Design Thinking Applications

Last week, I was showing Sameer Munshi (EY's Head of Behavioral Science) an emotional trading preventor app I vibecoded in an hour. I'd vibecoded the entire A/B test on Lovable - not just two versions of the app, but the actual A/B test logic built right into it with random user assignment. Everyone who visited got randomly assigned to: • either the experimental condition (with intervention) • or control condition (without intervention). While the design of the prototype simulated that of actual trading platforms, Sameer said something that I hadn't thought of: "It's a little hard, out of context, to say 'here, buy or sell' and then expect the intervention to work." I'd built this A/B testing setup but hadn't included the most basic thing yet: you can't test *emotional* trading without inducing the *emotions* that drive it. Sameer suggested creating scenarios like showing a screen that said: "Tesla just jumped 15% after Elon tweeted about record sales. You can buy before it rises more..." Suddenly it's not just clicking buttons - it's FOMO. That sick feeling you're missing easy money. Exponentially more visceral. If you're testing any behavior change intervention: • Identify the emotional triggers that drive the behavior • Engineer those moments in your test environment so it's as close to the real world as possible • Then test your intervention Whether it's impulse purchases, doomscrolling, or overtrading - you need to recreate the psychological context first. We don't make decisions in vacuums. Our decisions are driven by interactions between multiple situational and internal (psychological, biological, demographic) factors. Try to engineer as many of them as possible while testing your intervention. P.S. if you're trying to engineer delight for your ideal buyers via your marketing (without any dark patterns or salesy tactics) 📙 Here’s how to do that in 5 behavioral science-based upgrades (free!) → https://lnkd.in/g54xD9pY

How do you and your teams synthesise and select which customer needs or pains to progress in your #product, #design, or #innovation projects? Imagine you've just completed some great customer discovery research, including observing, interviewing and being the customer. You've built some good empathy for who your customers are, what is important to them, what pains them, and what delights them. Then you unpack your findings into some form of empathy map, and you've got 100s of sticky notes everywhere. You've then started to narrow them down to the most promising and interesting observations, but this still leaves you with a sizeable collection and you want to add some rigour to your intuition on which ones to take forward first. Well, here are 3 different methods that I’ve used and iterated over the years: Number One – The Opportunity Scale This first one is the simplest and is inspired by how Alexander Osterwalder et al rank jobs, pains and gains in their book Value Proposition Design, 2014. As a team, you take your short list of observations from your empathy map and rank them from how insignificant/moderate to how important/extreme the need/pain is for the customer with the most important/extreme being prioritised to explore further first. Number two – The Opportunity Matrix A The opportunity matrix increases the rigour and confidence of your prioritizing by adding ‘strength of evidence’ as another dimension. Strength of evidence at this stage of journey can be determined by the number and type of data points. For example, if you heard from several customers that a pain point was extremely painful then you could be more confident this was worth solving than one highlighted by only one customer. Likewise, observing customers do something provides stronger evidence than customers saying they do something. Here you prioritise the most important needs with the strongest evidence first. Something to watch out for is when your team selects an observation that has strong evidence but isn’t that important of a need or pain to customers. Teams can be blinkered by numbers and end up over-investing in time wasting-opportunities. Number three – The Opportunity Matrix B The third method swaps out evidence for fulfilment of the need - how satisfied are customers with their ability to fulfil the need/solve the pain with the solutions they use today? By matching this with the importance of the need/pain we can select those observations that we understand to be the most important and unmet for our customers. You can then overlay the strength of evidence across this ranking to make your final selection even more robust. And to take it to a whole new level and really de-risk your selection you can test your prioritised observations, written as need statements, in quantitative research with customers. This is something that Antony Ulwick shares in his book Jobs To Be Done, 2016. I hope you find these methods useful. #designthinking #humancentreddesign

Research design is the foundation of any rigorous study, shaping how questions are framed, data is collected, and conclusions are drawn. This document provides a structured approach to selecting and implementing qualitative, quantitative, and mixed-methods research designs. By examining philosophical assumptions, theoretical frameworks, and methodological strategies, it equips researchers with the tools to design studies that are both methodologically sound and aligned with their research objectives. The guide also highlights key elements such as literature reviews, ethical considerations, and the formulation of research questions. Beyond technical aspects, the document explores how research design influences the validity and reliability of findings. It presents best practices for integrating theoretical perspectives, structuring data collection procedures, and analyzing results to produce meaningful insights. Whether focusing on statistical measurements, thematic interpretations, or a combination of both, the document emphasizes the importance of coherence in research planning. It also introduces innovative approaches to handling complex research problems, ensuring that studies are not only rigorous but also adaptable to real-world challenges. For academics, practitioners, and students engaged in research, this resource serves as a comprehensive guide to developing well-structured studies. It provides practical strategies for crafting research proposals, refining methodologies, and ensuring ethical rigor. By bridging theory and practice, it enables researchers to create studies that contribute valuable knowledge while maintaining high standards of credibility and transparency.

