User Feedback Integration in Testing

User Acceptance Testing (UAT) is where the real users put the system to the test — and that’s when bugs often pop up like uninvited guests. 🎯 So how should a Business Analyst react? Here’s a practical, real-world approach👇 🔹 𝟏. 𝐒𝐭𝐚𝐲 𝐂𝐚𝐥𝐦, 𝐍𝐨𝐭 𝐃𝐞𝐟𝐞𝐧𝐬𝐢𝐯𝐞 Example: During UAT for a loan origination platform, a tester flagged that loan application forms were crashing on submit. 💡Instead of blaming dev or users, BA should listen carefully, replicate the issue, and documented the exact steps. 🔹 𝟐. 𝐋𝐨𝐠 𝐈𝐭 𝐂𝐥𝐞𝐚𝐫𝐥𝐲 𝐢𝐧 𝐭𝐡𝐞 𝐃𝐞𝐟𝐞𝐜𝐭 𝐓𝐫𝐚𝐜𝐤𝐢𝐧𝐠 𝐓𝐨𝐨𝐥 Use tools like JIRA or Azure DevOps. 💡Include: ✅ Clear description ✅ Steps to reproduce ✅ Screenshots/video ✅ Environment ✅ Severity and priority 🎯Tip: Categorize whether it’s a functional defect, UI issue, or data mapping error — devs love clarity! 🔹 𝟑. 𝐓𝐫𝐚𝐜𝐞 𝐈𝐭 𝐁𝐚𝐜𝐤 𝐭𝐨 𝐑𝐞𝐪𝐮𝐢𝐫𝐞𝐦𝐞𝐧𝐭𝐬 Was it a missed requirement, misunderstood user story, or a change that wasn’t captured? 💡Let's say, a “Export Report” button isn’t working. It turned out that the requirement wasn’t documented properly. BA should update the user story and collaborated with the Product Owner to include it in the next sprint. 🔹 𝟒. 𝐏𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐳𝐞 & 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐞 Not all bugs are blockers. 📍BA can work with the QA Lead and Product Owner to determine which bugs were critical for go-live and which could go into post-launch patching. Clear communication = smoother releases. 🔹 𝟓. 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐞 𝐅𝐢𝐱𝐞𝐬 𝐚𝐧𝐝 𝐂𝐥𝐨𝐬𝐞 𝐭𝐡𝐞 𝐋𝐨𝐨𝐩 Once the dev team resolves the bug, the BA ensures it meets the business need — not just technically fixed. 💡BA must always retest or sit with the UAT tester to verify resolution and update stakeholders. ✅ 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲 𝐟𝐨𝐫 𝐁𝐀𝐬: Your job during UAT isn’t just to observe — it’s to bridge users and tech when bugs appear, ensuring issues are documented, fixed, and business value is preserved. Let’s normalize the fact that bugs are not failures — they are feedback. Handle them like a pro. 🧠💬 BA Helpline

So many product teams work on new features they believe will be a game-changer for users. But how do you really know if a feature will be adopted by users? This is where UX research comes in. As UX researchers, we can help identify the probability of feature adoption by digging deep into user needs, behaviors, and expectations. Here are some ways we measure and predict feature adoption: 1. User Interviews and Surveys: By speaking directly to users, we can gauge their interest in a new feature. Through surveys or interviews, we explore how they might use the feature, what problems it would solve for them, and how it fits into their current workflows. These qualitative insights give us an early understanding of potential adoption barriers. 2. Usability Testing: A feature may seem like a great idea on paper, but how do users actually interact with it? Conducting usability tests on prototypes allows us to see whether users understand the feature, how intuitive it is, and where they might get stuck. If the feature feels cumbersome, adoption rates will likely be lower. 3. Task Success Rate: This metric allows us to measure how easily users can complete tasks using the new feature. A low success rate indicates friction, and users are less likely to adopt a feature if it doesn’t make their experience easier. 4. User Journey Mapping: By mapping out the user journey, we can see where the new feature fits into the overall user experience. Does it make sense within the flow of their tasks? Are there unnecessary steps or points of confusion? A smooth, integrated feature is more likely to be adopted. 5. A/B Testing: Once a feature is live, we can run A/B tests to see if it’s driving the desired behavior. Does the feature increase engagement or task completion compared to the previous version? These quantitative insights allow us to measure real-world adoption and refine the feature based on user interactions. 6. Feature Feedback: After a feature is released, gathering feedback is key. By monitoring user comments, satisfaction scores, and support tickets, we can understand how users feel about the feature. Are they using it as intended? Are there any pain points that need addressing? As UX researchers, our role is to validate whether a feature truly meets user needs and fits within their daily tasks. We can predict adoption rates, identify potential issues early, and help product teams make informed decisions before launching a feature. How do you measure feature adoption in your research?

