UX And Agile Methodologies

How to fail in an interview Topic: User Research Role: Product Owner/ Product Management 👔 Interviewer: "As a Product Owner, how do you incorporate user research into your decision-making process?" 🧑 Candidate: "I look at feedback from surveys and reviews to decide what users want." 👔 Interviewer: "That’s a start, but let’s dive deeper. Imagine this: You’ve launched a new feature, and initial feedback seems positive. However, over time, churn increases, and users complain it’s too complex. What steps would you take to ensure such issues are avoided in the future?" 🧑 Candidate: "I’d send out another survey to figure out what went wrong and try to fix it in the next release."   🎯 What the Product Owner Should Have Answered: ✍️ Empathize with Users: "I’d ensure continuous engagement with users through interviews, usability testing, and field studies. Surveys alone often miss the 'why' behind user behavior." ✍️ Iterative Validation: "I’d validate ideas early through prototypes or beta testing with a small user group. This helps uncover usability issues before a full release." ✍️ Metrics + Insights: "I’d combine qualitative insights with behavioral data, such as feature usage, drop-off rates, or session duration, to create a complete picture of user needs." ✍️ Feedback Loop: "After launching, I’d establish an ongoing feedback loop with users and prioritize iterative improvements based on data and direct user input."   🔍 Impact of a User-Centric Approach: ✅ Reduced Risk: Catching usability issues early prevents costly rework post-launch. ✅ Increased Engagement: Features designed with real user input drive better adoption. ✅ Stakeholder Confidence: A strong feedback loop demonstrates ownership and proactive problem-solving. 💬 Key takeaway: A Product Owner’s compass is user empathy. Research isn’t a task; it’s a continuous dialogue to build what users truly need.   📌 Your thoughts? How do YOU keep user voices at the center of your product decisions? 👇 👉 Join "Agile Interview Hub" for deeper insights: Link below

I used to think user research was easy. But then I switched to B2B. And oh boy... reality hit hard Back when I was working on a B2C product, I could run 10 user interviews in a day. Users would happily spend 45 minutes answering questions and testing new designs. I thought this was just regular product design. Turns out, I was riding a perfect wave of continuous discovery without even realizing it. Then I switched to B2B. And I admit it really felt scary at first. Users were just too busy to pick up my phone calls. It took 3 weeks to schedule 5 calls. Some users left a bad CSAT score with barely any comment. Damn. How can we build anything serious without ever talking to users? At that time, it really felt like an impossible task. And any way I tried to put it, there were just no efficient process to get those users on the phone. But then it hit me. What if the best discovery touch points weren’t designers or PMs at all? What if they were already happening… in sales calls, support chats, internal Slack threads? And we had this feedback scattered across tools, threads, and people. But no one was making sense of it. So we built a Feedback Management System. We plugged every feedback into a single source of truth directly in Notion: - Intercom conversations and Modjo calls with customers - Internal tickets from sales and support to discuss user pain points or feature requests - User feedback forms submitted on the platform All filtered and organized per team through Notion automations. Each designer spends 2 hours per week turning raw feedback into structured insights. Then each team reviews it together weekly, and it feeds product decisions and the roadmap. It’s simple. It’s scalable. And it changed everything. Product designers no longer design based on shaky assumptions or partial data. They're now the source of customer truth and alignment. In B2B, discovery doesn’t happen in a lab. It happens in the wild. You just need to know where to listen. #productdesign #uxdesign #userresearch

In UX, we talk a lot about what users think, but we rarely study how their attitudes actually change over time. Most research still relies on one-time surveys like SUS, NPS, or post-test ratings. These snapshots are useful, but they tell us almost nothing about how trust grows, how frustration accumulates, or how confidence rises and collapses after a single confusing update. Attitudes are not steady states. They are trajectories shaped by experience. There are scientific ways to track those trajectories. Continuous-Time SEM lets researchers measure how satisfaction or trust evolves in real time, even if we collect feedback at irregular moments. A streaming app can trigger a question after each session and see exactly when enjoyment starts to drop, so recommendations can intervene before disengagement sets in. Latent Transition Analysis helps us understand how people move between hidden states such as novice, intermediate, competent, or stuck. Instead of guessing who needs help in onboarding, we can calculate the probability a user will progress or remain frustrated and then redesign tutorials to move them forward. Bayesian Hierarchical Models solve a common UX problem. What if we do not have huge samples like consumer apps do? With twenty or thirty enterprise users, traditional statistics break down, but Bayesian methods still model growth and decline in attitudes. They can reveal that confidence improves for new employees but decreases for experts after a redesign, a pattern that would otherwise remain invisible. Joint Modeling goes further by connecting attitude trends with real outcomes such as churn. It can show that a drop in usability or motivation predicts cancellation two weeks before users actually leave, turning measurement into prevention. One of the most powerful and practical tools is Hidden Markov Modeling. Instead of relying on surveys, it infers emotional states from behavior like hesitation, rage clicks, repeated backtracking, or abandoned tasks. It detects frustration even when people are silent, revealing emotional shifts that traditional surveys fail to capture. If you want to go deeper into these methods and see more concrete examples, I put together a full breakdown on the blog. You can read it here: https://lnkd.in/eY_Nwme2