Build it and they will come? 🤔 When product teams launch a highly-requested feature, they tend to expect users to engage with it. But things don’t always work out that way. 😬 This is what Lyft discovered when they launched Women+ Connect. Despite the clear benefits and the demand for the feature, not all drivers who were eligible were opting into it. 🔍 The challenge? Simply telling users about a feature isn’t always enough to drive action. When Irrational Labs partnered with Lyft, here’s what our brilliant behavioral scientist Isabel Macdonald, PhD and team learned, working closely with Robyn Bald and Kirsten M.: 🚀 A simple shift in messaging—based on behavioral science—can massively impact feature engagement. Irrational Labs tested several behaviorally-informed messages and all outperformed the control. The winning message? “Just checking. Looks like you are not opted into Women+ Connect. Is this correct? Tap to review.” What this does: The question creates a desire for resolution and nudges the driver to take action (versus do nothing). The result? Compared to the control group, this approach got 173% more opt-ins from new drivers. 📈 So, what’s the takeaway for product teams? 👉🏼 Product success doesn’t come just from building great features. You have to frame them in ways that resonate with your users, capture their attention, and motivate them to act. 💡 Curious to see how small changes can lead to massive impact? Check out the link in the comments to learn how we helped Lyft get great engagement with a great feature. 👇🏼 #BehavioralScience #ProductManagement #UserEngagement #WomenInTech #IrrationalLabs #Lyft

As we reach the final month of the year, it’s a good time to reflect on how we’re combining Science and Design. When I started to integrate behavioral science into how products and experiences are designed, it became clear how much value a systematic, evidence-based approach can bring to organizations. One principle I emphasize with advisory clients is the importance of working from a well-formed hypothesis.. why, because testing structured hypotheses helps de-risk decisions and saves resources compared to relying purely on instinct. Behavioral theories and frameworks provide a way to identify the drivers and barriers that shape how employees and customers experience your organization. Moving beyond “behavior theater” means focusing on the actual behaviors that matter, and mapping what enables or constrains them. Creating a behavioral blueprint is one effective way to do this, it’s a visual artefact that maps the behaviors happening at each stage of an experience, alongside the behavioral drivers and barriers. These could be everything from beliefs, fears, and skills to policies and social norms. I know most organizations understand functional barriers well, but the personal and emotional ones often remain invisible.. and these are the areas where behavioral insight adds the most value. You can develop these maps in many ways, as long as they’re grounded in a solid behavioral model. If you’re looking for inspiration on how to structure these artefacts from a design perspective, these books are a great starting point: -This is Service Design Doing: Marc Stickdorn, Adam StJohn Lawrence, Markus Edgar Hormeß -The Design Thinking Playbook and Toolbox: Michael Lewrick -Mapping Experiences: Jim Kalbach -The Service Innovation Handbook: Lucy Kimbell -The Journey Mapping Playbook: Jerry Angrave -Strategic Design: Giulia Calabretta -Testing Business Ideas: Alexander Osterwalder & David Bland As this year closes, how are you developing better experiences and closing gaps?

