Integrating Customer Feedback Into Goal Setting

If your CX Program simply consists of surveys, it's like trying to understand the whole movie by watching a single frame. You have to integrate data, insights, and actions if you want to understand how the movie ends, and ultimately be able to write the sequel. But integrating multiple customer signals isn't easy. In fact, it can be overwhelming. I know because I successfully did this in the past, and counsel clients on it today. So, here's a 5-step plan on how to ensure that the integration of diverse customer signals remains insightful and not overwhelming: 1. Set Clear Objectives: Define specific goals for what you want to achieve. Having clear objectives helps in filtering relevant data from the noise. While your goals may be as simple as understanding behavior, think about these objectives in an outcome-based way. For example, 'Reduce Call Volume' or some other business metric is important to consider here. 2. Segment Data Thoughtfully: Break down data into manageable categories based on customer demographics, behavior, or interaction type. This helps in analyzing specific aspects of the customer journey without getting lost in the vastness of data. 3. Prioritize Data Based on Relevance: Not all data is equally important. Based on Step 1, prioritize based on what’s most relevant to your business goals. For example, this might involve focusing more on behavioral data vs demographic data, depending on objectives. 4. Use Smart Data Aggregation Tools: Invest in advanced data aggregation platforms that can collect, sort, and analyze data from various sources. These tools use AI and machine learning to identify patterns and key insights, reducing the noise and complexity. 5. Regular Reviews and Adjustments: Continuously monitor and review the data integration process. Be ready to adjust strategies, tools, or objectives as needed to keep the data manageable and insightful. This isn't a "set-it-and-forget-it" strategy! How are you thinking about integrating data and insights in order to drive meaningful change in your business? Hit me up if you want to chat about it. #customerexperience #data #insights #surveys #ceo #coo #ai

Most customers don’t actually know their goals. We spend so much time trying to “uncover” customer goals, but what if there’s nothing to uncover? Not because customers don’t care. Not because they’re not strategic. But because they’ve never been asked to think that way. Most customers are thinking: “𝘐 𝘣𝘰𝘶𝘨𝘩𝘵 𝘵𝘩𝘪𝘴 𝘱𝘳𝘰𝘥𝘶𝘤𝘵 𝘣𝘦𝘤𝘢𝘶𝘴𝘦 𝘪𝘵 𝘸𝘪𝘭𝘭 𝘴𝘰𝘭𝘷𝘦 𝘢 𝘱𝘳𝘰𝘣𝘭𝘦𝘮.” Not: “𝘐 𝘥𝘦𝘧𝘪𝘯𝘦𝘥 𝘵𝘩𝘦 𝘰𝘶𝘵𝘤𝘰𝘮𝘦 𝘐 𝘸𝘢𝘯𝘵, 𝘵𝘩𝘦𝘯 𝘮𝘢𝘱𝘱𝘦𝘥 𝘵𝘩𝘦 𝘱𝘳𝘰𝘤𝘦𝘴𝘴 𝘤𝘩𝘢𝘯𝘨𝘦𝘴 𝘐 𝘸𝘰𝘶𝘭𝘥 𝘯𝘦𝘦𝘥, 𝘢𝘯𝘥 𝘵𝘩𝘦𝘯 𝘴𝘦𝘭𝘦𝘤𝘵𝘦𝘥 𝘵𝘩𝘦 𝘵𝘰𝘰𝘭 𝘵𝘰 𝘨𝘦𝘵 𝘮𝘦 𝘵𝘩𝘦𝘳𝘦.” Also, the initiative with your tool is new and something they don't do very often so they don't have same level of experience you and your company has. That’s where you come in. You’ve seen hundreds of accounts. You know what success should look like. You know the goals that actually drive results and the benchmarks that show if they’re on track. So here’s how to shift the conversation: 𝟭. 𝗖𝗮𝘁𝗲𝗴𝗼𝗿𝗶𝘇𝗲 𝗴𝗼𝗮𝗹𝘀 𝘁𝗵𝗮𝘁 𝗺𝗮𝘁𝘁𝗲𝗿:  • Save time  • Save money  • Drive leads  • Boost productivity 𝟮. 𝗧𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗲 𝘁𝗵𝗼𝘀𝗲 𝗶𝗻𝘁𝗼 𝗺𝗲𝘁𝗿𝗶𝗰𝘀:  • Open rate  • Cost per lead  • Leads per month  • Resolution time 𝟯. 