Employee Feedback Collection

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

How tech workers really feel about work right now With insights from over 8,000 of you (possibly the largest survey of its kind), Noam Segal and I are excited to share the results of our first-ever large-scale tech worker sentiment survey. What we discovered is that tech professionals are experiencing a fascinating mix of emotions about their careers in 2025. Our biggest takeaways: 1. Burnout is at critical levels: Almost half of our respondents are experiencing significant burnout. 2. Tech workers are more optimistic than we expected—but optimism is declining: 58.5% of tech workers remain optimistic about their roles, and 54.8% remain optimistic about their careers. However, there has been a significant negative sentiment shift over the past year. 3. Startup founders are the happiest people in tech: They’re the only group growing more optimistic while consistently outranking everyone else in workplace well-being. 4. Managers need help: Only 26% of tech workers consider their managers highly effective, while over 40% view them as ineffective. 5. Where people work makes little difference in how they feel about work—on the surface. But dig deeper, and hybrid workers are the happiest, remote workers are doing well, and in-office workers are experiencing hidden frustrations. 6. Small-company employees are doing the best: They outperform their large-company counterparts on nearly every work sentiment measure, from job enjoyment to sense of belonging. 7. The mid-career slump: Mid-career workers are struggling the most with burnout, lower job enjoyment, and the most pessimism about the future. 8. A widespread gap in career clarity: Many tech workers don’t know what they should be doing to continue developing in their careers. Two bonus takeaways you'll find at the end of the report: 1. Women are more burned-out (but more engaged) than men 2. AI is keeping tech workers up at night Don't miss the full report: https://lnkd.in/g8RZeFja

Getting the right feedback will transform your job as a PM. More scalability, better user engagement, and growth. But most PMs don’t know how to do it right. Here’s the Feedback Engine I’ve used to ship highly engaging products at unicorns & large organizations: — Right feedback can literally transform your product and company. At Apollo, we launched a contact enrichment feature. Feedback showed users loved its accuracy, but... They needed bulk processing. We shipped it and had a 40% increase in user engagement. Here’s how to get it right: — 𝗦𝘁𝗮𝗴𝗲 𝟭: 𝗖𝗼𝗹𝗹𝗲𝗰𝘁 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 Most PMs get this wrong. They collect feedback randomly with no system or strategy. But remember: your output is only as good as your input. And if your input is messy, it will only lead you astray. Here’s how to collect feedback strategically: → Diversify your sources: customer interviews, support tickets, sales calls, social media & community forums, etc. → Be systematic: track feedback across channels consistently. → Close the loop: confirm your understanding with users to avoid misinterpretation. — 𝗦𝘁𝗮𝗴𝗲 𝟮: 𝗔𝗻𝗮𝗹𝘆𝘇𝗲 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 Analyzing feedback is like building the foundation of a skyscraper. If it’s shaky, your decisions will crumble. So don’t rush through it. Dive deep to identify patterns that will guide your actions in the right direction. Here’s how: Aggregate feedback → pull data from all sources into one place. Spot themes → look for recurring pain points, feature requests, or frustrations. Quantify impact → how often does an issue occur? Map risks → classify issues by severity and potential business impact. — 𝗦𝘁𝗮𝗴𝗲 𝟯: 𝗔𝗰𝘁 𝗼𝗻 𝗖𝗵𝗮𝗻𝗴𝗲𝘀 Now comes the exciting part: turning insights into action. Execution here can make or break everything. Do it right, and you’ll ship features users love. Mess it up, and you’ll waste time, effort, and resources. Here’s how to execute effectively: Prioritize ruthlessly → focus on high-impact, low-effort changes first. Assign ownership → make sure every action has a responsible owner. Set validation loops → build mechanisms to test and validate changes. Stay agile → be ready to pivot if feedback reveals new priorities. — 𝗦𝘁𝗮𝗴𝗲 𝟰: 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 𝗜𝗺𝗽𝗮𝗰𝘁 What can’t be measured, can’t be improved. If your metrics don’t move, something went wrong. Either the feedback was flawed, or your solution didn’t land. Here’s how to measure: → Set KPIs for success, like user engagement, adoption rates, or risk reduction. → Track metrics post-launch to catch issues early. → Iterate quickly and keep on improving on feedback. — In a nutshell... It creates a cycle that drives growth and reduces risk: → Collect feedback strategically. → Analyze it deeply for actionable insights. → Act on it with precision. → Measure its impact and iterate. — P.S. How do you collect and implement feedback?

