Streamlining Customer Support Processes

Last week, I talked about the possibilities of AI to make work easier. This week, I want to share a clear example of how we are doing that at HubSpot. We’re focused on helping our customers grow. So naturally, we take customer support seriously. Whether it’s a product question or a business challenge, we want inquiries to be answered efficiently and thoughtfully. We knew AI could help, but we didn’t know quite what it would look like! We first deployed AI in website and support chat. To mitigate any growing pains, we had a customer rep standing by for questions that came through who could quickly take the baton if things went sideways. And, sometimes they did. But we didn’t panic. We listened, we improved, and we kept testing. The more data AI collects, the better it gets. Today, 83% of the chat on HubSpot’s website is AI-managed and our Chatbot is digitally resolving about 30% of incoming tickets. That’s an enormous gain in productivity! Our customer reps have more time to focus on complex, high touch questions. AI also helps us quickly identify trends—questions or issues that are being raised more frequently—so we can intervene early. In other words, AI has not just transformed our customer support. It has elevated it. So, here is what we learned: Don’t panic if customer experience gets worse initially! It will improve as your data evolves. Evolve your KPIs and how you measure success- if AI resolves typical questions and your team resolves tricky ones, they will need more time. Use AI to elevate your team's efforts How are you using AI in support? What are you learning? 

Old CSM: Random tactics, reactive playbooks, and hoping for the best. New CSM: Repeatable, proactive systems to deliver, grow, and monetize customer value. If we are being honest with ourselves, most teams are not offering CSM but rather extended customer support. And no, the “classic” approach of monitoring product usage and satisfaction and checking in if they drop is not being proactive. (And AI can do it faster and better) Admittedly, it’s better to wait for customers to reach out if they have a problem, as research shows the majority never do and simply vanish. But what proactivity really means is to be one step ahead and lead customers all the way. To do that, you also need to be something else we keep talking about - a strategic CSM. If you have no clue what the customer journey has to look like, it’s going nowhere. (Except for the handful of customers that don’t need your help at all). After spending 5 years in CSM, I’ve identified 6 core elements of an effective system: 1. Customer Discovery → You need to understand your customers' needs that are defined by the goals they want to accomplish, the problems they need to solve, and their skill/knowledge gaps 2. Success Planning → You need to create a roadmap from your customers’ starting point to their desired outcome and break it down into actionable items (problems → tasks → skills/knowledge → inputs) 3. Customer Enablement → You need to build a training and education program  to help customers build the skills and knowledge to solve their problems (bottom-up) 4. Tracking results → You need to measure your customers’ progress towards their goals and identify churn risks from customers falling behind, to fix them in real-time 5. Demonstrating value → You need to highlight the benefits of working with you and your product to every stakeholder according to their goals in their own language 6. Monetizing value → You need to identify growth opportunities for your customers, show how they can seize them, quantify the additional value, and create demand for additional resources, feature,s or products CSM needs a hard reset, or it becomes obsolete. Vanity metrics and vibes are not paying the bills. PS: Check out the CSM Operating System --> https://lnkd.in/dwP6UpYG

Customer service conversations are the heartbeat of your business. They are a treasure trove of data about your operation and product flows, your agents and how they treat your customers, and your customers' preferences and needs. Yet, most contact centers analyze only a fraction of these interactions, using dated technology, leaving valuable insights untapped and decisions driven by incomplete data. At Replicant, we believe it’s time to bring every conversation to light. That’s why Conversation Intelligence is transforming customer service conversations into actionable insights. By analyzing 100% of calls with the latest audio AI, leaders can identify operational issues that lead to unnecessary calls, optimize agent performance, and pinpoint automation opportunities—turning their contact centers into strategic assets. For example, a large e-commerce provider used Conversation Intelligence to uncover an issue impacting 5% of their calls. Within one week, they implemented a fix that redefined their customer service strategy, eliminating inefficiencies and elevating their customer experience. This isn’t just about solving problems; it’s about leading with clarity. When every customer conversation becomes a data point for innovation, and AI summarizes it into actions for you, your contact center becomes a competitive advantage. The future belongs to leaders who anticipate, innovate, and act boldly. Are you ready to lead the way?

