How to Make Research Efficient and Impactful

🔬 Week 2: Frugal Innovation in Research—Doing More with Less (Without Lowering Ambition) What if our biggest research breakthroughs aren’t being held back by funding but by the way we think about funding? Universities have long equated quality and impact with cost. The bigger the grant, the bigger the impact, right? Not necessarily. Some of the most powerful research is: 🔹 Interdisciplinary—but underfunded 🔹 Mission-driven—but administratively blocked 🔹 Locally rooted—but globally relevant Frugal innovation is a mindset. It’s about unlocking value through creativity, constraint, and collaboration. Here’s what that can look like in practice: 1️⃣ Leverage existing assets Research groups often sit on underused data, infrastructure, or relationships. With smart coordination across faculties, with partners, and through shared platforms, we can unlock capacity without extra spend. 2️⃣ Modular, micro-funding models Instead of full-project grants, seed small, fast experiments. Let researchers test bold ideas quickly. Think R&D sprints, internal MVP grants, or "research hackathons" to accelerate proof-of-concept. 3️⃣ Rethink who contributes to research Undergraduate researchers, professional staff, community partners, and alumni can all be part of the research ecosystem. Programmes like VIPs (Vertically Integrated Projects) embed students in long-term research teams, growing capacity, not cost. 4️⃣ Entrepreneurial partnerships Industry collaboration isn’t just about large-scale commercialisation. Co-designed research, shared PhD supervision, and cooperative education placements can bring in new funding streams, especially when we make engagement easy and fast. 5️⃣ Tools like AI can change the game Literature reviews, ethics applications, and funding proposals can be accelerated with AI to redirect time to where it matters: thinking, collaboration, and impact design. The goal shouldn't be to do more with less. Instead, we need to aim to do better with what we have, and frugal innovation can help us shift from a mindset of "resource scarcity" to "resourcefulness." Next week: The Unseen Friction in Research Systems and How to Fix It #RethinkingResearch

After mentoring over 800 UX researchers over the past decade, I’ve noticed one clear pattern: The best researchers don’t just gather data, they drive action. They have habits that help them uncover insights, inspire teams, and de-risk decisions. Here are 8 of the most effective habits I’ve seen (and how you can start practicing them): 1. Comfort in ambiguity ↳ Great researchers don’t rush to conclusions. ↳ They embrace the grey areas and let insights emerge. Next time you’re synthesizing data, resist the urge to clean it up. Explore contradictions, which often lead to breakthroughs. 2. Ask the unasked questions ↳ They challenge assumptions and dig deeper. ↳ When everyone’s aligned, they ask, “What if we’re missing something?” Start every project with this question: “What don’t we know that could derail us?” 3. Endlessly curious ↳ Great researchers don’t just ask why, they ask what if? ↳ Curiosity fuels their creativity and problem-solving. Pick one unexpected user behavior from your data this week and explore why it’s happening. 4. Know when to stop ↳ They understand that more data doesn’t always mean better decisions. ↳ They recognize when diminishing returns set in and shift from research to action. Before starting a new study, ask, “What decision are we trying to inform?” If you already have enough data, stop and act. 5. Playful with insights ↳ They treat insights like puzzles, not checkboxes. ↳ The best researchers experiment with how findings are framed and presented. In your next synthesis, frame one insight in three different ways to spark new perspectives. 6. Thrive in collaboration ↳ Effective researchers know insights gain power when shared. ↳ They work closely with designers, PMs, and engineers to co-create solutions. Bring stakeholders's needs into research studies directly, help them make tough decisions and mitigate risk, watch how buy-in skyrockets. 7. Bring discomfort ↳ They don’t settle for validating assumptions—they challenge them. ↳ Insights that spark discomfort often lead to the biggest breakthroughs. If your findings aren’t sparking hard conversations, dig deeper. Research that challenges assumptions often drives transformation. 8. Unafraid to be ignored ↳ Effective researchers understand that not every insight will lead to action—and that’s okay. ↳ They focus on building a culture of evidence-based decision-making over time. Track the outcomes of your research. Revisit findings at the right moment, like a project pivot, a problem resurfaces, or priorities shift. Timing can turn a no into a valuable yes. What habits have made you a better researcher? Share them in the comments // Sick of begging people to listen to your research only to be met with a thumbs up emoji? I share strategies to deliver UXR impact on my Substack: https://lnkd.in/eR5M2geZ

Top enterprise brands are fundamentally transforming their research workflows by strategically implementing AI automation. Not to replace researchers, but to amplify their impact. Every hour your research team spends on mundane tasks is an hour they're not uncovering the insights that could double your conversion rate. The math is simple yet brutal when you're running a successful digital product: wasted time equals wasted revenue. Think about where your team currently spends most of their hours: ↳ transcribing interviews ↳ organizing data ↳ creating visualizations ↳ writing standardized reports These are precisely the tasks AI tools excel at handling... with stunning efficiency and accuracy. Instead, your researchers should redirect their expertise to what truly matters: extracting meaningful insights that directly impact customer experience and conversion rates. The brands that master this approach aren't just saving time, they're gaining a significant competitive advantage through deeper customer understanding. When your research team spends less time organizing data and more time interpreting it, they uncover the friction points that, when resolved, can boost conversion rates dramatically. The question isn't whether AI will transform UX research workflow... It's whether you'll be among the leaders capitalizing on this shift or playing catch-up later.