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]

Managing your evidence collection work like a messaging queue 📨 Engineering teams are pissed at you. Here's why: You've interrupted them 28 times this quarter for "quick" evidence requests. Infrastructure engineer interrupted 12 times for AWS screenshots. Result: 23 minutes per interruption × 12 interruptions = 4.6 hours of lost productivity. Their sprint velocity dropped 15% and one feature was shipped late. Meanwhile, engineering solved this exact problem in the 2000s with message queues. Here's how to apply the same pattern to evidence collection: Traditional evidence collection: → Interrupt engineer mid-deployment → "Quick question about backup configs" → Engineer loses context, makes mistake → 23 minutes to get back into flow state → Repeat 47 times per quarter Message queue approach: 🔄 Batch Producer Pattern Bundle all evidence requests into quarterly sprints, not random interruptions 📊 Priority-Based Processing Critical controls: Automated monthly pulls (zero interruption) Standard controls: Quarterly batch (predictable timing) Documentation: Annual cycles (planned capacity) ⚡ Async Processing with SLAs Engineers complete requests during planned downtime, not your deadline panic Implementation guidance (works in any ticketing system) Quarterly Evidence Sprint: Week 1: Release complete evidence backlog (not piecemeal requests) Week 2-3: Teams process during their maintenance windows Week 4: You batch-process all evidence submissions Result: 4.6 hours context switching → 45 minutes focused work Request Queue Format: "EVIDENCE-2024-Q4-AWS-001 What: CloudTrail logs for October 1-31 Why: CC6.1 logical access monitoring Format: JSON export, last 30 days Due: December 15 (not "ASAP") Priority: P2 (standard quarterly)" Message Queue Benefits: ✅ Throughput: 300% faster evidence collection ✅ Quality: Fewer errors when teams aren't rushed ✅ Relationships: Engineering stops avoiding your Slack messages Stop interrupt-driven evidence collection. Start queue-driven collaboration. #GRCEngineering

As 2025 winds down, I've been thinking about what's ahead in the new year. Here's my take: Surveys will fundamentally transform from passive data collection tools into active customer service systems. The survey fatigue crisis isn't about volume. It's about value. Customers are sharing less direct feedback because surveys feel impersonal, extractive, and disconnected from any tangible benefit. They're asked to give time and honest opinions, then sent into a void with no follow-up, no resolution, and no sense that their feedback mattered. AI will solve this by turning surveys into genuine conversations and closing the loop in real time. Conversational, adaptive surveys powered by purpose-built generative AI will detect vague or frustrated responses and ask empathetic and personalized follow-up questions to get to the root of customer concerns, transforming what felt like interrogation into dialogue. More importantly, AI agents embedded directly in the survey experience will take appropriate action on the spot: escalating urgent issues, offering solutions, connecting customers to the right resources, expressing genuine appreciation for positive feedback and compliments, or simply acknowledging their concerns with empathy and a clear path forward. Customers will finally see the benefit of sharing feedback because they'll experience immediate value from doing so. Organizations that embrace this shift will reverse the declining direct feedback trend. When customers know their input leads to real-time resolution and genuine recognition rather than disappearing into a database, they'll engage more willingly and honestly. The compound effect is powerful: better feedback drives better understanding, which enables faster resolution, which builds trust and loyalty, which encourages more feedback. By the end of 2026, the organizations winning on customer experience won't be the ones sending fewer surveys. They'll be the ones that turned surveys into the first line of customer service, powered by AI that understands context, responds with empathy, and closes the loop while customers are still engaged. Qualtrics has more than 1,000 customers actively using these exact capabilities today. #BigIdeas2026

Had an insightful conversation over the weekend with a colleague about a common pitfall in CX programs: relying solely on surveys and ignoring other valuable insights. Here are some key takeaways: Ease of Implementation Surveys are easy to deploy and manage, providing quantifiable data that’s simple to analyze. This makes them an attractive option for many organizations, especially those with limited resources. Tradition and Comfort Many companies stick to surveys because it’s what they’ve always done. Changing this entrenched practice can be challenging, especially if the leadership team prefers traditional methods. Resource Constraints Surveys can be cost-effective, making them appealing for smaller organizations that may not have the budget for more sophisticated tools. Organizational Silos Feedback often gets trapped within departmental silos, preventing insights from being shared and acted upon. Lack of Ownership Without clear ownership of the feedback loop, survey results can end up being ignored. It’s crucial to have designated teams responsible for analyzing feedback and driving action. Inadequate Analytics Capabilities Many companies lack the analytical capabilities - people and tech - to turn survey data into meaningful insights. Cultural Resistance Taking action on feedback requires change, which can be met with resistance. Companies need a culture of continuous improvement to effectively address feedback. Short-Term Focus Organizations sometimes prioritize short-term gains over long-term improvements, leading to reluctance in making significant changes based on feedback. Here is where we ended in terms of actions to take: 1. Integrate Multiple Data Sources: Combine survey data with digital analytics, social listening, and customer journey mapping for a comprehensive view of the customer experience. 2. Foster a Customer-Centric Culture: Encourage leadership commitment, employee training, and recognition programs that reward customer-centric behavior. 3. Invest in Analytics: Enhance analytics capabilities to turn data into actionable insights. 4. Close the Feedback Loop: Implement a closed-loop feedback system and communicate changes to customers. 5. Design Thinking and Customer Co-Creation: Use design thinking methodologies to deeply understand customer needs and co-create solutions. 6. Cross-Functional Collaboration: Promote collaboration across departments to discuss feedback and develop action plans. 7. Measure Impact and Iterate: Continuously measure the impact of changes and iterate to improve further. What are you doing to get out of the CX-as-a-survey (CXaaS) trap? #customerexperience #cx #surveys #analytics #designthinking #customercentric

