User Persona Development

Sustainability Archetypes 🌎 Sustainability strategies take different forms depending on priorities, industry pressures, and long-term objectives. Organizations approach sustainability in distinct ways, reflecting their motivations, expectations, and desired outcomes. Recognizing these archetypes helps clarify how sustainability is integrated into business strategies and where opportunities for improvement exist. Some organizations operate as Box Checkers, ensuring compliance with regulations and aligning with industry benchmarks. This approach minimizes risk but often lacks strategic ambition beyond meeting external expectations. Others prioritize Brand & Reputation, leveraging sustainability to enhance competitive positioning and stakeholder trust. While effective in building credibility, this approach requires consistency to avoid greenwashing risks. A different approach focuses on Immediate Return, where sustainability initiatives are assessed based on their ability to deliver measurable financial gains in the short term. While this method ensures direct ROI, it may overlook long-term value creation. Alternatively, some organizations are Impact & Purpose Focused, integrating sustainability to drive meaningful social and environmental change while strengthening stakeholder engagement. For businesses prioritizing Innovation, sustainability becomes a driver of product, service, and process advancements, unlocking new market opportunities and enhancing differentiation. This approach aligns with long-term growth but requires investment in R&D and forward-thinking leadership. Another perspective centers on Risk Reduction, where sustainability is embedded to mitigate financial, regulatory, and operational risks, ensuring long-term resilience. Understanding these archetypes provides a framework for assessing the depth and intent behind sustainability commitments. Some organizations fit neatly into one category, while others combine multiple approaches to balance compliance, reputation, innovation, and financial returns. Strategic alignment between sustainability and core business objectives determines the effectiveness of any approach. Moving beyond compliance or reputation management toward innovation and impact-driven models strengthens long-term competitiveness and resilience. Each organization must assess whether sustainability efforts are reactive or transformative. The most effective strategies go beyond short-term gains, integrating sustainability as a fundamental component of value creation and risk management. Sustainability is not a one-size-fits-all approach. Recognizing different archetypes helps refine strategies, identify gaps, and ensure sustainability becomes a long-term driver of business success. #sustainability #sustainable #business #esg #climatechange

I often felt that user research and creating personas were a guessing game and a waste of time. I was wrong. Here is how to ensure the research brings great results: It can indeed feel like a pointless exercise when you're doing research just to check a box, or when your personas end up being a slide nobody ever opens again. The truth is, only good research drives good decisions. So, why isn't it always good? 1) You interview too few people, or only those easy to reach Talking to just five people from your internal network or friends of friends rarely gives you a full picture. If you don't capture a range of motivations and use cases, you're likely building for a narrow and biased segment. 2) You ask leading questions When people sense what you want to hear, they try to be nice. This results in empty validation that hides the real frictions they face. 3) You stop at surface-level insights If the notes are a collection of generic statements like "I want it to be easy to use," you’re not learning anything actionable. Real insights come from digging into stories, context, and behavior. 4) Your findings aren't actionable Insights without a direct impact on what you're building tend to fade into the background. If you can't point to how research shaped a feature or decision, it's just noise. 𝗪𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝗶𝘁 𝗿𝗲𝗹𝗶𝗮𝗯𝗹𝗲 • Focus on behavior, not opinion: Asking people to describe what they did in a specific situation reveals more truth than asking them what they want. • Pattern recognition for the win: It’s tempting to anchor on one powerful quote, but decisions based on isolated comments are dangerous. The goal is to spot repeated patterns across interviews and use those to inform the product direction. • Co-create personas with your team: This way, they use them, not ignore them. Personas made in isolation often fail because they don’t feel real or relevant. Involving designers, engineers, and even sales in creating personas helps ensure they are grounded in actual experience and get referenced often.    𝗧𝗼𝗼𝗹𝘀 𝘁𝗵𝗮𝘁 𝗰𝗮𝗻 𝗵𝗲𝗹𝗽 • Maze makes it easy to run user tests without scheduling interviews. It’s great for testing flows, copy, and concepts with actual users at scale. • WhiteBridge.ai helps you to identify similar people or talk to completely fresh prospects. • Dovetail allows you to tag and synthesize interview data efficiently. You can quickly identify themes and build a research repository that your team can access anytime.    Remember, if you can't do it right, you shouldn't do it at all. There are other ways to make the best product bets possible. Do you trust in your user research? Sound off in the comments! #productmanagement #productmanager #userresearch P.S. To become a Product Manager who can perform good research, be sure to check out my courses on www. drbartpm. com :)