UX research is 𝗻𝗼𝘁 losing relevance. It’s entering a golden era with the power of AI. Understanding users is still a key skill for every PM. It reduces guesswork. Reveals real problems. Strengthens decisions across the product lifecycle. Here’s a breakdown of research methods every PM should use: 🔵 𝗤𝘂𝗮𝗹𝗶𝘁𝗮𝘁𝗶𝘃𝗲 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 → Interviews, field studies, contextual inquiry → Understand the why behind user behavior → Use it in early discovery or to explore new problems 🔵 𝗤𝘂𝗮𝗻𝘁𝗶𝘁𝗮𝘁𝗶𝘃𝗲 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 → Surveys, A/B tests, usage analytics → Track what’s happening at scale → Use it to validate direction and measure impact 🔵 𝗠𝗶𝘅𝗲𝗱 𝗠𝗲𝘁𝗵𝗼𝗱𝘀 → Combine qualitative and quantitative → Get context and confidence from both types of data → Strongest when used across the product lifecycle The best PMs don’t skip research. They use it to make every decision sharper. They combine insight and data. And they do it fast. With AI, UX research is now faster, smarter, and easier. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝗮 𝗳𝗲𝘄 𝗔𝗜 𝘁𝗼𝗼𝗹𝘀 𝘄𝗼𝗿𝘁𝗵 𝗲𝘅𝗽𝗹𝗼𝗿𝗶𝗻𝗴: ⚡ whyser.ai AI-led interviews with real users ⚡ syntheticusers.com Research with AI-generated personas ⚡ maze.co Automated unmoderated testing ⚡ sprig.com Survey and feedback analysis using AI 𝗪𝗵𝗮𝘁'𝘀 𝗻𝗲𝘅𝘁? Research is evolving. AI gives it speed. You bring the context, decisions, and direction. The best product teams don’t guess. They ask, listen, and learn. Then build. 📌 𝗪𝗮𝗻𝘁 𝗮 𝗳𝘂𝗹𝗹 𝗴𝘂𝗶𝗱𝗲? Product Map mapped the UX methods, tools, and templates 👇 https://lnkd.in/easzZpCD ♻️ Repost to help more product teams get closer to users 💙 Like if you’re already bringing UX research into your work 🛎️ Follow Matvey Bryksin for more practical tips 💬 What’s your main UX research method right now? Drop it in the comments!

I just spoke with Elijah Woolery and Aarron Walter of the Design Better podcast about the hidden forces that drive product adoption and behavior change. Here's what product managers and growth leaders need to know: 🧠💡 Humans don't act rationally, and the environment affects behavior more than attitudes, preferences, or beliefs. This isn't just theory—it's the foundation of effective product design. A few insights worth noting: 🔄 Your biggest competitor isn't who you think. It's the status quo—what users are already doing. The biggest predictor that I'll exercise today is whether I exercised yesterday. 👁️ Don't ask users what they want; watch what they do. Brazil's stock exchange thought their users needed better information about expiring bonds. The problem? People don't remember expiration dates from 10 years ago. By focusing on the behavior (reinvestment) rather than awareness, we increased bond reinvestment 5X. 🎯 For truly successful product engagement, focus on what I call "uncomfortably specific key behaviors" rather than abstract metrics like retention or engagement. At One Medical, we increased bookings by 20% not by asking people to "get care" (who thinks that way?) but by recommending a specific doctor. ✨ Your users don't come in with fixed preferences—you help create them. The Significant Objects Project sold junk shop items on eBay with compelling stories, turning $50 worth of items into $3,500. As a product leader, it's your job to help users understand value, not assume they already know it. ⏱️ Present bias is real: Chime switched from "save money on overdraft fees" (future benefit) to "get paid two days earlier" (immediate benefit)—and saw dramatically better conversion. I run Irrational Labs, a behavioral economics consultancy with Dan Ariely, where we apply these principles to help products drive meaningful behavior change. What hidden forces are affecting your product experience? Listen to the full conversation here: https://lnkd.in/efB6FD_6 #BehavioralEconomics #ProductDesign #GrowthMarketing

