer Support for Better Experience

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

You probably have more customer info than ever.⁣ So why can’t your team answer basic questions or make confident decisions?⁣ It’s because data lives in separate systems. Align your tools, insights & the people serving customers.⁣ ⁣ Here’s what that disconnect looks like every day:⁣ ✓ The agent answering the call can’t see the customer’s last chat.⁣ ✓ The supervisor reviewing performance can’t trace a customer issue from beginning to end.⁣ ✓ And service teams are expected to deliver great experiences without knowing what’s already been said or promised.⁣ ⁣ The path forward isn’t more tools.⁣ It’s fewer, smarter ones that are connected and accessible.⁣ ⁣ ❶ Start by mapping one customer journey with your cross-functional teams at the same table (in person if possible).⁣ ⁣ ❷ Identify where handoffs happen, where data gets lost, and where communication breaks — both internally and with the customer.⁣ ⁣ ❸ Then rebuild your systems so the right people have the right context at the right moment — without logging into five platforms or asking the customer to explain again.⁣ ⁣ That’s how you create Emotional Highs™:⁣ Not surface-level satisfaction, but a meaningful emotional lift that makes people stay, return, promote, and forgive when mistakes happen.⁣ ⁣ Loyalty isn’t driven by your tech stack.⁣ It comes from how people FEEL when every interaction is easy, efficient, and clearly built around their needs.⁣ Yes — feel. As in emotions. The thing that’s always driven buying decisions, even if companies pretend otherwise.⁣ ⁣ This isn’t a tech upgrade.⁣ It’s experience transformation.⁣ And it’s how you compete and win in today’s market.⁣ ⁣ Are YOU #DoingCXRight®?⁣ Need help with ❶ ❷❸ above? Message me. ⁣ 👉 Share + comment if you found this helpful so others can benefit.⁣ ⁣ #CX #TheFormula #Nextiva #CustomerExperience #CustomerService⁣

DoorDash reduced customer support errors by 90%. (Yes, you read that right.) With millions of daily support requests, their traditional systems were straining under inefficiencies. But instead of just patching up the issues, they reimagined their entire support model using AI. The result? A system that now delivers fewer errors, scales effortlessly, and ensures a seamless support experience for their growing customer base. But like any transformation, there were major blockers to overcome: 1️⃣ Wasted time searching for info, causing delays and frustration. 2️⃣ Manual extraction of info created bottlenecks, slowing operations. 3️⃣ Limited to English, unable to provide consistent support globally. So, what did DoorDash do? 🔹 Built an Intelligent RAG System: Their RAG system acts like a digital librarian, condensing queries and matching them to past cases and articles for accurate, context-rich responses. 🔹 Introduced LLM Guardrails for Quality Control: A dual-layer quality check ensures AI accuracy, reducing hallucinations by 90% and compliance issues by 99%. 🔹 Continuous Quality Monitoring with LLM Judge: A hybrid automated and human review system continuously improves response quality at scale. Here’s the real impact of this transformation: ✅ Thousands of support requests handled automatically every day ✅ Seamless multilingual support for a global customer base ✅ Support agents freed up to tackle complex issues, improving efficiency DoorDash didn’t just address support challenges—they built a scalable AI-powered system. Take note: When integrating AI at scale, focus on reimagining your entire operation, not just solving problems. AI-led support isn’t just a quick fix. It’s the foundation for a smarter, more efficient business. _______ PS. Visit my profile, Hiren, & subscribe to my weekly newsletter: - Get product engineering insights. - Catch up on the latest software trends. - Discover successful development strategies. #CustomerSupport #SupportAutomation #AIIntegration #Simform