Data-Driven Customer Experience Improvements

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

The PMs who win in the next wave won't be the ones who figured out how to prompt to build. They'll be the ones who figured out how to run 10x the customer learning with the same team. Here's why that matters right now. AI has handed engineering teams a jetpack. Cursor. Codex CLI. Claude Code. The delivery side of product development — build, specify, launch — is being automated at a breathtaking pace. But as Andrew Ng recently pointed out, the real bottleneck today isn't coding. It's discovery. While everyone raced to accelerate shipping, the question mark moved upstream. We now have the ability to build faster than we've ever been able to learn. And building fast on the wrong insight isn't speed — it's just expensive mistakes, sooner. The good news: the same AI revolution is quietly making discovery dramatically more powerful too. A few of the emerging use cases: 1️⃣ Analyzing feedback at scale. What used to require a researcher and two weeks can now be done by a PM in an afternoon — feeding thousands of NPS verbatims, support tickets, or app reviews into an AI and getting back a structured synthesis of themes, patterns, and verbatim quotes. 2️⃣ Automating feedback rivers. Tools like Reforge Insights, Enterpret, and Kraftful now continuously monitor customer feedback across every channel and surface actionable signals without anyone having to manually triage. 3️⃣ AI-moderated user interviews. Platforms like Reforge and Listen Labs are making it possible to run interviews at a scale that was never feasible with human moderators — turning what used to be 10 interviews into 100. 4️⃣ Discovery via prototypes. With vibe-coding tools like Lovable, v0, and Bolt, PMs can now build functional prototypes and gather real behavioral data — heatmaps, drop-offs, in-product surveys — before a single line of production code is written. 5️⃣ Natural language metric analysis. Ask your database a plain-English question, get a chart back. No SQL. No waiting for a data analyst. The feedback loop between a hypothesis and an answer just collapsed from days to minutes. The teams that wire these workflows together won't just be better informed. They'll develop a sharper product intuition — the kind that David Lieb (Founder of Google Photos, Partner at YC) described as "the world's most sophisticated machine learning model ever created." Join me Thursday, March 5th at the Lean Product Meetup with Dan Olsen in Mountain View, CA where I'll be sharing the exact 10 AI discovery workflows I now rely on to help me decide what's worth building faster 👉 https://lnkd.in/gfrJVsd3

From query to knowledge in seconds. That’s the promise of RAG systems. Instead of relying only on what a model learned during training, a RAG pipeline retrieves relevant information from external sources and uses it to generate accurate, grounded responses. Here’s how the architecture typically works. - Input Layer The process begins with the user query. System prompts guide model behavior while the system connects to knowledge sources such as documents, databases, internal knowledge bases, APIs, or enterprise systems. The query is then structured for retrieval. - Retrieval Processing The query is converted into a vector embedding, which represents its semantic meaning. The system performs vector search in a database to find similar documents. Similarity matching ranks results and top-K selection chooses the most relevant chunks of information. - Context Assembly The selected pieces of information are combined into a structured context. This retrieved context becomes the knowledge the model will use to answer the question. - Reasoning Layer The model analyzes the query and retrieved context together. It integrates external knowledge, performs multi-step reasoning when needed, and generates responses grounded in the retrieved documents. - Consistency Checking The system verifies that the generated answer aligns with the retrieved sources to reduce hallucinations and improve reliability. - Response Layer The response is structured clearly for the user. Citations may be included, confidence levels assessed, and the final output delivered to the application or interface. - Feedback Loop User feedback and system monitoring help improve the pipeline. Knowledge bases are updated, embeddings refreshed, and retrieval strategies optimized over time. RAG systems work because they combine vector search, knowledge retrieval, and LLM reasoning - allowing AI to answer questions using current, trusted information. Where are you using RAG today - internal knowledge assistants, customer support, or enterprise search?

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]

I’ve been experimenting with ways to bring AI into the everyday work of telco — not as an abstract idea, but as something our teams and customers can use. On a recent build, I created a live chat agent I put together in about 30 minutes using n8n, the open-source workflow automation tool. No code, no complex dev cycle — just practical integration. The result is an agent that handles real-time queries, pulls live data, and remembers context across conversations. We’ve already embedded it into our support ecosystem, and it’s cut tickets by almost 30% in early trials. Here’s how I approached it: Step 1: Environment I used n8n Cloud for simplicity (self-hosting via Docker or npm is also an option). Make sure you have API keys handy for a chat model — OpenAI’s GPT-4o-mini, Google Gemini, or even Grok if you want xAI flair. Step 2: Workflow In n8n, I created a new workflow. Think of it as a flowchart — each “node” is a building block. Step 3: Chat Trigger Added the Chat Trigger node to listen for incoming messages. At first, I kept it local for testing, but you can later expose it via webhook to deploy publicly. Step 4: AI Agent Connected the trigger to an AI Agent node. Here you can customise prompts — for example: “You are a helpful support agent for ViewQwest, specialising in broadband queries – always reply professionally and empathetically.” Step 5: Model Integration Attached a Chat Model node, plugged in API credentials, and tuned settings like temperature and max tokens. This is where the “human-like” responses start to come alive. Step 6: Memory Added a Window Buffer Memory node to keep track of context across 5–10 messages. Enough to remember a customer’s earlier question about plan upgrades, without driving up costs. Step 7: Tools Integrated extras like SerpAPI for live web searches, a calculator for bill estimates, and even CRM access (e.g., Postgres). The AI Agent decides when to use them depending on the query. Step 8: Deploy Tested with the built-in chat window (“What’s the best fiber plan for gaming?”). Debugged in the logs, then activated and shared the public URL. From there, embedding in a website, Slack, or WhatsApp is just another node away. The result is a responsive, contextual AI chat agent that scales effortlessly — and it didn’t take a dev team to get there. Tools like n8n are lowering the barrier to AI adoption, making it accessible for anyone willing to experiment. If you’re building in this space—what’s your go-to AI tool right now?