When you're deploying AI agents for a CX function, having a good Knowledge Base is a non-negotiable. Why? When optimized, it can empower your AI agents to deliver fast, accurate responses. When neglected, it can leave customers frustrated and agents underperforming. If you want to make sure your help center actually HELPS, here are 5 strategies you can deploy: 1. Structure your content in a Q&A format with clear headings and concise instructions to make it easy for both customers and AI to find relevant information. 2. Use precise keywords. If you have membership tiers, explicitly say which tier you're talking about. 3. Update content regularly with release dates for new features and remove outdated articles. 4. Use visuals (carefully). Reference images and annotations can improve usability—just make sure you have the bandwidth to keep them accurate. 5. Make agents accessible by providing a clear link to the AI agent channels for when customers need help beyond the answers available to them. A lot of companies view help centers as a nice-to-have but the truth is, the ROI is massive. And if you're thinking of using (or already use) AI agents for your customer support, you need to keep it well maintained so the agents can: → Identify knowledge gaps → Make suggestions to make your documentation easier to understand When your help center is optimized, AI agents can perform at their best, which translates to happier customers and less workload for your team. Read the full article for more strategies we recommend—link in the comments! 👇

AI is giving us unprecedented visibility into customer needs. The real opportunity? Using it to reimagine how we anticipate and prevent customer issues before they arise. Working with CX leaders has shown me that proactivity is not a switch you can flip. It’s a cultural shift that requires alignment and a shared commitment to collaboration across the entire organization. When that shift happens, everything changes. Success is measured not only by resolution speed, but by how effectively businesses prevent friction before it ever reaches the customer. I’ve seen this take shape in powerful ways: 👉 Using RingCentral AI Interaction Analytics to predict and resolve recurring pain points before they trigger support requests 👉 Turning support insights into product improvements 👉 Partnering with operations to eliminate bottlenecks that frustrate customers downstream When organizations adopt a proactive mindset, support shifts from a service function to a strategic advantage: one that fuels growth, deepens trust, and fosters loyalty that endures. #CustomerSupport #CustomerExperienceStrategy #RingCentral

This is how Adyen built an LLM-based ticket routing + support agent copilot to increase the speed of their support team. - Adyen used LangChain as the primary framework. The entire setup runs on Kubernetes for flexibility and scalability. - First, the ticket routing system uses an LLM to automatically direct support tickets to the right agents based on content analysis. This improved the accuracy in ticket allocation compared to their human operators. - For the support agent copilot, Adyen built a document management and retrieval system. It uses vector search to retrieves relevant docs from their internal support documentation and suggests answers to support agents, which cuts down the response time significantly. - The architecture is modular so their existing microservices are integrated easily too. Link to article: https://lnkd.in/gqUZZ6nd #AI #RAG #LLMs

Klarna shared yesterday that their AI assistant handled two thirds of their of their customer support enquiries in its first 4 weeks — but an important lesson for startups is that it achieved this in no small part by leveraging existing support docs. So what’s happening here? As well as being “always on”, in many contexts AI chat is simply a much more effective interface for the user, routing them to the right places in the fewest steps possible. Lots of teams struggle with low consumption rates in their product docs. People are often just not reading them. The data from Klarna’s AI assistant shows the answers are there — and they’re helpful — but the problem is often in retrieving them or reaching users at the right time. In the past weeks at Outverse, most companies we've spoken to are thinking about AI in their support stack, and several are considering how they might implement similar strategies to Klarna’s. There’s huge appetite here – and upside for companies. But this should be all be thought about from a systems level. You need good informational and documentation hygiene to get to a position where this level of deflection would be possible in the first place. So if you’re hoping to implement something similar, investing in the quality and depth of your product documentation is a prerequisite. And you should be looking at ways to ensure these knowledge assets are accurately maintained as your product evolves as well.