𝗔𝗱𝗱 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸𝘀: Poor → Good → Best (ex: <5 Leads/mo, 6-15 Leads/mo, 16+ Leads/mo) 𝟰. 𝗥𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗴𝗼𝗮𝗹 𝘁𝗵𝗲𝘆 𝘀𝗵𝗼𝘂𝗹𝗱 𝗰𝗵𝗮𝘀𝗲. If they’re generating 1 lead a month, don’t aim for 25. Example: “𝘎𝘪𝘷𝘦𝘯 𝘺𝘰𝘶𝘳 𝘱𝘳𝘦𝘷𝘪𝘰𝘶𝘴 𝘱𝘦𝘳𝘧𝘰𝘳𝘮𝘢𝘯𝘤𝘦, 𝘐 𝘸𝘰𝘶𝘭𝘥 𝘳𝘦𝘤𝘰𝘮𝘮𝘦𝘯𝘥 𝘺𝘰𝘶𝘳 𝘧𝘪𝘳𝘴𝘵 𝘨𝘰𝘢𝘭 𝘣𝘦 10 𝘭𝘦𝘢𝘥𝘴/𝘮𝘰𝘯𝘵𝘩 𝘣𝘺 𝘦𝘯𝘥 𝘰𝘧 𝘘1, 𝘵𝘩𝘦𝘯 𝘸𝘦 𝘤𝘢𝘯 𝘴𝘩𝘪𝘧𝘵 𝘵𝘰 𝘱𝘩𝘢𝘴𝘦 2 𝘪𝘮𝘱𝘳𝘰𝘷𝘦𝘮𝘦𝘯𝘵𝘴 𝘥𝘳𝘪𝘷𝘦 𝘺𝘰𝘶𝘳 𝘵𝘰𝘸𝘢𝘳𝘥𝘴 20 𝘭𝘦𝘢𝘥𝘴/𝘮𝘰𝘯𝘵𝘩.”) The opportunity isn’t to 𝘢𝘴𝘬 for customer goals. It’s to help them 𝘤𝘩𝘰𝘰𝘴𝘦 the right ones together and guide the path forward. Because the CSM isn’t just a partner. You’re the strategic coach they didn’t even know they needed. How do you guide goal setting with your customers? #customersuccess

One of my favorite questions about AI is, "𝐇𝐨𝐰 𝐜𝐚𝐧 𝐈 𝐮𝐬𝐞 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 𝐭𝐨 𝐚𝐧𝐚𝐥𝐲𝐳𝐞 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐟𝐞𝐞𝐝𝐛𝐚𝐜𝐤 𝐚𝐭 𝐬𝐜𝐚𝐥𝐞?" Nearly every business collects customer feedback, but few analyze it effectively or consistently. Most rely on simple metrics (like NPS) or manually read through comments - neither approach surfaces the insights that can lead to real breakthroughs. The good news is that frontier AI models can now do an analysis that previously required expensive consultants or data science teams. Here's how to turn your unstructured customer feedback into actionable insights using gen AI: 1 Create a dedicated project space in a frontier model that saves history. I recommend Claude's "Projects", ChatGPT's custom GPTs, or Gemini's "Gems". Title it something like "Customer Feedback Analyzer" and include basic instructions about your business, products, and what insights matter most to you. 2 Upload your feedback data - survey responses, customer service transcripts, app reviews, social mentions, etc. More is better, and bias towards what you've collected the past few months. 3. Start exploring. Ask the model: "What are the top 10 themes emerging from this feedback? For each theme, provide 3 representative quotes and estimate what percentage of customers mentioned this theme." This gives you the big picture before diving deeper. 4. Go beyond sentiment analysis. Instead of the simplistic positive/negative breakdown, try: "Categorize feedback by customer emotion (frustrated, confused, delighted, etc.) and rank by intensity. What specific product/service elements trigger each emotion?" 5. Identify hidden opportunities. The real gold is in what customers aren't explicitly saying. Try: "Based on the feedback, what are customers trying to accomplish that my product isn't fully enabling? What adjacent problems could we solve?" Create competitive intelligence. Ask: "Which competitors are mentioned? What features or attributes do customers compare us favorably or unfavorably against? What competitive advantages should we emphasize?" 6. Prioritize action items. Finally, ask: "If you were my product manager, what 3 changes would create the biggest customer impact based on this feedback? Rank by expected ROI and implementation difficulty." The most valuable aspect of this approach is consistency over time. Run this analysis at least quarterly to track how customer perceptions evolve as you implement changes. What challenges have you faced analyzing customer feedback? Drop me a comment about what's working (or not) in your approach! If this kind of advice is helpful, then you'll love my AI for SMBs Weekly newsletter. Subscribe link in the comments. ✨ ✌🏻 ✨ #GenerativeAI #CustomerFeedback #SMB #DataAnalysis