For the last 20 years, we’ve built VoC programs around the same formula: send surveys, wait for responses, analyze, react. It’s clean. It’s measurable. I also think it’s wildly out of step with how customers live and interact every day. Over the next several years, I think VoC shifts from interruption-based to observation-based. Passive signal capture from wearables, devices, connected products, in-app behavior. We’ll have a more honest picture of the customer experience than any survey ever gives us. This data will help us predict what’s about to happen and give every brand the chance to act before the customer ever raises a hand. Leading brands will blend passive signals with targeted, active listening. They’ll also give instant value back to the customer for every piece of data they share, whether it’s volunteered or detected. Everyone else? They’ll still be chasing CSAT responses while fewer and fewer customers fill out surveys. On Monday, here’s where to start if I were you: Compare where you think you’re getting feedback to where customers actually express themselves. Document the gaps. Test one new signal source line app behavior, device data, or voice tone in calls, and see how it changes your insight. Identify how you can route every signal into a system that can respond instantly, not just analyze later. Make every piece of feedback, whether active or passive, trigger something tangible for the customer. Build comfort with behavioral data, machine learning outputs, and multi-signal analysis on your team. VoC is about to stop asking questions and start delivering answers. The only question left is: will your program be ready when the shift happens? #customerexperience #voc #surveys

Many of you may not be familiar with the details of Amazon COSMO. As early adopters of these evolving trends, sharing more insights. ➡️What Is COSMO? COSMO (Common Sense Knowledge Generation) goes beyond traditional keyword-based search. It deciphers the why behind purchases. For example: Someone buys a memory foam pillow not just because it's "comfortable," but because they suffer from neck pain and need support for better sleep quality. By tapping into deeper customer intent, COSMO makes Amazon smarter at connecting products with real needs, improving search relevance, and delivering personalized shopping experiences. ➡️For sellers, COSMO means: ✅Listings need to be optimized for intent, not just keywords. ✅Backend attributes and product information (titles, descriptions) must align with customer search behaviour. ✅PPC strategies must shift from volume-focused targeting to intent-driven campaigns. COSMO rewards those who optimize listings and advertising strategies for genuine customer intent. ➡️What We’re Seeing Already ✅Mobile search results now push filters more prominently, creating a different shopping experience from desktop. This trend is just the beginning of COSMO’s impact. ✅Amazon’s relevance scoring now bridges gaps between search intent and product information. Products optimized for intent will rank higher organically. ➡️How to Stay Ahead of COSMO ✅Optimize Product Listings: Align titles, descriptions, and attributes with customer intent. Close semantic gaps by understanding the reasons behind customer searches. ✅Adjust PPC Strategies: Focus on ads that match search intent, not just high-volume keywords. Leverage COSMO’s insights to discover new, relevant keywords. ✅Monitor Search Trends: Use SQP and other Amazon reports to track changes in search behaviour. Adapt daily to refine your strategy and outpace competitors. ➡️What This Means for PPC Ranking PPC ranking for keywords may become obsolete, and COSMO will make ad spending more efficient in the long run. #amazon #amazonadvertising #amazonads