I’ve worked at quite a few companies, and the same thing happens again and again in UX research: A researcher works hard on a study. They write a massive report filled with insights. They send it out… and nothing happens. It doesn’t matter how brilliant the findings are—no one is reading a 180-page document. And if no one reads it, nothing changes. So instead of writing reports that get ignored, I run synthesis workshops. How it works: Instead of just delivering research, you bring stakeholders into the synthesis process. Designers, product managers, and customer journey experts work together with the researcher to: 1. Review key data—the researcher pre-selects and preps the most important findings. 2. Identify patterns and themes—using affinity mapping or similar methods. 3. Recognize issues & opportunities—what needs to change, and where are the gaps? 4. Map out impact—for users, business goals, and design. 5. Prioritize & brainstorm solutions—to define design recommendations By the end of the session, everyone owns the findings. The insights aren’t just the researcher’s anymore—they belong to the whole team. Why this works: • Stakeholders engage with the research instead of just receiving a PDF. • Insights get used because everyone is part of defining the next steps. • It’s faster than writing a giant report and drives real change. Instead of a report that gathers dust, you walk away with shared understanding, buy-in, and actionable recommendations. If your research isn't leading to impact, try bringing people into the process instead of just handing them the results.

The job of a PhD student is solving a set of difficult problems over several years. But here’s the part we often skip—validating the problem itself. In academia, we tend to validate our solutions, not our problems. Many projects start with: “Here’s what the literature says.” But real impact begins with a different question: “Is this a real problem worth spending years on?” There’s a quote often attributed to Einstein that captures the idea: “If I had an hour to solve a problem, I’d spend 55 minutes understanding the problem and 5 minutes on the solution.” To ensure that a research problem is real, relevant, valuable, and future-aligned before investing time in designing solutions, I ask PhD students to use a Problem Validation Framework comprised of the following stages: 1.      Reality Check – Is this a real problem? 2.      Worthiness Check – Is it worth investigating? 3.      Solvability Gate – Is this problem solvable? 4.      Startup Scan – Are innovators trying to solve it? 5.      Future Alignment – Does this address a future-oriented need? If any Fail, then we must refine, narrow, or redefine the problem before proceeding. Then I encourage the student to combine Stages 1–5 into a one-page Research Value Proposition (Why I should be spending several years working on this problem?). If PhD students validate the problem first, everything that follows—methods, data, solutions—becomes sharper, more relevant, and more impactful. What do you think? How does this framework align with the way you approach research problems?

On Adding Clarity to Research (Or: The Most Underrated Path to Publication) Too often, we overthink our responses to questions about our research—whether from reviewers, colleagues, or even our own students. Most of the time, the real answer is simple: add clarity. Clarity means removing unnecessary complexity, making your logic visible, and guiding your reader so they can follow your thinking without friction. It doesn’t mean “dumbing down” your work—it means making your contribution unmistakable. How to Add Clarity to Your Research: Simplify your language. Replace jargon with plain terms where possible. If you must use technical language, define it the first time. Structure your arguments. Use signposts to walk the reader through your logic: what you’re asking, why it matters, and how your method and findings address it. Use tables and figures effectively. Visuals aren’t decoration—they’re tools for making relationships, comparisons, and key takeaways obvious. Every table and figure should answer a question or make a point more memorable. Be explicit about analytical choices. Tell the reader why you selected certain models, variables, or robustness checks. Even if they don’t agree, they’ll respect a transparent rationale. Highlight your contributions early and often. Don’t make the reader hunt for your value-add. State it clearly in the abstract, reiterate it in the introduction, and make sure it’s obvious in the discussion. When you can add clarity—through thoughtful language, structured arguments, effective visuals, and transparent decision-making—you’ll not only smooth your path to publication, you’ll also make your work more accessible and impactful for a broader audience. Best of luck! #AcademicLife #Publication #Research #WritingTips #Clarity

