Scaling Customer Experience Operations

Every time technology takes a big step forward, the same question comes up: What does this mean for people? During the Industrial Revolution, as machines took over manual tasks, they didn’t make people obsolete—they created new roles that allowed us to work in ways not possible. The arrival of computers and data processing didn’t just replace office tasks; it freed us up to tackle higher-level work, pushing us to new heights. Now, AI is doing the same. It’s not here to replace our strengths but to amplify them. For teams in marketing, sales, and customer service, AI provides powerful tools that make our jobs easier, faster, and more impactful, giving us more time to focus on what truly matters. In Marketing, AI creates, and people curate. AI can quickly draft content, but it’s people who polish it into something that truly connects with an audience. AI can provide a starting point, but it’s the human touch that makes it shine. In Sales, AI offers context, and people offer connection. AI can save time by preparing data and insights, allowing salespeople to spend more time with customers. While AI lays the groundwork, it’s the sales rep who builds trust and connection. In Customer Service, AI handles simplicity, and people handle subtlety. Chatbots can answer basic questions, giving human reps the chance to focus on complex issues that need empathy and a personal touch. AI isn’t here to take over; it’s here to power us. By handling tasks that can save us time, it frees us up to do what we do best—connect, create, and make a difference.

The Paradox of Growth: The Bigger You Get, the Less You Know I came across something that stuck with me: When companies scale, they gain users — but lose understanding. Not because they stop caring, but because their customer feedback starts living everywhere — support tickets, sales calls, forums, surveys, social media, and app store reviews. That thought really made me pause. I’ve seen this firsthand. When a company is small, every piece of feedback feels personal — every bug report or review has a face behind it. But as you grow, those voices scatter across platforms and departments. Support sees the frustration, sales hears the hesitation, leadership sees the numbers — and somehow, everyone’s looking at the same customers, but no one’s hearing them anymore. That, in my opinion, is the quiet cost of growth. This is the problem Enterpret is solving — by helping teams stay in tune with their customers even as they scale. Here’s how it works: → It collects real-time customer feedback from 55+ channels — support tickets, sales calls, social media (X, Reddit, Instagram, Facebook), app store reviews, community forums, surveys, Slack, and more. → It analyzes all that feedback using AI and tells you exactly what to fix or build next. → It maps everything through a customer knowledge graph that connects feedback, complaints, and requests by channel, user, and payment data. → It even provides a chat interface where you can directly ask questions, and AI agents that flag bugs or issues automatically. That’s why teams like Notion, Perplexity, Canva, Chipotle, and The Farmer’s Dog use it — to make sure customer voices never get lost in the noise. In my view, the real lesson here isn’t about using more tools — it’s about staying close to the people you build for. Here’s how I’d approach it: ✅ Centralize every piece of feedback — even if it’s messy. ✅ Look for patterns instead of isolated complaints. ✅ Use AI systems like Enterpret to uncover the “why” behind what customers say. Because in the end, growth shouldn’t make you deaf. It should make you listen better — just faster. How does your team make sure you’re hearing what customers really mean, not just what they say? #CustomerFeedback #AIProducts #ProductStrategy #VoiceOfCustomer #Enterpret #Leadership

