Automating Customer Support to Boost Satisfaction

Most of the consulting I've been doing over the last few weeks has been related to setting up chatbots, generative AI for the front and back end of the service journey or knowledge management, and other automation tools for CX. When I come into these conversations, I first want to know what problem they're trying to solve and why they want to do it with automation. There are many problems automation can help you solve, but I’m finding too many people want to use it to replace a larger percentage of their workflow than is probably healthy for their customer experience. It CAN save time and money, but it still takes someone (or many people) to manage to make sure users are having the experience they deserve. Some Common Examples: Chatbots: A conversational chatbot requires constant management - your product and services CHANGE, so the chatbot needs training, new prompts, new decision trees, new conversation flows, etc. When you let them go stale they create infuriating, looping experiences for your customers. Auto-responders: Great when you want to let people know you received their request, trigger an update if wait times are longer than expected, or anytime you know your auto-response is 100% relevant to whatever action the customer took. When the path to contact support is a maze, users will take whichever path will get them to a text box - you can’t be certain they’re using the correct category, and then create an auto-response specifically for that category. Ticket Deflection: This usually comes in the form of serving knowledge base articles before a customer can reach out to support for a self-serviceable task. Again, this is great and can reduce your queue to mostly inbounds that require interaction from a person, but if you’re not keeping your KB content up to date, it’s useless and creates a headache fast. Be smart about the automation you're introducing to your service journey and make sure they're serving the customer and your team.

Over 50% of our support chats were resolved by our AI assistant last week. No human intervention! This didn’t happen by accident. For small business owners looking to automate support, the real work happens before you flip the AI switch. It starts with building a strong foundation, and getting your team onboard. Here’s how we did it: The Process 1. Audit your support history We analyzed thousands of past tickets and chats to identify the most common and repetitive questions. Yes, we did this with AI. 2. Build (or expand) your knowledge base We created over 1,000 new help articles in a single quarter—filling gaps, refining answers, and making sure every article was easy to follow. Yes, we also created new articles with AI. 3. Train the AI assistant We integrated our knowledge base with our AI assistant and ran extensive testing to improve responses and coverage. 4. Educate and align the team We openly communicated how AI would help, not replace our support team. We showed how it would reduce mundane work and free them up to focus on more strategic, meaningful customer conversations. 5. Monitor, learn, and iterate We continuously tracked resolution rates, flagged weak responses, and kept refining the system. The Results • Faster, more consistent support for customers • 50% drop in manual support chats • A more energized support team, now focused on deeper issues, proactive outreach, and customer success initiatives The Takeaway AI isn’t just a tool. It’s a mindset shift. If your team sees it as a threat, you’ll hit resistance. But if you bring them along—show them how it removes the boring parts of the job so they can focus on the impactful ones, you unlock a whole new level of engagement. The real power of AI isn’t about replacement. It’s about elevation. Elevate your team. Serve your customers better. And don’t skip the groundwork. #AI #CustomerSupport #Automation #SmallBusiness #SaaS #Leadership #CustomerSuccess #ecommerce

Came back from vacation Monday. Inbox? On fire.🔥 Buried in the chaos: a customer story that stopped me in my tracks (and made me so happy). A Customer Support leader at a fast-growing financial services company used AI to transform his team - in just a few weeks. This leader works for a financial services company that’s in high-growth mode. Great news, right? Yes! For everyone except his Customer Support team… As the business grew faster, they were bombarded with repetitive questions about simple things like loan statuses and document requirements. Reps were overwhelmed. Customers faced longer response times. The company has been a HubSpot customer for nearly 10 years. They turned to Customer Agent, HubSpot’s AI Agent, and got to work: - Connected it to their knowledge base → accurate, fast answers - Set smart handoff rules → AI handles the simple, reps handle the complex - Customized the tone → sounds like them, not a generic bot (you know the type) In a short space of time, things changed dramatically: - Customer Agent now resolves more tickets than any rep - 94.9% of customers report being happy with the experience - For the first time, the team can prioritize complex issues and provide proactive support to high-value customers It’s exciting to see leaders using Customer Agent to not just respond to more tickets, but to increase CSAT and empower their teams to drive more impact. 2025 is the year of AI transformed Customer Support. I am stunned by how quickly that transformation is playing out!

Learnings from transforming CX with Gen AI for a Financial Services giant in APAC 🚀 One of the largest Financial Services players in the APAC recently leveraged Verloop to transform its contact center. The outcomes? Transformational change in customer support experience which not only drove CSAT up but also helped them bring efficiency into their CX Ops. Here is a snapshot of outcomes and learnings Outcomes -------------- 1. About 30% increase in Customer Satisfaction score 2. 43% fewer tickets assigned to their support desk 3. 70% Reduction in Average Response Time 4. 30% Cost Savings by CX efficiency Learnings -------------- 1. Effort - Easier said than done; most models are great for building demos but a nightmare when implementing large complex scenarios 2. Focus - Niche-trained LLMs work better than a large model 3. Latency - Latency in response especially in audio calls is a deal breaker. 4. RAG + LLM - Balancing when to refer to RAG vs when should LLM handle the task takes a while 5. Cost - Models cost significant amount of money to run; attach and focus on business outcomes 6. Data Quality - Investing time in data cleansing and organization pays off massively 7. AI + Human - AI handles the repetitive tasks, while AI-assisted human agents are required for empathy and complex problem-solving 8. Keep Building - Continuous improvements and training of flows is critical more so in the first few months of launch Implementing Guardrails --------------------------- 1. Focus on Ethical AI usage with strict guidelines to ensure AI operates within ethical boundaries, maintaining transparency and customer trust. 2. Adhere to rigorous data privacy regulations to protect customer information. Protecto works like a charm! 3. A key trait of any such implementation is AI knowing when to hand over Launch Experience -------------------- 1. Collaborative Approach - Everyone is learning in this journey; engage early and frequently with all stakeholders 2. Stay Agile - Launch iteratively and keep improving instead of one big bang launch 3. Human training - Focus on training all stakeholders; things are different vs structured data We started Verloop with the idea that the future of contact centers is AI-first, human-assisted. These engagements help us stay on the course and keep building towards our vision. We are already living in the future and it is slowly spreading everywhere! 🌟 #contactcenter #GenAI #CXTransformation #transformation Verloop.io CA. Ankit Sarawagi Melisa Vaz Nikhil Gupta Urvashi Singh Kiran Prabhu Ravi Petlur Kumar Gaurav

Most companies are using chatbots wrong. It’s not the AI that frustrates customers—it’s how businesses frame it. A new study found that when a chatbot provides customer service, satisfaction drops by 8-9% compared to a human—even if the experience is identical. Why? ❌ Customers assume chatbots = cost-cutting at their expense ❌ AI feels like a business decision, not a service upgrade ❌ People feel shortchanged—even when the chatbot works well But behavioral science flips the script: ✅ Frame chatbots as a better option (faster, 24/7, no wait times) → Satisfaction jumps 55% ✅ Offer a discount for chatbot users → Satisfaction returns to human levels ✅ Let people experience chatbot benefits firsthand → 75% more likely to stay with the brand 💡 The lesson? AI isn’t the problem—perception is. If you don’t account for fairness and trust, AI creates friction. But when used strategically, chatbots can boost satisfaction, loyalty, and conversions. 🔥 Love research-backed insights like this? Subscribe to Science Says—they break down the latest studies into actionable takeaways. 👇 Have you ever had a chatbot experience that felt better than talking to a human? What made it work? Interested in what the Maven AGI group thinks Justin Wright Jonathan Corbin #AI #BehavioralScience #CustomerExperience