Testing user outcomes can reveal what users actually need. A key part of user-centered design is comparing what users want to do (needs) with what they actually experience (outcomes). When we talk about user needs, we’re often describing problems or gaps in their experience. Teams want to address these needs, but I often see them jump ahead and assume their design will automatically lead to better outcomes. Sometimes this is fine. However, it’s often where things go off track. Using intuition is part of design, but there’s a difference between imagining an ideal experience and actually testing whether it works. Here’s a simple way to think about it: USER NEED = Intention This is what users are trying to do. It reflects their goals, motivations or problems they want to solve. USER OUTCOME = Reality This is what users experience after using your product. It includes emotions, behaviors, and results. It may not directly address the user's need. Too often, teams assume that trying to create something that will help users will lead to a good outcome. But in reality: → The product might solve the wrong problem → Users may struggle to complete their task → The experience may lead to frustration or confusion If your work is mostly based on assumptions, here’s how to bring it back to the user need if you're faced with starting with outcomes the business has assigned: 1. Start with assumptions grounded in quick user research 2. Run small tests. We use Helio to collect fast feedback 3. Compare the results to the original need. Did users accomplish what they set out to do? UX metrics help you see where what users need doesn't match what they actually experience. Attitudinal metrics like satisfaction, expectations, usefulness, and engagement can point out the biggest gaps so you can focus on what matters to users. It's great to start with user needs, but the reality is that most teams begin with an idea of the outcome they want to achieve. That’s okay. As long as you keep checking in with users and adjusting based on the feedback you collect. #productdesign #uxmetrics #productdiscovery #uxresearch

The key to effective usability testing? Approaching it with a Human-Obsessed mindset. This is crucial. It determines whether your improvements are based on assumptions or real user insights. It guides how you engage with: → User needs → Common tasks → Pain points → and Preferences throughout their journey on your site. Usability testing isn’t straightforward. It requires a deep understanding of user behavior and continuous refinement. How do you start a Human-Obsessed usability testing approach? Follow these steps: 1. Set Specific Goals — Focus on areas like navigation and checkout.  — Know what you aim to improve. 2. Match Test Participants to Users — Ensure your participants reflect your actual user base.  — Diverse feedback is key. 3. Design Realistic Tasks — Reflect common user goals like finding a product or making a purchase.  — Keep it real. 4. Choose the Right Method — Decide between moderated (in-depth) and unmoderated (scalable) tests.  — Pick what suits your needs. 5. Use Effective Tools — Leverage tools like UserTesting or Lookback.  — Integrate analytics for comprehensive insights. 6. Create a True Test Environment — Mirror your live site.  — Ensure participants are focused and undistracted. 7. Pilot Testing — Run a pilot test to refine your setup and tasks.  — Adjust before full deployment. 8. Collect Qualitative and Quantitative Data — Gather user comments and behaviors.  — Measure task completion and errors. 9. Report Clearly and Take Action — Use visuals like heatmaps to present findings.  — Prioritize issues and recommend improvements. 10. Keep Testing Iteratively — Usability testing should be ongoing.  — Regularly test changes to continuously improve. Human-Obsessed usability testing is powerful. It’s how Enavi ensures exceptional user experiences. Always. Use it well. Thank us later.