If you work in UX research, you know that your insights are only as good as the sample you collect. Perfect random samples are rare in our field, but that doesn’t mean you have to settle for low-quality data. The real challenge is balancing speed and cost with validity, and there are practical ways to do it. The first step is understanding your sampling options. In an ideal world, you would run a simple random sample where every user has an equal chance of being picked. If you have a clean customer database or panel, you can randomize IDs and draw participants this way, but it’s costly and rare in UX. A more accessible variation is systematic sampling: sort your list randomly and invite every 10th or 20th user. It works if the list is truly random, but beware of hidden patterns like chronological ordering that can skew results. For teams that need reliable subgroup comparisons - say you want both iOS and Android users represented - stratified sampling is a better fit. Divide your population into meaningful segments, get the actual proportion for each, and sample within those groups. And when you’re dealing with a geographically dispersed or very large audience, cluster or multistage sampling helps reduce cost by selecting groups like cities first, then sampling users within them, though you need a larger sample to maintain precision. Most UX teams can’t do pure probability sampling, so they rely on non-probability methods. These include convenience samples of whoever responds, quota sampling where you fill set targets like a 50/50 device split, snowball recruiting through referrals for niche users, and in-product intercepts that capture feedback right in context. They’re fast and cost-effective but come with high bias risks. The good news is you can make these work better: use simple quotas to make sure you hear from new and power users, recruit through more than one channel so you don’t only reach forum regulars, trigger intercepts in ways that don’t miss those who drop off, and always document who you didn’t reach, like churned users. For large-scale or high-stakes projects, a hybrid approach combines the best of both worlds. You might recruit 500 people from a random sample and add 1,500 from an opt-in panel, then use propensity modeling and weighting to align the opt-in group to the random group. This balances cost and statistical validity. Weighting in general is a powerful tool to align your sample to known population benchmarks like census data or internal analytics. Post-stratification weights on key cells such as age by gender, and raking iteratively aligns marginal distributions when you don’t have full cross-cell data. Weighting adds variance, so it’s important to calculate your effective sample size for proper margins of error rather than assuming your raw n reflects precision.

The market research industry is being torn down & rebuilt. No one is talking about it loudly. 92% of organizations are flying blind, with garbage data. Here's the no-BS explanation: Survey fatigue has reached critical mass: • Response rates dropping to 5-15% • 81% of consumers ready to unsubscribe • Declining quality as participants rush through The say-do paradox is more real than ever: • 83% of organizations face this problem • Customers post-rationalize their decisions • Social desirability bias = polished lies Traditional survey-based methodologies are increasingly failing to capture authentic customer insights, and it sucks because millions of dollars are wasted. The organizations succeeding in this environment are those that acknowledge the limitations of traditional methods and actively experiment with hybrid approaches. What's working: Ethnographic research (8.5 effectiveness) Behavioral analytics (8.1 effectiveness) Real-time feedback systems (7.2 effectiveness) Passive data collection (7.8 effectiveness) Heatseeker combines these into one platform. Our market experiments get to participants in their environment, passively collecting data based on what they do in real time. The future of market research lies not in abandoning surveys entirely, but in using them as one component of a broader, more sophisticated approach to understanding customer behavior. The conversation across Reddit, LinkedIn, and industry forums is clear: the time for relying solely on what customers say they want is over. The future belongs to understanding what they actually do. What's your experience with survey fatigue? Are you seeing response rates drop in your organization?

Your feedback process should act as a funnel, catching data from all the various sources and bringing it into a centralized location. As you get feedback from various sources, it’s helpful to be consistent in what you collect. Capturing data in a handful of key areas is particularly useful, including: >Touchpoint. What was the touchpoint, or where was the customer in their journey? For example, this could be after a repair, or an interaction with customer service. >Objective. What was the customer’s objective? For example, they wanted to get their cable working again. >Experience. What was the actual experience? The cable got repaired but it happened outside the promised window of time. >Emotional impact. What was the emotional impact of this experience? The range you establish could be very satisfied to very unsatisfied, on a scale. I’ve seen alternatives such as very happy to very frustrated. What words best capture emotion in your setting? These factors give you a solid foundation for comparing both structured and unstructured feedback. UL, a global company that provides product testing and certification, made a push to more completely capture the on-the-fly feedback their employees were hearing. They created a simple feedback form inside their CRM system. The link can be accessed quickly by any employee, anytime. For example, they can easily pull up the form from their phone and enter the customer’s feedback. Nate Brown, who spearheaded the effort, said at the time, “This is a complete game-changer in how UL understands customers.” Find more examples here: https://lnkd.in/e-t5Zs2b #customerfeedback #customerexperience #customerservice