Make evidence-based decisions faster. Here’s how you can speed up literature review. -------------- To drive successful medical strategy, you need to access the latest research and clinical data. But, traditional literature & clinical trial reviews, are often the most time-consuming part of that process, requiring hours of manual filtering, reading abstracts, and struggling with keyword-based searches that don’t go deep enough. The problem with current literature review process: ❌ Manual and time-consuming ❌ Keyword searches aren’t enough to surface detailed insights ❌ Can’t easily filter for specific criteria like patient populations, biomarkers, study design, P/S outcomes, IE criteria, or drug classes You're stuck reading through countless abstracts, trying to determine if each study fits your needs. It’s inefficient, frustrating, and ultimately a roadblock to making faster, evidence-based decisions. Enter large language models (GenAI). These advanced tools transform the way medical affairs teams can approach literature reviews, making the process faster, smarter, and more targeted. With Gen AI & AI-powered tools, you can: ✅ Connect in your literature ✅ Create specific filtering statements for the exact criteria you need—whether it's filtering by drug class, patient population, or biomarker ✅ Boil down to the most relevant studies without needing to read through endless abstracts ✅ Get high-level summaries before diving deep into the details AND, when done right, still maintain a high level of accuracy, quality, and comprehensiveness that ChatGPT alone can not do. When literature reviews take too long, they act as a blocker, preventing you from focusing on the rest of your evidence-based work, like analyzing study results, planning IITs, or engaging with key opinion leaders (KOLs). A more efficient, streamlined literature review process that eliminates bottlenecks, giving you quicker access to clinical evidence and allowing you to focus on what really matters: delivering impact and driving patient outcomes.

Strong research starts with clear, focused objectives. This visual guide walks you through the 12 best practices for developing effective research objectives—from clarity and relevance to scope and adaptability. Follow these steps to create goals that drive successful, measurable outcomes. Step-by-Step Explanation: 1. Prioritize Objectives Focus on the most important goals first. This ensures your research stays organized and aligned with your main purpose. 2. Ensure Relevance Your objectives should connect directly to the research problem. Irrelevant goals waste time and dilute your findings. 3. Consider the Target Audience Tailor your objectives so they’re understandable and valuable to the intended readers, such as scholars, policymakers, or practitioners. 4. Review and Refine Regularly revisit your objectives to improve clarity, precision, and alignment with your evolving research plan. 5. Be Open to Adaptation Stay flexible. Research may uncover new directions that require adjusting your objectives to better reflect reality. 6. Document Your Objectives Clearly write down your objectives. This provides structure, guides your methodology, and keeps your project focused. 7. Be Specific and Clear Avoid vague language. Well-defined objectives prevent misinterpretation and enhance the clarity of your research direction. 8. Align with Research Questions or Hypotheses Objectives should directly support your core research questions or hypotheses, ensuring your efforts contribute to answering them. 9. Use Action Verbs Start objectives with verbs like “analyze,” “evaluate,” or “compare.” This creates a sense of purpose and makes goals measurable. 10. Focus on Measurable Outcomes Design objectives that produce tangible results—what will be measured or observed? This is essential for validity. 11. Be Realistic and Feasible Set achievable goals within your timeframe, resources, and scope. Overambitious objectives can derail your research. 12. Consider the Scope of the Study Don’t overextend. Your objectives should reflect what is realistically possible within the limits of your study design and resources.