Here at Innolitics, we like to spread the lessons we've learned for SaMD Developers 🤓 Over the course of serving our clients, we've developed some tips for engaging with the FDA: • Engage Early: Do not wait until regulatory submission. It is important to bring clinicians, patients, and regulatory experts in from day one. • Design for Fit, Not Just Function: Even the best model could fail if it does not fit into existing workflows, or even the already existing infra-structures. • Document Expertise Input: Regulators want proof in the submission package that stakeholder input shaped the device’s design requirements, risk management, usability testing, and labeling. • Keep the Loop Open: Maintain advisory boards and feedback loops post-launch to ensure safe evolution. Overall, the traceability of your regulatory decision is of utmost importance; if you can map each regulatory document back to a stakeholder that influenced the decision, you have created a submission that tells a coherent, trust-building story. You might ask where can you document stakeholder input in regulatory submissions? Here are where regulators expect to see multi-disciplinary influence show up: Design History File: • Show how clinical and patient feedback shaped design inputs. • Keep minutes of advisory board meetings and trace design decisions. User Needs & Design Inputs (part of DHF) • Build a Requirements Traceability Matrix (RTM) linking stakeholder input → requirement → design feature → verification test. Human Factors / Usability File • Document usability studies with intended users (clinicians, patients). • Show design changes made based on real-world feedback. Risk Management File • Capture diverse perspectives: clinical (diagnosis errors), patient (misuse), IT/security (data risks). • Show how risks flagged by different groups were mitigated. Software Development Plan • Record how regulatory and quality experts influenced coding standards, testing, and change management. • Map clinical input into verification scenarios. Clinical Evidence • Show how study design reflected clinical expert advice. • Justify patient population diversity with advisory board input. Labeling & IFU • Capture patient and clinician input on wording, clarity, and instructions. • Document regulatory-driven changes (e.g., disclaimers or limitation statements). Read more about these here: https://hubs.ly/Q03Q8jMx0 #SoftwareEngineering #MedicalDevices #ClinicalWorkflow #FDACompliance

Customer discovery via functional prototypes + PostHog is night & day better than the old school way of asking for feedback on Figma mockups. Here's why: I get to observe actual user behavior instead of asking the user to guess how they might use my product. My favorite example of why this matters comes from a Sony Walkman user study. They asked a bunch of people what they thought about a yellow walkman and they said "so sporty! not boring like the black one!". And yet, when they were given the opportunity to take a walkman home after the study, everyone picked the black one. We learned a lot more from user behavior than we did expressed preferences. Here's my setup for now observing user behavior from prototypes: 1. Create a functional prototype in your favorite prototyping tool (Bolt, Lovable, Reforge Build, Magic Patterns, Claude Code) 2. Ask the prototyping tool to integrate PostHog analytics 3. Ask the prototyping tool to instrument key user actions in PostHog Then you get all of these ways of observing actual behavior: - DAUs \ WAUs \ retention curves - I can actually see if people come back and use my prototype instead of taking their word for it - Action metrics dashboards - I can see what actions people are taking vs not - Post-usage survey - I can add a built-in pop-up survey to ask the user a question about the experience after they have engaged with the prototype - Session replays - I can see exactly where people are clicking and how they are using the product to identify usability issues - Heatmaps - I can see what part of my design is working across all sessions I'd never go back to testing with just a mockup after this.

Design validation testing and human factors Validation Human factors (HF) validation and user interface (UI) design validation are both performed at the end of a product’s development to ensure that the product is suitable for its intended use, users, and use environments, but each with a slightly different scope. Whereas HF validation testing is conducted to generate data related to whether users can interact with the product safely and effectively, UI design validation is conducted to yield evidence that the product meets users’ needs.   Noting the similarities between HF validation and user interface design validation, manufacturers might wonder how they can combine these activities. Incorporating design validation activities into human factors validation testing can be an excellent use of time and resources. This is especially true when the combined human factors and design validation test sessions are short enough that participants are not fatigued by the session’s end, enabling them to participate fully and generate robust data. Furthermore, combining these activities is useful from a recruiting perspective, noting you can recruit one set of participants for the combined session rather than recruiting one set of participants for each activity.  Consider the following best practices to ensure you are integrating user interface design validation elements into HF validation testing is a smooth and productive endeavor:  Understand the scope of HF validation and UI design validation activities. For the HF validation test, you will have participants complete use scenarios and knowledge tasks encompassing all critical tasks, and many of these activities will likely be important for user interface design validation as well. Ensure you have a clear understanding of what activities must be performed for the HF validation as well as the UI design validation such that you can determine what additional activities should be added for UI design validation. Furthermore, consider if you need to collect additional data from the HF validation test activities beyond what is traditionally planned (e.g., dominant hand used, glove size, task time).   Complete HF validation test activities before user interface design validation questions. Noting the necessary rigor in an HF validation test to avoid bias and represent a realistic interaction, you should not ask any UI design validation questions until you have completed all HF validation test activities. Completing the HF validation test activities would include completing any debrief on use scenarios and knowledge task performance, as well as any further subjective feedback questions. Furthermore, if there are additional hands-on activities required to validate the UI design elements that were not already encompassed within the HF validation test, you should also wait until completing the HF validation test activities to proceed with these UI design validation activities.  