In the past few months, I’ve reviewed 100+ of user research processes Most research isn’t failing because of lack of effort, but because it’s not designed to drive action. If your research isn’t influencing decisions, it’s just collecting dust. Here are the biggest mistakes I’ve seen and how to fix them 1. Treating research like a one-and-done project Stop running research in isolated phases and start embedding it throughout the entire product lifecycle. → Instead of conducting a single discovery study at the beginning of a project and moving on, build a lightweight rolling research program. Running bi-weekly customer interviews alongside the product roadmap can continuously feed insights into decision-making. → Rather than a one-time usability test before a big launch, implement a research checkpoint at each major development phase—discovery, prototyping, beta testing—to catch usability issues early and often. If research isn’t ongoing, insights will always arrive too late to influence decisions. 2. Focusing on methods instead of impact Stakeholders don’t care if you ran 30 interviews—they care about what changed because of them. → Instead of saying, “Users struggle with onboarding,” say, “Our research shows that simplifying the onboarding flow can reduce support tickets by 25%, saving the company $100K annually.” → Frame your findings in the context of business goals. Instead of focusing on frustration with navigation, highlight how improving navigation could lead to a 15% increase in product adoption over the next quarter. Your research should always tie back to revenue, retention, or efficiency, otherwise, it won’t be prioritized. 3. Drowning stakeholders in data Your job isn’t to dump everything you’ve learned—it’s to guide better decisions. Instead of handing over a 50-page report that no one reads, create a one-page executive summary that includes: - the problem identified - the impact on the business - 2-3 actionable recommendations - potential next steps If you’re running a usability study, instead of listing every issue found, prioritize the top three issues that, if fixed, will have the biggest impact on conversion rates. If stakeholders can’t find what matters quickly, they won’t act on it. 4. Working in silos Research isn’t a solo effort. Collaboration is key to making an impact. → Instead of presenting findings at the end of the project, run “research playback” sessions where stakeholders actively engage with raw findings helps teams internalize user challenges. → Co-creating research questions with your stakeholders ensures the insights align with their needs. Involving customer support teams in scoping research can help surface recurring pain points they hear daily. Make research a two-way conversation, not a broadcast. How do you think about and iterate on your research process? Join 10,000+ UXRs in becoming more strategic: https://lnkd.in/eR5M2geZ

𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 sounds great, but what does it mean in practice? Practical tips + 3x case studies from Depop | Carwow | Wooga Continuous Discovery is when you have an ongoing stream of user research: 💡 𝗠𝗼𝗿𝗲 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 – new research is happening all the time.  🕓 𝗥𝗲𝗱𝘂𝗰𝗲𝗱 𝗹𝗲𝗮𝗱 𝘁𝗶𝗺𝗲 – no need to wait to speak to users. 😍 𝗗𝗲𝗲𝗽𝗲𝗿 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗲𝗺𝗽𝗮𝘁𝗵𝘆 – keeps your users front of mind. This requires doing 5 things on a continuous basis: 1. 𝗥𝗲𝗰𝗿𝘂𝗶𝘁 - find users to speak to 2. 𝗦𝗰𝗿𝗲𝗲𝗻 - select who to speak to 3. 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗲 - find time to speak to them 4. 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 - ask them insightful questions 5. 𝗔𝗻𝗮𝗹𝘆𝘀𝗲 - interpret and share the answers The trick is automating recruiting, screening and scheduling. This takes a lot of time and effort otherwise. That means there's a psychological and logistical barrier to user research. 𝗥𝗲𝗰𝗿𝘂𝗶𝘁𝗶𝗻𝗴 𝗶𝗱𝗲𝗮𝘀: • Trigger an in-app pop up or email • Trigger based on user engagement or retention • Pipe through to screening survey 𝗦𝗰𝗿𝗲𝗲𝗻𝗶𝗻𝗴 𝗶𝗱𝗲𝗮𝘀: • Run in google forms / Typeform etc. • Ask for details to link back to user account • Ask a FEW questions so you know who you are talking to 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴 𝗶𝗱𝗲𝗮𝘀: • Block out regular time for user interviews. • Use Calendly or similar to agree times (can also include more screening questions) See the carousel for the case studies, or read the full article: https://lnkd.in/eGxCQKiB Visit Hustle Badger for practical advice for product leaders DM me for private workshops, coaching and advice