One of the major problems with research today how success is measured. Success is 𝘪𝘮𝘱𝘭𝘪𝘤𝘪𝘵𝘭𝘺 measured by: • Did you 𝘥𝘦𝘭𝘪𝘷𝘦𝘳 a particular study? • Are you 𝘧𝘶𝘭𝘧𝘪𝘭𝘭𝘪𝘯𝘨 research request? • Are you 𝘤𝘰𝘯𝘥𝘶𝘤𝘵𝘪𝘯𝘨 primary research? None of those are explicitly linked to business outcomes. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁: 💩 Research is stuck in this tactical delivery model where we are focused on churning shit out—regardless of level of impact—rather than having a focus on having a strategic impact 💩 • When a measure becomes a target, it ceases to be a good measure. — Goodhart's Law Research teams are focused on delivering the 𝘸𝘳𝘰𝘯𝘨 𝘵𝘩𝘪𝘯𝘨𝘴. We have this invisible script in our heads that is telling us we must be right 𝙖𝙡𝙡 𝙩𝙝𝙚 𝙩𝙞𝙢𝙚. We have this invisible script in our heads that says the 𝙖𝙢𝙤𝙪𝙣𝙩 𝙤𝙛 𝙧𝙚𝙨𝙚𝙖𝙧𝙘𝙝 we conduct is correlated to the value we deliver to stakeholders. 🚨 Spoiler alert: We don't have to be right, and the amount of research is the wrong measure. Take Major League Baseball as an example... A world class hitter bats a .300 (30% of the time they hit the ball and get on base) ⚾️ That means 70% of the time, they don't. Now I am not suggesting research aim for a .300. 🔴 But I am suggesting we stop focusing on churning out research reports... 🟢 And start churning out research impact. 𝗛𝗼𝘄? We need to build Strategic Insight and Foresight Teams (SIFT). Functional teams that pull together UX research, CX research, market research, product analytics, marketing analytics, and more... 📚 Turn that data into stories 📝 Those stories into strategies 🚀 And turn strategies into outcomes This means doing fewer things better... ...and getting on base more. SIFT Teams are independent, centralized, functions that help the entire business make more informed—and less-risky—business decisions. 𝗔𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗹𝗲 𝘁𝗼 𝗮𝗹𝗹; 𝗯𝗲𝗵𝗼𝗹𝗱𝗲𝗻 𝘁𝗼 𝗻𝗼𝗻𝗲. Let's stop measure output; and start measuring outcomes. ~fin~ ===== If you found this post helpful 👇 ❤️ Like it 🛟 Save it 🤝🏻 Follow me for more 🔥 💌 Subscribe (link on carousel) 📺 Check out my Youtube channel ❓ Ask me a question (link on top of profile) 𝘕𝘉: 𝘞𝘩𝘦𝘵𝘩𝘦𝘳 𝘤𝘰𝘮𝘮𝘦𝘯𝘵𝘴 𝘢𝘳𝘦 𝘰𝘯 𝘰𝘳 𝘰𝘧𝘧, 𝘐 𝘥𝘰𝘯’𝘵 𝘥𝘦𝘣𝘢𝘵𝘦 𝘰𝘯 𝘴𝘰𝘤𝘪𝘢𝘭 𝘮𝘦𝘥𝘪𝘢 𝘱𝘰𝘴𝘵𝘴 𝘢𝘯𝘺𝘮𝘰𝘳𝘦. 𝘐𝘵 𝘥𝘰𝘦𝘴𝘯’𝘵 𝘴𝘦𝘳𝘷𝘦 𝘢𝘯𝘺 𝘱𝘶𝘳𝘱𝘰𝘴𝘦 𝘰𝘵𝘩𝘦𝘳 𝘵𝘩𝘢𝘯 𝘵𝘩𝘦 𝘧𝘦𝘦𝘥 𝘵𝘩𝘦 𝘢𝘭𝘨𝘰𝘳𝘪𝘵𝘩𝘮. 𝘐𝘧 𝘺𝘰𝘶 𝘨𝘦𝘯𝘶𝘪𝘯𝘦𝘭𝘺 𝘸𝘢𝘯𝘵 𝘵𝘰 𝘥𝘪𝘴𝘤𝘶𝘴𝘴 𝘰𝘳 𝘥𝘦𝘣𝘢𝘵𝘦, 𝘶𝘴𝘦 𝘵𝘩𝘦 𝘭𝘪𝘯𝘬 𝘰𝘯 𝘮𝘺 𝘱𝘳𝘰𝘧𝘪𝘭𝘦 𝘵𝘰 𝘳𝘦𝘢𝘤𝘩 𝘰𝘶𝘵. 𝘛𝘳𝘰𝘭𝘭-𝘭𝘪𝘬𝘦 𝘤𝘰𝘮𝘮𝘦𝘯𝘵𝘴 𝘸𝘪𝘭𝘭 𝘣𝘦 𝘥𝘦𝘭𝘦𝘵𝘦𝘥.

I sit at exec dinners with researchers all the time and ask them 2 questions:   "What are you working on?" They get excited and tell me about fascinating studies, interesting methodologies.   Then I ask: "What does your CEO care about right now?"   Silence. Or worse, they reference goals completely unrelated to the research they just described. That disconnect is why even good research doesn't drive impact.   What we discovered at Maze is that the gap is because most researchers fail to build empathy inward. As a result, research isn't aligned with what the stakeholders actually care about.   If you want to make sure your org gets the most value out of your research, here are 3 ways you can keep them happy (while owning the process):   1️⃣ Make stakeholders a key part of the research process   When stakeholders participated in the research discussions, they were 2x more likely to use findings to influence decision-making every month.   → Make your findings accessible with visual elements accompanied by user quotes → Open up the floor to feedback by sharing rough notes and asking, “What stands out?” or “Does this match what you’re hearing?”   2️⃣ Become a powerful storyteller   → Tell a story that starts with a few intriguing findings and builds to a crescendo: highlighting key opportunities, threats, and next steps → Make sure reports have an easily readable and understandable section at the beginning that itemizes the results and benefits of implementing the findings → Flex the format for different audiences: executive presentations might focus on high-level insights and business implications, while technical teams might require more detailed implementation considerations   3️⃣ Share insights before it's too late   CEOs make important decisions weekly, not monthly, so you need to make sure you're sharing insights right when they need them:   → Get quick reaction polls on prototypes → Pull 48-hour concept evaluations → Explore first-click tests for interaction failures   If you want to make sure your team gets 100% of the value from your research studies, start here.