Following user feedback is a Product Management virtue. Is there an actual way to implement it, between all the noise, bugs, and stakeholder requests? Well… Most teams claim they are customer-driven. Yet the moment you open Zendesk, App Store reviews, survey results, and Slack threads, you instantly remember why everyone quietly avoids this work. Feedback is everywhere, contradictory, emotional, duplicated, and nearly impossible to turn into decisions.  It is chaos disguised as “insights.” This is why the new Amplitude AI Feedback release caught my attention and made it all the easier to decide to partner with them on this update. It successfully connects what users say with what they actually do, in one workflow. No extra tools.  No extra tabs. You see their words, frustrations, and praise. You see their behavior. And AI transforms it into ranked themes, rising trends, top requests, and complaints. Noise turns into clarity. Opinions turn into patterns. Patterns turn into action. And because it is native inside Amplitude, it kills the biggest problem in feedback work: Fragmentation. Everything flows into analytics, session replay, and cohorts, creating a full loop from insight to fix. You can trace why an issue matters, how many users care, how it impacts behavior, and which actions you should take. Finally, a single source of truth for PMs, UX, CX, and marketing. I’m also genuinely impressed with the supported sources of feedback: App Store, Google Play, Zendesk, Intercom, Freshdesk, Salesforce Service, Gong, Trustpilot, G2, Reddit, Discord, and X. Slack arrives in Q1, and there will be more! If you ever felt overwhelmed by feedback, this is one of the first attempts I have seen that genuinely solves the operational pain, not just the reporting part. It launches… Today! Take a look: https://lnkd.in/dAJKeTez What was the most successful update you know that came from the product’s users? Let me know in the comments. #productmanagement #productmanager #userfeedback

If you're a disruptor, you know how important customer-pilots are. And with these 2 strategies you turn pilots into paid customers. 💪🏻👇🏻 For context: I learned this doing UX research for healthcare at Siemens. I’ve been sharing my secret strategies with founders, because most don’t realize that just because they land a pilot, doesn’t mean they - get meaningful feedback - turn pilots into clients How come?! If you’re innovating (especially in healthcare): Testers are BUSY ‼️ To get buy-in, you need to get creative. For those now thinking: With the backing of Siemens that’s much easier. True, it can get you the meeting, BUT, it doesn't make testers less busy, inspired to give good feedback, let alone buy from you. ✌🏼 So, how to get good feedback from busy people? (Let alone convert them into customers?!) Instead of treating testing like a technical checklist, do this👇🏻 1. Strategic prep: Show you understand their reality before first contact👩🏻💻💡 - Map out workflows + pain points - Design feedback collection around their schedule Example: We didn't ask for hour-long interviews. We asked 3 questions during existing breaks. 2. Strategic testing: Don't just test if it works technically👇🏻 - Test individual user value: Does it solve friction in users' actual workflows? - Test multi stakeholder landscape value: Does it address pain severe enough to justify changing workflows? Oh, and: Don't forget to pitch the feedback process itself! 😅 ❌ Most founders: "Can we schedule time for feedback?" ☑️ What works: "Here's how we'll collect feedback in 10' during your standup. No extra meetings." Process transparency = respect for their time = foundation for trust. Why this approach drives conversion: You built trust through execution 🤘🏻✨ When testers feel seen + you’ve got a good product most will be rooting for you and will buy from you. I've seen it happen so many times. 💪🏻 Please share your thoughts or questions below! 🧠 #founder #saas #innovation #creativity #leadership #HealthcareInnovation #HealthTech #Biotech #PatientCare #DigitalHealth #HealthcareLeadership #MedTech #ux #Technology #AI #ArtificialIntelligence #digitaltransformation

Your customers are telling you exactly how to create a better product—are you listening? I've worked with a lot of companies and used a lot of feedback systems. The ones that work the best require categorizing and prioritizing them so the company is tackling the most important issues first. Slack does this well. To ensure that user feedback directly influenced product development, Slack refined its feedback collection and integration process. They implemented tools and processes to categorize feedback effectively and route it to the appropriate teams quickly. This systematization of feedback collection and analysis enabled Slack to prioritize product updates and feature requests more efficiently, leading to faster iterations and improvements that closely aligned with user needs. How can you tag and prioritize your feedback so you are focused on the most important issues?