The challenge in continuous discovery is synchronizing the feedback loops. Yesterday, I discussed two kinds of feedback in continuous discovery – balancing and reinforcing loops. Another dimension to continuous discovery, as highlighted by Stephanie Leue, is layering the types of customer feedback across the product's vision, strategy, and outcomes. (https://lnkd.in/dQa7tKuW) A third dimension, which can become politically challenging, is integrating these feedback loops with different business functions. In our Helio services, discovery and research are design-led functions. This includes rapid feedback cycles that complete within a week and usually extend over a few weeks as we iterate on an idea. Product and marketing teams coordinate through weekly check-ins. The involvement of the engineering team varies – they participate weekly in some cases, depending on the complexity or stage of the project, but might only join in for broader, less frequent information-sharing sessions. Design and research require frequent internal feedback cycles as they refine ideas over a week. These shorter feedback cycles ensure that the best concepts are tested with an audience. It’s a magical dance when it comes to life. In a setup where feedback is gathered weekly from a large group of 200-500 users, my experience over the last few years has taught me the following: 1. Making the feedback process as transparent as possible is crucial, allowing everyone to see how information is collected. Answer as many questions from the team about the approach. 2. Actively invest in hunches. They drive the weekly discovery process and encourage the broader team to engage. Your first ones suck. Don’t give up – you get better through iteration. 3. When you have these instincts or hunches, the next step is formulating the right questions that align with the project's objectives. Teams will critique the questions to death, but asking more questions is often faster. And you get better at asking questions. 4. Timing matters. You may need to present findings on a new idea quickly within limited timeframes. Don’t sweat it – get into a rhythm. 5. Since the process involves a wealth of data and various methods, consistently presenting these ideas is essential for maintaining trust weekly. Continuous discovery takes practice to coordinate, but once you get a system up and running, your team will experience the benefits of regular, consistent user feedback! #productdesign #productdiscovery #uxresearch #marketresearch

I used Lovable to 10X my user research by vibecoding a digital twin. Here’s the entire flow you can replicate in 2 hours.. 👇 1. Every research call ends in Granola. 2. Transcript auto-fires into Zapier. 3. Zapier sends it to ChatGPT. • With a long, ruthless system prompt. • It extracts only what the interviewee said. • Zero contamination from my opinions. Pure signal. 4. That structured summary then lands in Notion: • One database for every research call • One People database with name, org, role, persona, LinkedIn • Relational links between people ↔ conversations Now I can see: How each person evolved my thinking over time. Not just isolated calls. Then comes the truly powerful part. I used Lovable to spin up a RAG chatbot. Under the hood: • Notion auto-syncs daily into the knowledge base • OpenAI for generating embeddings • Supabase for vector storage (pgvector) Result? I can “talk” to my user research. Anytime, anywhere. Across personas. Global Mobility leads. Immigration attorneys. Founders. CHROs. It’s a living digital twin of all the experts I've spoken to. We’ve already documented 100+ calls. And scaling this isn't scary anymore. I only focus on asking better questions. The system handles everything else. User research shouldn’t slow you down. It should compound. Next, I'll add more functionalities and integrate this with a Slack bot. Tell me in the comments if you've implemented any useful user research automations!

Worthy Read: Continuous interviewing with Kristian Collin Berge (CEO & Co-founder at UX Signals) by Afonso Franco "87% of product people say they have built products or features nobody wanted. Only 1 in 5 features in a typical software product are in regular use." 🎯 Learn how continuous user interviews can help you build products people actually want. Kristian Collin Berge, Co-Founder/CEO of UX Signals, shares: 🔍 Why analytics alone aren't enough to understand your users 👥 How talking to users regularly leads to 15-20% faster company growth ⚡️ The story of how Airbnb increased revenue by changing just 7 characters of text 🚀 How to streamline your user interview process: - Automate user recruitment - Build consent collection into your process - Include teammates in interviews - Summarize findings immediately after each session Real example: At Porterbuddy, implementing weekly user interviews led to a 250% increase in their main success metric 📈 Read the article: https://buff.ly/40sQjzZ ❓ When was the last time you talked to your users? Share your thoughts in the comments below.

"What should we do when user research doesn't fit into a 2-week sprint?" That's one of the most common questions I get when teaching how to integrate UX into Agile environments. It's a real challenge. Some research just takes a long time. Weeks. Months even. So, how do you square that with a team operating in 2-week cycles? Here's the key: Don't try to do the same work faster. Instead, do it differently. → Don't cram a long research study into a sprint. → Don't treat research like a separate phase. → And don't stress about being "done" in the traditional sense. Agile teams do research continuously. That research can generate value even while the study is in progress. You don't need to finish the study to deliver value. At the end of each sprint, ask: What have we learned so far? What are the early signals? What new questions are showing up? Share those. That's valuable. That's "done enough." Agile isn’t just about shipping. It’s about learning.