If your customers need a dictionary, a google search and a couple of phone calls to understand your process, we’ve got a problem. Leaders in regulated industries - like healthcare, banking, insurance and the others often sacrifice customer experiences at the altar of stringent compliance norms. Forms, procedures, and long processes become the standard. Jargons and tech talk get thrown around like confetti. Eventually it leaves customers feeling overwhelmed, frustrated, and helpless. When complexity becomes the default, customer relationships suffer. That's why we often see that as soon as a new entrant simplifies things, it triggers a big exodus of even loyal customers of existing brands towards the new option. Sometimes it happens quietly without a whimper. And as brand owners, if we end up noticing it too late, it hits growth, market share and profitability. Regulated industries can, and should create effortless customer experiences. Ease is not about bypassing compliance. It is about designing customer journeys that respect regulations while remaining: ✅ clear, ✅ empathetic, and ✅ straightforward. Here are THREE things I advise my clients who run a compliance-heavy business: 👉🏼 Make simplicity in communication non-negotiable. Replace jargon-filled language with clear, simple explanations. Start with the assumption that your customer does not understand a word of the compliances. The onus is always on you to make it easier to understand. 👉🏼 Proactivity goes a long way. Clarify expectations upfront. Explain the process upfront. Provide guidance and support upfront. This reduces customer effort, eliminates uncertainty and helps smooth sailing through compliance-related processes. 👉🏼 Infuse empathy into every interaction. Train teams to prioritize empathy. Train them on understanding customer perspectives and emotions. Train them to take ownership of the entire customer journey and not just a link in the chain. If you look at it now, these are three very simple things which I'm sure you already know in probably different contexts. But try applying it cohesively and consistently in the context of making your customer's life easy. That's when the magic happens! 🔮 P.S. Tag a company that went above and beyond to make a seemingly complicated task easy for you. Let's give them a shout out today! #CustomerExperience #CustomerDelight #Leadership #CustomerCentricity

To enhance customer service efficiency and satisfaction, implementing intelligent chatbots and automated response systems is key. These systems operate 24/7, reduce costs, and provide consistent, personalized interactions. Here's a short guide on the key aspects to consider: 👉 Types of Chatbots Traditional rule-based chatbots follow predefined rules to answer specific questions, offering limited interactions. AI-based chatbots use generative AI, machine learning, and natural language processing to understand and respond to a wide range of questions naturally and effectively. 👉 Automated Response Systems AI-powered Interactive Voice Response (IVR) systems, automated email replies, and instant messaging bots streamline customer support. These systems handle inquiries efficiently, routing them to the appropriate departments and ensuring quick, accurate responses across various communication channels. 👉 Security & Privacy Considerations To safeguard customer information, ensure that chatbots and automated systems comply with data protection regulations such as GDPR. Transparency is key; customers must be informed that they are interacting with a chatbot and offered options to connect with human operators when needed. 👉 Implementing Intelligent Chatbots Successful chatbot implementation starts with defining clear objectives to address specific customer service needs. Choose a platform that supports natural language processing and integrates with existing systems. Continuously train and optimize the chatbot using updated data for better performance. 👉 Enhancing Customer Service Personalize interactions using customer data to provide tailored responses and recommendations. Collect feedback to refine the chatbot's performance. Combine automated systems with human support to handle complex issues requiring a personal touch, ensuring comprehensive customer service. 👉 Measurement & Analysis Monitor performance metrics like resolution time, customer satisfaction, and chatbot usage to evaluate effectiveness. Use data analysis to identify areas for improvement, optimizing chatbot functionality and ensuring a continuously improving customer service experience. #CustomerService #AI #Chatbots Ring the bell to get notifications 🔔

Generative AI has been making waves in the industry for over two years, revolutionizing how businesses engage with customers. In this blog, the Engineering team at Noom shares how they developed their AI-powered customer support solution. Noom is a digital health company offering a subscription-based mobile app that helps users achieve their wellness goals, and it relies heavily on its chatbot for customer interactions. While directly leveraging ChatGPT-4 for customer chats was a promising first step, the team identified several challenges: issues with hallucinations, a lack of customization to user needs, and a mismatch with Noom's unique communication style. To address these challenges, the team developed a customized solution. They started by using Prompt Instruction with GPT-4 to form the foundation of their AI assistant. Next, they implemented Prompt Augmentation with Noom's Knowledge Base (RAG), Dynamic Prompts based on user data, and JSON Format Responses. These elements enabled the system to accurately process user messages, understand their needs, and deliver tailored responses. Furthermore, recognizing the importance of human connection, the team integrated classification models with LLMs to identify when a human touch was needed, ensuring users felt understood and valued. This approach is a great example of companies leveraging generative AI to create customized solutions that address their unique challenges. #datascience #machinelearning #generative #LLM #chatGPT #customer #chatbot – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gvJg5tMK