A good survey works like a therapy session. You don’t begin by asking for deep truths, you guide the person gently through context, emotion, and interpretation. When done in the right sequence, your questions help people articulate thoughts they didn’t even realize they had. Most UX surveys fall short not because users hold back, but because the design doesn’t help them get there. They capture behavior and preferences but often miss the emotional drivers, unmet expectations, and mental models behind them. In cognitive psychology, we understand that thoughts and feelings exist at different levels. Some answers come automatically, while others require reflection and reconstruction. If a survey jumps straight to asking why someone was frustrated, without first helping them recall the situation or how it felt, it skips essential cognitive steps. This often leads to vague or inconsistent data. When I design surveys, I use a layered approach grounded in models like Levels of Processing, schema activation, and emotional salience. It starts with simple, context-setting questions like “Which feature did you use most recently?” or “How often do you use this tool in a typical week?” These may seem basic, but they activate memory networks and help situate the participant in the experience. Visual prompts or brief scenarios can support this further. Once context is active, I move into emotional or evaluative questions (still gently) asking things like “How confident did you feel?” or “Was anything more difficult than expected?” These help surface emotional traces tied to memory. Using sliders or response ranges allows participants to express subtle variations in emotional intensity, which matters because emotion often turns small usability issues into lasting negative impressions. After emotional recall, we move into the interpretive layer, where users start making sense of what happened and why. I ask questions like “What did you expect to happen next?” or “Did the interface behave the way you assumed it would?” to uncover the mental models guiding their decisions. At this stage, responses become more thoughtful and reflective. While we sometimes use AI-powered sentiment analysis to identify patterns in open-ended responses, the real value comes from the survey’s structure, not the tool. Only after guiding users through context, emotion, and interpretation do we include satisfaction ratings, prioritization tasks, or broader reflections. When asked too early, these tend to produce vague answers. But after a structured cognitive journey, feedback becomes far more specific, grounded, and actionable. Adaptive paths or click-to-highlight elements often help deepen this final stage. So, if your survey results feel vague, the issue may lie in the pacing and flow of your questions. A great survey doesn’t just ask, it leads. And when done right, it can uncover insights as rich as any interview. *I’ve shared an example structure in the comment section.

The "P" in Personal Knowledge Management (PKM) is both what makes it so powerful, and also challenging to discuss. Implementation will differ by person, but there are identifiable "styles" to guide your choice of strategy... At the core, the goal of any PKM strategy is to transform the daily deluge of #information into easily accessible and usable knowledge. Specifically, this goal can be broken down into the four "C" objectives: 🔸 Capture - easily and consistently record information, ideas, and insights as they occur     🔸 Curate - organize and structure that captured knowledge to make it fully searchable and easily retrievable     🔸 Connect - identify relationships and make associations between disparate pieces of information to generate new insights     🔸 Communicate - share knowledge to collaborate, learn, and build upon each other’s understanding and insights Needless to say, there are many ways to accomplish these objectives. One strategy is not necessarily better than the other, but will depend on the way in which you conceptualize and process information. Neuroscientist Anne-Laure Le Cunff has developed the following useful archetypes: 🔸The Architect - highly focused on planning and design, their ideal strategy will be one that allows them to organize their ideas precisely and hierarchically 🔸The Gardener - the opposite of the Architect. Focus much more on bottom-up identification of relationships between ideas over time 🔸The Librarian - combines elements of both, with a primary focus on curating a knowledge base closely tied to specific projects, with an emphasis on ease of retrieval Productivity expert Thiago Forte has identified a fourth archetype: 🔸 The Student - novices at #knowledgemanagement, often actual students or those at the beginning of their career. Focus is on short-term tasks, e.g., preparing for an exam Identifying which of these archetypes best describe you is important in that different tools for implementing PKM have very different orientations and strengths, and you will want to choose a tool that is strong in the areas that align w/ how you think about and are most comfortable managing information. Another way to gauge this fit is to look at two different strategic frameworks. The PARA framework, developed by Tiago Forte, provides a well-defined, top down structure comprised of the following components: 🔸 Projects: Time-bound initiatives with specific goals 🔸 Areas: Ongoing responsibilities and roles (usually tied to current job responsibilities) 🔸 Resources: Topics of long-term interest for reference 🔸 Archives: Inactive items from other categories At the other end of the spectrum is the Zettelkasten framework, a bottom-up approach focused on creating densely linked atomic notes, where the organizational structure emerges over time. In the next post in this series, we will look at specific tools for implementing #PKM, corresponding to these different archetypes and frameworks.

