How to Gather User Feedback for B2B Products

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?

User experience surveys are often underestimated. Too many teams reduce them to a checkbox exercise - a few questions thrown in post-launch, a quick look at average scores, and then back to development. But that approach leaves immense value on the table. A UX survey is not just a feedback form; it’s a structured method for learning what users think, feel, and need at scale- a design artifact in its own right. Designing an effective UX survey starts with a deeper commitment to methodology. Every question must serve a specific purpose aligned with research and product objectives. This means writing questions with cognitive clarity and neutrality, minimizing effort while maximizing insight. Whether you’re measuring satisfaction, engagement, feature prioritization, or behavioral intent, the wording, order, and format of your questions matter. Even small design choices, like using semantic differential scales instead of Likert items, can significantly reduce bias and enhance the authenticity of user responses. When we ask users, "How satisfied are you with this feature?" we might assume we're getting a clear answer. But subtle framing, mode of delivery, and even time of day can skew responses. Research shows that midweek deployment, especially on Wednesdays and Thursdays, significantly boosts both response rate and data quality. In-app micro-surveys work best for contextual feedback after specific actions, while email campaigns are better for longer, reflective questions-if properly timed and personalized. Sampling and segmentation are not just statistical details-they’re strategy. Voluntary surveys often over-represent highly engaged users, so proactively reaching less vocal segments is crucial. Carefully designed incentive structures (that don't distort motivation) and multi-modal distribution (like combining in-product, email, and social channels) offer more balanced and complete data. Survey analysis should also go beyond averages. Tracking distributions over time, comparing segments, and integrating open-ended insights lets you uncover both patterns and outliers that drive deeper understanding. One-off surveys are helpful, but longitudinal tracking and transactional pulse surveys provide trend data that allows teams to act on real user sentiment changes over time. The richest insights emerge when we synthesize qualitative and quantitative data. An open comment field that surfaces friction points, layered with behavioral analytics and sentiment analysis, can highlight not just what users feel, but why. Done well, UX surveys are not a support function - they are core to user-centered design. They can help prioritize features, flag usability breakdowns, and measure engagement in a way that's scalable and repeatable. But this only works when we elevate surveys from a technical task to a strategic discipline.

CSMs, are we asking the right questions? 🤔 Sometimes, we stick to surface-level questions that don’t really get to the heart of what our customers need. But small tweaks can lead to big insights. Here’s how to take your customer conversations from basic to brilliant: Go from: "Are you happy with the product?" ➡️ To: "Can you share a specific example of how our product helped you achieve a recent business goal?" Asking if someone is happy only scratches the surface. The better question digs into the value they get from the product and how it ties into their success metrics. Go from: "Do you have any issues with the product?" ➡️ To: "Can you walk me through a recent challenge you faced while using the product and how you worked around it?" A yes/no question limits feedback. Asking for a specific experience helps you understand user pain points and provides actionable data. Go from: "What features do you like?" ➡️ To: "Which feature did you use most often this past week, and how did it help your team?" It’s not just about what customers like; it's about what creates the biggest impact for their team. Go from: "What are you concerned about during your next board meeting?" ➡️ To: "What key metric are you most focused on reporting to your board next quarter?" Asking this question helps you understand your customer’s priorities and where your product can help them deliver on their goals. Go from: "What metrics are you held accountable to in your specific role?" ➡️ To: "Which metric has been most challenging for you to hit, and how can our product help improve it?" This question shifts the focus to their pain points, giving you a chance to help them leverage your product to overcome obstacles. Go from: "Are there aspects of our product that you feel you are not fully utilizing yet?" ➡️ To: "Is there a feature of our product that you haven’t fully explored but think could be valuable for your team?" This specific question gets customers thinking about how to get more value from your product and where they might need help to unlock new features. --- These updates give you more than answers. They push deeper talks that lead to useful ideas and better connections. What’s one question you plan to improve in your next customer conversation?