🔥 𝗪𝗲 𝗖𝘂𝘁 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗦𝘂𝗽𝗽𝗼𝗿𝘁 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗧𝗶𝗺𝗲 𝗳𝗿𝗼𝗺 𝟰 𝗛𝗼𝘂𝗿𝘀 𝘁𝗼 𝟰𝟳 𝗦𝗲𝗰𝗼𝗻𝗱𝘀 𝗨𝘀𝗶𝗻𝗴 𝗧𝗵𝗶𝘀 𝗡𝟴𝗡 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 Most SaaS companies are drowning in support tickets. We automated ours with AI. 𝗛𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝗲𝘅𝗮𝗰𝘁 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄: → 𝗚𝗺𝗮𝗶𝗹 𝗧𝗿𝗶𝗴𝗴𝗲𝗿 captures support emails instantly → 𝗚𝗲𝗺𝗶𝗻𝗶 𝗧𝗲𝘅𝘁 𝗖𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗲𝗿 categorizes by urgency + intent (refund/bug/feature) → 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 orchestrates the decision logic with memory and context awareness → 𝗣𝗶𝗻𝗲𝗰𝗼𝗻𝗲 𝗩𝗲𝗰𝘁𝗼𝗿 𝗦𝘁𝗼𝗿𝗲 retrieves relevant docs from 2,000+ past solutions via semantic search → 𝗗𝘂𝗮𝗹 𝗚𝗲𝗺𝗶𝗻𝗶 𝗠𝗼𝗱𝗲𝗹𝘀 generate accurate, brand-consistent responses → 𝗔𝘂𝘁𝗼-𝗿𝗲𝗽𝗹𝘆 𝘀𝗲𝗻𝘁 𝘃𝗶𝗮 𝗚𝗺𝗮𝗶𝗹 - customer gets help in under 60 seconds 𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁?  1. 87% of Tier-1 queries resolved without human intervention  2. The support team now focuses on complex issues only  3. Customer satisfaction jumped 34%  4. Operating costs down 60% This isn't about replacing humans. It's about giving them leverage. 𝗕𝗲𝘀𝘁 𝗽𝗮𝗿𝘁? Built entirely in N8N - no custom code, fully customizable, scales infinitely. If you're a CTO, VP of Ops, or Head of CS dealing with ticket overload, this architecture works for SaaS, e-commerce, and service businesses handling 500+ monthly support requests. Want the workflow template? Comment "WORKFLOW" below 👇 #N8N #AIAutomation #CustomerSupport #SaaS #WorkflowAutomationRetry

Do you know the reason why most AI apps fail ? Not because the model is bad - but because the system design is weak. If you’re building LLM apps, agents, or AI platforms, traditional backend knowledge isn’t enough anymore. You need AI-native system design. Here are 15 must-know system design concepts for AI Engineers that separate demos from production : 1) Latency Budget Users won’t wait. Split time across retrieval, model inference, tools, and streaming. 2) Throughput vs Concurrency Design for traffic bursts and parallel requests - not just average load. 3) Caching Cache embeddings, retrieved chunks, tool results, even final responses to cut latency and token cost. 4) Rate Limiting Protect against abuse, bots, and cost explosions using per-user, per-key, and per-endpoint limits. 5) Load Balancing Spread traffic across APIs, retrieval services, and inference endpoints to avoid hotspots. 6) Async Workflows (Queues) Move slow or heavy tasks to background queues for retries and durability. 7) Idempotency Retries should never duplicate actions - critical for agents calling payments, emails, or tickets. 8) Timeouts + Circuit Breakers Prevent stuck requests and cascading failures when tools or models go down. 9) Observability (Logs, Metrics, Traces) Track prompts, retrieval, tool calls, tokens, latency, and errors. Debugging without tracing is impossible. 10) Fault Tolerance + Fallbacks When vector DBs, tools, or models fail — degrade gracefully with safe responses. 11) Data Consistency + Eventual Consistency Use events and eventual consistency instead of fragile cross-service transactions. 12) Streaming Responses Send tokens as they’re generated to improve perceived speed. 13) Model Routing Not every request needs a premium model. Route by intent and complexity to save cost and boost speed. 14) Security + Privacy Protect against prompt injection, PII leaks, and data exfiltration using least privilege, redaction, and policy filters. 15) Evaluation + Guardrails Ship AI like a product. Add eval pipelines, safety checks, and output validation (JSON schemas). The big lesson: AI engineering is no longer just prompting. It’s distributed systems + reliability + cost control + safety + UX. Master these, and you don’t just build AI demos - you build production-grade AI. Save this if you’re serious about AI engineering. Share it with your team. This is the real stack behind Agentic AI.

Every Amazon director must know: Personalization hides what your customer sees. ____________________ THE TEST ____________________ We searched “dry shampoo spray” (a massive volume term) in two ways: 1️⃣ Prior purchase: Account had purchased Living Proof before 2️⃣ Clean browser: Zero search history ____________________ THE RESULTS ____________________ --> Completely different <-- 1️⃣ Prior purchase: Living Proof ranks organically + paid 2️⃣ Clean browser: ZERO organic ranking. Only a sponsored ad. Same keyword. Same product. Two totally different experiences. Why? Personalization. ____________________ SO WHAT ____________________ This impacts a few things: 1️⃣ ORGANIC LOSS: You'll pick up ZERO organic search for any new buyer. If they haven't clicked on your product before, it will never appear in organic search. Never. 2️⃣ PAID PREMIUM: You're paying 5X more in ad spend b/c variable bid rates are that much higher for terms you don't rank on organically. ____________________ NOW WHAT (how to fix) ____________________ --> Stop trusting your own browser --> Audit indexing using clean, logged-out sessions --> Check visibility across Amazon’s 13 ship zones using isolated IPs --> Place high-volume search terms like “dry shampoo spray” in indexable locations: title, bullets, description, and backend search terms Most brands don’t realize this is happening until they’re overspending and underperforming. If this feels familiar, let’s talk. We’ve solved this exact problem recently — and the results were immediate.