💡 Mapping user research techniques to levels of knowledge about users When doing user research, it's important to choose the right methods and tools to uncover valuable insights about user behavior. It's possible to identify 3 layers of user behavior, feelings, and thoughts: 1️⃣ Surface level - Say & Think This level captures what users say in conversations, interviews, or surveys and what they think about a product, feature, or experience. It reflects their stated opinions, thoughts, and intentions. Example: "I prefer simple products" or "I think this app is easy to use." Methods: Interviews, Questionnaires. These methods capture stated thoughts and opinions. However, insights may be influenced by social norms or biases. 2️⃣ Mid-level - Do & Use This level reflects what users actually do when interacting with a product or service. It emphasizes actions, usage patterns, and observed behaviors, revealing insights that may differ from what users say. Example: Users may claim they enjoy customizing app settings, but data shows they rarely change default options. Methods: Usability Testing, Observation. Observation helps to reveal gaps between what people say and what they actually do. 3️⃣ Deep level - Know, Feel and Dream This level uncovers deep motivations, emotions, desires, and aspirations that users may not be consciously aware of or may struggle to articulate. It also includes tacit knowledge—things people know intuitively but find hard to express. Example: A user might not realize that their preference for a minimalist design comes from the information overload of a current design. Methods: Probes (e.g., participatory design, diary studies). Insights collected using these methods will uncover implicit and emotional drivers influencing behavior. 📕 Practical recommendations for mapping ✅ Triangulate insights by using multiple methods. What people say (interviews/surveys) may differ from what they do (observations) and feel. That's why it's essential to interpret these results in context. For example, start with interviews to learn what users say. Follow up with usability testing to observe real behavior. Use probes for long-term or emotional insights. ✅ Align research with business goals. For product improvements, focus on usability testing to catch interaction issues. For innovation, use probes to generate new ideas from user insights. ✅ Practice iterative learning. Apply surface techniques (like surveys) early to refine assumptions and guide more in-depth research later. Use deep techniques (like probes) for strategic decisions and to foster innovation in long-term projects. 🖼️ UX Research methods by Maze #ux #uxresearch #design #productdesign #uxdesign #ui #uidesign

Most PhDs misuse methodology. Picking methods too early = PhD trap. Research onion: Philosophy → Approach → Strategy → Methods A student showed me a thick methodology chapter. Charts, interviews, software, tables. One question broke it: why these choices? —Nothing connected. —The chapter looked busy. —The logic was missing. This image is the research onion. It shows that methodology = decisions, not decorations. Each layer answers a question students rarely write down. Here is what students miss, with clear contrasts: 1. Philosophy Missed question: what view of reality guides the study? ✖️ “I used questionnaires and interviews.” ✔️ “The study adopts interpretivism because user comfort is shaped by perception, not just measurable variables.” 2. Research approach Missed question: am I testing theory or building it? ✖️ “This study is deductive and inductive.” ✔️ “A deductive approach is used to test an existing daylight performance theory.” 3. Strategy Missed question: what is the overall logic of inquiry? ✖️ “Five interviews were conducted as a survey.” ✔️ “A case study strategy examines design decisions within a single housing project.” 4. Method choice Missed question: one method or integrated methods? ✖️ “Mixed methods were used.” ✔️ “Simulation results were integrated with interviews to explain performance outcomes.” 5. Time horizon Missed question: snapshot or change over time? ✖️ “Data were collected from users.” ✔️ “A cross-sectional design captured user responses at one point in time.” 6. Techniques and procedures Missed question: how exactly is data analysed? ✖️ “SPSS was used for analysis.” ✔️ “Regression analysis tested the relationship between window size and daylight availability.” —Good research is not about more methods. —It is about fewer contradictions. —Methodology is the spine. ♻️find this useful? —like + comment + repost —🔔follow Edidiong Ukpong(PhD Architecture) for more

Wanna increase your organization's ability to learn? 👀 I’m a big fan of frameworks. Although they are an oversimplification of reality, they give us a way to start when we don’t know exactly what the first step is. Systems thinking, experience design, behavioral science, and other disciplines have come up with AMAZING ways for discovery, design, measurement, and experimentation and I chose a few of my favorites to share with you. 💜 ✨ For discovery you can use － The Iceberg Model to uncover hidden factors that shape your learning culture － Causal Loop Diagrams to map feedback loops and understand how different elements influence learning behaviors － The Fishbone Diagram to identify and analyze the potential causes of the discovered problems － The Self-Determination Theory to identify if people have the incentives to be internally motivated, and act upon the results. ✨ For strategy & design, you can use － The Impact - Effort Matrix to prioritize problems or solutions based on their impact (how much they will contribute to goals) and the effort required to solve them － Participatory Design to involve everyone directly in the design process to ensure solutions are relevant and user-centered. － Kotter’s 8-Step Change Model to create a plan for change and sustain it long-term － Appreciative Inquiry to increase awareness about the initiatives that already work well, and scale them ✨ For implementation, you can use: － Communities of Practice to build peer networks for ongoing knowledge sharing and problem-solving － Co-Development to create spaces where peers collaborate to solve real challenges, share insights, and co-create solutions through structured group discussions. － Action Learning to use real problems to drive team learning and reflection. ✨ For measurement, you can use: － Randomized Control Tests to test solutions on a small scale, measuring their impact and ensuring they deliver the desired results before scaling up. What's another one you'd add to the list? 💬 #learninganddevelopment #learningarchitecture