The best PMM research doesn’t come from collecting more data. It comes from collecting data from more SOURCES...aka triangulation. Triangulation helps you improve the validity, depth, and confidence of your findings by cross-checking insights across distinct but complementary data sources. This helps reduce bias and reduce how much you need from a single data source. For instance, for most B2B personas, just 5 solid interviews will get you 80% there, if you complement it with other sources. So, how can you apply this practically? Let’s go through a real example: Research question: What key benefits should we emphasize in the messaging for our primary persona, Business Ops leads? 1️⃣ Data source 1: Qualitative (what they say) Sources (pick one or more): --> 4 customer interviews with biz ops leads --> Gong snippets from late-stage technical eval calls --> Internal CSM notes during onboarding and renewal   Common quotes include: “Every tool we add creates another integration headache.” “I just want something that doesn’t break other things.” This suggests they care less about flashy features and more about stability, reliability, and ease of maintenance. Now let’s verify this by going thru behavior data. 👇 2️⃣ Data source 2: behavioral (what they do) Sources (pick one or more): --> Support logs and ticket categories for similar accounts --> Feature usage of admin controls, integrations, and audit logs --> Help center searches by role/persona tag Insights: → Ops users are most active in integration, data sync, and permission → High NPS users rarely file tickets, but when they do, it’s for downtime or bugs, not UI complaints This confirms that reliability and ease of system management drive real behavior. 3️⃣ Data source 3: outcome ( what they choose) Sources: --> Win/loss notes --> Procurement objections tagged by role --> Post-sale NPS comments filtered by Business Ops titles Insights: → In wins: “Didn’t have to loop in Engineering” or “We were able to integrate in 1 sprint” → High NPS Ops users cite: “It just works. Rarely need to touch it.” This confirms that the decision patterns match the earlier sentiments. ✅ Triangulated insight: “Business Ops leaders prioritize system trust and low-maintenance integrations; they will choose a solution that promises stability, control, and minimal firefighting over advanced features.” In summary, triangulated findings are more defensible, easier to get buy in and more resistant to bias. You won’t always have time for deep research, especially in a startup. But even a scrappy mix of 2–3 sources can level up your insight. The good news is you can use AI to speed up the grunt work, and then YOU bring the insight. This is the type of work that helps you drive business strategy and get seen. ❓ When you build personas or messaging, what sources do you pull from? #productmarketing #research #strategy #coaching 

💬 A couple of years ago, I was helping a SaaS startup to make sense of their low retention rates. The real problem? The C-suite hesitated to allow direct conversations with users. Their reasoning was rooted in their desire to maintain strictly "white-glove-level relationships" with their high-paying clients and avoid bothering them with "unnecessary" queries. Not going deeper into the validity of their rationale, but here are some things I did instead to avoid guesswork or giving assumptive recommendations: 1️⃣ Worked with internal teams: Obvious, right? But when each team works in their silo, lots of things fall through the cracks. So I got customer success, support and sales teams in the room together. We had several group discussions and identified critical common pain points they had heard from clients. 2️⃣  Analytics deep-dive: Being a SaaS platform, the startup had extensive analytics built into their product. So we spent days analyzing usage patterns, funnels, and behavior flow charts. The data spoke louder than words in revealing where users spent most of their time and where drop-offs were most common. 3️⃣ Social media as primary feedback channels: We have also started monitoring public forums, review sites, and tracked social media mentions. We collected a lot of useful insights through this unfiltered lens into users' many frustrations and occasional delights. 4️⃣ Support tickets: This part was very tedious, but the support tickets were a goldmine of information. By classifying and analyzing the nature of user concerns, we were able to identify features that users found challenging or non-intuitive. 5️⃣  Competitive analysis: And of course, we looked at the competitors. What were users saying about them? What features or offerings were making them switch or consider alternatives? 6️⃣ Internal usability tests: While I couldn't talk to users directly, I organized usability tests internally.  By simulating user scenarios and tasks, we identified main friction points in the critical user journeys. Ideal? No. But definitely eye-opening for the entire team building the platform. 7️⃣  Listening in on sales demos: Last but not least, by attending sales demos as silent observers, we got to understand the questions potential customers asked, their concerns, and their initial reactions to the software. Nothing can replace solid, well-organized user research. But through these alternative methods, we managed to paint a more holistic picture of the end-to-end product experience without ever directly reaching out to users. And these methods not only helped in pinpointing the issues leading to low retention, but also offered actionable recommendations for improvement. → And the result? A more refined, user-centric product that saw an uptick in retention, all without ruffling a single white glove 😉 #ux #uxr #startupchallenges #userretention