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? 👇

How to Build a Top 1% Shopify Store in 2026 (After 10+ Years of Doing It) 1. Start With a Real Product and Positioning • Weak product positioning = low AOV, heavy discounting, no repeat buyers • Strong positioning = pricing power, loyalty, organic word of mouth • If you are blaming ads, creators, or algorithms, the product story is usually the problem • Top stores do not sell products, they sell a belief customers want to belong to 2. Build One Core Conversion Path • Homepage hero → collection or PDP → clear value proof → frictionless checkout • Too many paths kill momentum • Top stores obsess over one primary journey and remove everything else • Every pixel should push the customer forward, not sideways 3. Know the Shopify Math Cold • Contribution margin, blended CAC, AOV, repeat purchase rate, cash collected • Revenue screenshots mean nothing if cash flow is broken • If the math does not work at small spend, it will not work at scale • Great operators know their numbers weekly, not monthly 4. Drive Traffic With Direct Response Creative • Short form video wins in 2026 • 15 to 60 second videos that show the product in real life • Clear hook, clear problem, clear reason to buy now • The goal is not views, it is qualified traffic that converts 5. Turn Your PDP Into a Sales Page • Your product page is your closer • Show how it works, who it is for, why it is different • Answer objections before they scroll • Reviews, UGC, FAQs, and guarantees are non negotiable 6. Hammer the Customer With Trust Before Purchase • Retarget site visitors aggressively with education and proof • Founder videos, customer stories, behind the scenes, how it is made • Most brands stop after the click, top brands sell after the click • Trust is built through repetition, not clever copy 7. Build Retention Before You Chase More Traffic • Email and SMS are not optional • Welcome flows, post purchase flows, replenishment, loyalty • The money is made on the second and third order • Acquisition gets you attention, retention builds the business 8. Stay Ahead of Scale Constraints • Inventory, fulfillment, CX, creative output • Most Shopify brands stall because operations lag behind demand • If you wait for problems to show up, you are already late • Top operators build systems before they are forced to

Hiring good people is just the start. Onboarding well is the key to keeping them. The truth about weak onboarding: ↳ It costs you 2-3x more in the long run ↳ Creates unnecessary imposter syndrome ↳ Breeds preventable mistakes ↳ Kills momentum before it starts What strong onboarding actually looks like: 1. Structured First 90 Days • Clear milestones and wins • Regular check-in rhythm • Progressive responsibility increase 2. Support System That Works • Dedicated mentor assignments • Cross-team introductions • "Stupid question" channels 3. Resources Ready Day 1 • Updated documentation • Tool access pre-configured • Team processes explained 4. Learning Built Into The Schedule • Protected learning blocks • Practice environments • Feedback loops Stop expecting people to "figure it out." Start investing in their success. The best companies know: A slow start beats a false start. What was your best (or worst) onboarding experience? ♻ Share if you believe in better onboarding

Onboarding in a new job is often a box-ticking exercise with no real strategy. What people think onboarding is: 1 - Making sure a laptop is set up. 2 - A few meetings with people in the diary. 3 - A pen and pad. 4 - Welcome emails from the team. 5 - A quick tour of the office. What onboarding could (and should) be: 1 - Pre-boarding - the onboarding plan should start the moment they accept the job offer. The period between quitting their job and starting their new job can often be a lonely and uncertain time. You could send an email outlining what to expect, a virtual office tour, a quick call with their new manager, or even a coffee date with their team. 2 - A personalised plan with a detailed 30-60-90 day roadmap, including specific expectations and milestones. 3 - Regular check-ins and the opportunity to ask the same question more than once, without feeling stupid. 4 - An in-depth introduction to the company culture. It is talked so much about in the recruitment process, but so little in an induction. 5 - Why limit onboarding to a week when probation periods can be 3 to 6 months? Extend onboarding until the probation period ends to ensure continuous support and guidance, helping new employees truly succeed. Anyone had any poor onboarding experiences? Or any great ones you can share? #onboarding #probation #hr #recruiting

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? 💪

Scaling isn’t just about growth—it’s about creating lasting demand. 💡 Did you know? Most scale-ups fail because they build products before fully understanding demand. If you’re struggling to scale, it’s not just your product—it’s the demand engine behind it. Too many scale-ups hit a wall because they: ↳ Misunderstand their market’s pain points. ↳ Lack scalable systems for distribution. ↳ Skip the trust-building phase entirely. Scaling without demand is like building a skyscraper on quicksand—it won’t hold. Here’s how to create real demand and build momentum: 1️⃣ Revisit Customer Personas Regularly ↳ Market needs evolve—your personas should too. ↳ Pro tip: Interview your best customers quarterly to uncover shifting priorities. 2️⃣ Focus on Micro-Niches Before Scaling Wide ↳ Master one niche before targeting broader audiences. ↳ Success in one market builds credibility for the next. 3️⃣ Automate While Staying Personal ↳ Use tools to handle repetitive tasks but keep customer touchpoints human. ↳ Example: Automate onboarding but send personalized check-ins during the first 30 days. 4️⃣ Build Feedback Loops for Every Step ↳ Create systems where sales, marketing, and product teams align. ↳ Regular feedback refines your approach before scaling failures multiply. 5️⃣ Turn Customers into Evangelists ↳ Delighted customers do the marketing for you. ↳ Encourage referrals and amplify testimonials—they’re your secret growth engine. Scaling is about strategy, not luck. When you understand your market, align your systems, and deliver exceptional value, demand doesn’t just grow—it compounds. 💡 Which of these strategies will you implement first? Share your thoughts below or message me to dive deeper into scaling smarter. ♻️ Share this with someone who deserves to hear it. 👉 Follow Ben Botes for more insights