I'd like to discuss using Customer Feedback for more focused product iteration. One of the most direct ways to understand customers needs and desires is through feedback. Leveraging tools like surveys, user testing, and even social media can offer invaluable insights. But don't underestimate the power of simple direct communication – be it through emails, chats, or interviews. However, while gathering feedback is essential, ensuring its quality is even more crucial. Start by setting clear feedback objectives and favor open-ended questions that allow for comprehensive answers. It's also pivotal to ensure a diversity in your feedback sources to avoid any inherent biases. But here's a caveat – not all feedback will be relevant to every customer. That's why it's essential to segment the feedback, identify common themes, and use statistical methods to validate its wider applicability. Once you've sorted and prioritised the feedback, the next step is actioning it. This involves cross-functional collaboration, translating feedback into product requirements, and setting milestones for implementation. Lastly, once changes are implemented, the cycle doesn't end. Use methods like A/B testing to gauge the direct impact of the changes. And always, always return to your customers for follow-up feedback to ensure you're on the right track. In the bustling world of tech startups, startups that listen, iterate, and refine based on customer feedback truly thrive. #startups #entrepreneurship #customer #pmf #product

𝗦𝘂𝗽𝗽𝗼𝗿𝘁 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 🧩 is the missing puzzle piece product needs. Here how to snap it into place: Support teams → You're sitting on a goldmine of insights. Product leaders → You're probably not hearing them. 1. 𝗧𝗵𝗲 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗪𝗮𝘆: 𝗦𝗺𝗮𝗿𝘁 𝗧𝗮𝗴𝗴𝗶𝗻𝗴 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 𝘠𝘰𝘶 𝘥𝘰𝘯’𝘵 𝘯𝘦𝘦𝘥 𝘈𝘐 𝘵𝘰 𝘤𝘳𝘦𝘢𝘵𝘦 𝘢 𝘴𝘵𝘳𝘰𝘯𝘨 𝘧𝘦𝘦𝘥𝘣𝘢𝘤𝘬 𝘭𝘰𝘰𝘱. 𝘚𝘵𝘢𝘳𝘵 𝘩𝘦𝘳𝘦:  • Start w/ 10 tags tied to actual customer problems (ask your best agent or export your data and ask AI)  • Make them specific (e.g., “Dashboard_Loading_Speed” > “Performance”)  • Train support to tag consistently (this is where most teams fail)  • Track weekly trends and build monthly impact reports  • Identify which issues cause the most customer pain 🛠 We started with just 10 core tags—and refined over time and nowuse 100s. Each week, ask: ↳ What came up most often? ↳ What took the longest to resolve? ↳ What ONE fix would move the needle most? It works—but it’s manual, and easy to miss emerging trends. 2. 𝗧𝗵𝗲 𝗠𝗼𝗱𝗲𝗿𝗻 𝗪𝗮𝘆: 𝗔𝗜-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗩𝗼𝗶𝗰𝗲 𝗼𝗳 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝘈𝘐 𝘴𝘶𝘱𝘱𝘰𝘳𝘵 𝘵𝘰𝘰𝘭𝘴 𝘩𝘢𝘷𝘦 𝘤𝘩𝘢𝘯𝘨𝘦𝘥 𝘵𝘩𝘦 𝘨𝘢𝘮𝘦: • Analyze 100% of support conversations • Tickets are auto tagged by AI which can trigger workflows • Use NLP to detect themes without tags • Surface hidden friction points, grouped by sentiment   My Support Ops team shares insights like: ↳ New friction points by user segment ↳ Confusing UX patterns ↳ Estimated support cost per issue ↳ Predicted impact of potential fixes 🔍 We recently spotted a major adoption blocker invisible in product metrics—but obvious in support conversations. The result? Better prioritization, faster fixes, happier customers. Whether you're leading support or building product, this is the shift: Customer feedback shouldn’t be anecdotal—it should be operational. Support has the data. Product needs the context. The puzzle only clicks when both sides connect. P.S. Which method are you using today? ———————————— 📩 𝘞𝘢𝘯𝘵 𝘧𝘳𝘰𝘯𝘵𝘭𝘪𝘯𝘦 𝘢𝘥𝘷𝘪𝘤𝘦 𝘢𝘯𝘥 𝘧𝘳𝘦𝘴𝘩 𝘱𝘦𝘳𝘴𝘱𝘦𝘤𝘵𝘪𝘷𝘦 𝘦𝘷𝘦𝘳𝘺 𝘰𝘵𝘩𝘦𝘳 𝘸𝘦𝘦𝘬? 𝘛𝘰𝘱-𝘛𝘪𝘦𝘳 𝘚𝘶𝘱𝘱𝘰𝘳𝘵 𝘥𝘦𝘭𝘪𝘷𝘦𝘳𝘴 𝘴𝘵𝘳𝘢𝘵𝘦𝘨𝘪𝘦𝘴 𝘵𝘰 𝘦𝘭𝘦𝘷𝘢𝘵𝘦 𝘴𝘶𝘱𝘱𝘰𝘳𝘵 𝘭𝘦𝘢𝘥𝘦𝘳𝘴. [𝘭𝘪𝘯𝘬 𝘪𝘯 𝘱𝘳𝘰𝘧𝘪𝘭𝘦]

Customer Success Leaders—If you're not actively shaping the Product Roadmap, you're missing a critical opportunity. The most effective organizations don’t treat CS as a participant—they rely on it as a strategic partner. Product teams should be co-designing the future with their customers. That means: ✅ Understanding emerging use cases and evolving needs ✅ Enhancing the product based on real customer insights ✅ Prioritizing with business impact and revenue in mind In today’s market—where consolidation, cost-cutting, and efficiency are top priorities—building a product that truly solves business challenges is the difference between success and irrelevance. So, how do you drive better alignment between CS and Product? Here’s what I've seen work: 1️⃣ Lead with Data & Insights -Identify the most adopted and least adopted product features -Pinpoint where customers are dropping off and why -Find personas and use cases that drive the most value -Look for patterns and trends across your customer base 2️⃣ Support Data with Customer Stories -Conduct interviews and surveys to capture direct feedback -Dive into workflows and edge cases to understand nuances -Align product evolution with customer goals and business objectives 3️⃣ Prioritize Product Feedback Strategically -Leverage customer data to rank impact and urgency -Tie feedback to revenue—renewals, expansions, and upsells -Ensure recommendations align with the broader product vision 4️⃣ Maintain an Open Dialogue -Establish a structured collaboration rhythm (bi-weekly syncs, Slack channels, shared roadmaps) -Keep all teams informed on designs, timelines, and priorities -Be clear, concise, and adaptable—Product is balancing competing priorities across the org 5️⃣ Close the Loop—Every Time -Set clear expectations with customers early and often -Enable Product teams to engage directly with customers for firsthand learning -Continue gathering feedback even after launch (beta programs, customer advisory boards) At the end of the day, great products are built by teams who stay close to the customer. CS should not be a passive observer in product development—it should be a driving force. When you get this right, you influence retention, expansion, and advocacy. And that’s a business win. __________________ 📣 If you liked my post, you’ll love my newsletter. Every week I share learnings, advice and strategies from my experience going from CSM to CCO. Join 12k+ subscribers of The Journey and turn insights into action. Sign up on my profile.