In 2026, I think the brands we'll view as being truly customer-led will do two things at once. First, they'll automate the simple stuff fast. Second, they’ll bring a real human in on purpose when it matters. Most teams are treating this stuff like a tradeoff, as in, “If we use more AI, the experience will feel cold.” But this can only be true when you automate the wrong moments. The pro move is to design the human moments the same way you design the digital ones; you have to name them, protect them, staff them, train for them, and measure them. Here are the moments that should never be left to automation alone. High stakes money moments. This would include a large disputed charge, a denied claim, a sudden fee, or a mortgage problem. High emotion moments. Like a health issue, a death in the family, a lost bag with a laptop or other expensive item, anything before or during a wedding, or a ruined trip. High trust moments. A new account, a fraud scare, or anything that makes a customer think, “Should I still trust you after this?” In those moments, speed is not the only job. Judgment is the job. Tone is the job. Ownership is the job. Empathy is the job. So yeah, automate. But do it with rules in place. Use automation to clear the runway, but use people to land the plane. Here’s a weekend challenge if you’re up for it: write down your top 10 “human-required” moments, and see if a customer can reach a real person in under 60 seconds when they have one. The question every CX leader should be asking right now is simple: Which moments in my business deserve a human, every time? #customerexperience #customersuccess #journeymanagement

Every company says they listen to customers. But most just hear them. There's a difference. After spending years building feedback loops, here's what I've learned: Feedback isn't about collecting data. It's about creating change. Most companies fail at feedback because: - They send random surveys - They collect scattered feedback - They store insights in silos - They never close the loop The result? Frustrated customers. Missed opportunities. Lost revenue. Here's how to build real feedback loops: 1. Gather feedback intelligently - NPS isn't enough - CSAT tells half the story - One channel never works Instead: - Run targeted post-interaction surveys - Conduct deep-dive customer interviews - Analyze product usage patterns - Monitor support conversations - Build customer advisory boards - Track social mentions 2. Create a single source of truth - Consolidate feedback from everywhere - Tag and categorize insights - Track trends over time - Make it accessible to everyone 3. Turn feedback into action - Prioritize based on impact - Align with business goals - Create clear ownership - Set implementation timelines But here's the most important part: Close the loop. When customers give feedback: - Acknowledge it immediately - Update them on progress - Show them implemented changes - Demonstrate their impact The biggest mistakes I see: Feedback Overload: - Collecting too much data - No clear action plan - Analysis paralysis Biased Collection: - Listening to the loudest voices - Ignoring silent majority - Over-indexing on complaints Slow Response: - Taking months to act - No progress updates - Lost customer trust Remember: Good feedback loops aren't about tools. They're about trust. Every piece of feedback is a customer saying: "I care enough to help you improve." Don't waste that trust. The best companies don't just collect feedback. They turn it into visible change. They show customers their voice matters. They build trust through action. Start small: 1. Pick one feedback channel 2. Create a clear process 3. Act quickly on insights 4. Show results 5. Scale what works Your customers are talking. Are you really listening? More importantly, are you acting? What's your approach to customer feedback? How do you close the loop? ------------------ ▶️ Want to see more content like this and also connect with other CS & SaaS enthusiasts? You should join Tidbits. We do short round-ups a few times a week to help you learn what it takes to be a top-notch customer success professional. Join 1999+ community members! 💥 [link in the comments section]