𝗪𝗵𝗮𝘁 𝗶𝘀 𝘆𝗼𝘂𝗿 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗮𝗿𝗰𝗵𝗲𝘁𝘆𝗽𝗲? I've managed engineering teams for over 20 years. The most problematic engineers are the ones doing work that doesn't match who they are. Many times, I saw a builder forced into a tech lead role, and sometimes a deep specialist pressured to be a generalist. A product-minded engineer stuck on a team that only values raw output (hello AI). Meta learned this early on. Their original culture celebrated one type of engineer: the Coding Machine, in which people were ranked by the code they produced. If you cared about testing or system design, you were swimming upstream. At the same time, Stripe had the opposite problem. They optimized for Tech Leads with strong social skills. At one point, an entire group of Staff Engineers realized none of them were actually writing code. Both companies fixed this by naming what they already knew: engineers create impact in different ways. Here are the archetypes I've seen hold up: 🔹 𝗧𝗲𝗰𝗵 𝗟𝗲𝗮𝗱. It is someone who coordinates the team, carries context, and unblocks people. The most common archetype and often the first Staff role engineers grow into. 🔹 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁. Owns technical direction across systems. It usually thinks in years. Cares about how everything fits together, not just whether it works today. 🔹 𝗦𝗼𝗹𝘃𝗲𝗿. Someone who likes to deal with the hardest problems, fixes them, and moves on. Often vanishes for weeks and returns with something nobody expected (the best Boy Scout). 🔹 𝗕𝘂𝗶𝗹𝗱𝗲𝗿. When a builder takes something, it turns it into working software faster than anyone. This is someone whom some people call a 10x engineer. 🔹 𝗗𝗼𝗺𝗮𝗶𝗻 𝗘𝘅𝗽𝗲𝗿𝘁. It has a deep knowledge in one area: security, payments, ML, and infrastructure. It is a specialist who is usually needed for short periods on projects. 🔹 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗛𝘆𝗯𝗿𝗶𝗱. This is someone who thinks about what to build before how to build it. This role becomes increasingly important in today's companies (someone calls it a product-minded engineer). Most strong engineers blend two of these. And your archetype should shift over time. Mine has. I started as a Builder, moved into Architect territory, and now I operate mostly as a mix of Tech Lead and Product Hybrid as a CTO. It happened because I paid attention to which work gave me energy and which drained me. If your team is full of the same archetype, you have a blind spot. Meta had all Builders, and Stripe had all Tech Leads. Both paid for it. If you're burned out, check whether the problem is the workload or the fit. Sometimes you're not tired. You're just in the wrong archetype for whom you've become.

You don’t need $100K to define your ICP—here’s how to do it smarter At OneLogin, we spent $100K on ICP research—quantitative data analysis, stakeholder, customer, and competitor interviews. The result? A 100-page report that largely confirmed what we already knew: ✅ Mid-market segment ✅ Technology-first industries (FinTech, EduTech, Telcos) + manufacturing The real insight? Buyers wanted more value at a competitive price. Competing with Okta and Microsoft in IAM meant we had to be strategic—positioning OneLogin as the #1 Value Leader was the right move. Good news: You don’t need $100K to do this today. AI can help. 🚀 AI-powered market analysis, TAM, TRM, industry and persona research Google Gemini Pro + Deep Research → Industry analysis in minutes (I use it every time when onboarding a new client). Perplexity, ChatGPT → Persona development, interview guides, messaging validation. Next: Analyze and segment your CRM data. Once you’ve identified your target segments and personas, you need to validate them against your actual customer data. This is where most companies struggle—their CRM is a mess. ❌ No data normalization ❌ Inconsistent industry tagging ❌ Missing roles, functions, firmographics At some point, you have two choices: 1️⃣ Clean up and enrich your CRM (tools like ZoomInfo and LI Sales Navigator can help) 2️⃣ Scrap it and rebuild from scratch If your data is somewhat usable, run segmentation analysis to uncover: Churn & lost opportunity reasons New and expansion revenue by segment Segments with the highest and lowest utilization ICP mistakes companies make (and how to fix them) ❌ Don't assume your current and future ICPs are the same. ✅ Build a moat—Your best customers today may not be your best customers tomorrow. ❌ Focus only on firmographics. ✅ Look at technographics—Are prospects using complementary tech? Should you block accounts where certain tech stacks lead to lost deals? ❌ They define personas based on assumptions. ✅ Validate with real customers. Listen for the exact language buyers use to describe pain points. 🔹 Pro tip: Use ChatGPT to create an interview guide, summarize call recordings, and extract insights to refine your ICP. How do you operationalize ICP? Knowing your ICP helps marketing with broad targeting. BDRs use it for qualification. But sellers need a subset—target accounts. 🔥 Jon Miller’s FIRE framework helps prioritize accounts based on: Fit (firmographics, technographics, industry, geo) Intent (buying signals) Relationships (leverage execs, board, investors for warm intros) Engagement (activity with your brand) Final Thought: Your ICP isn’t static—it should evolve with market shifts, competitor moves, new products, and GTM strategies. If you serve multiple industries or have a multi-product offering, you need multiple ICPs. If you’re not revisiting it, you’re flying blind. 📌 More on ICP process? Check out GTM Partners’ resource—link in comments. #ICP #B2BMarketing #AI #BoldGTM

𝗘𝗺𝗽𝗹𝗼𝘆𝗲𝗲 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗕𝗲𝗹𝗹 𝗖𝘂𝗿𝘃𝗲 Every organization carries a silent curve; one not found in textbooks, but in the lived behavior of people who show up every day. Not a statistical model, but a cultural one. A mirror of how work is distributed, rewarded, ignored, or quietly endured. ⭐ 𝙏𝙝𝙚 𝙃𝙞𝙜𝙝 𝙋𝙚𝙧𝙛𝙤𝙧𝙢𝙚𝙧 — “𝙏𝙝𝙚 𝘼𝙩𝙡𝙖𝙨” Carries the workplace on their shoulders with a tired smile. Moves fast, solves problems before they become problems, and quietly absorbs the weight others drop. Their desk is a battlefield of finished tasks, empty coffee cups, and a to‑do list that never ends. Everyone depends on them. No one protects them. 🔧 𝙏𝙝𝙚 𝙐𝙣𝙥𝙤𝙡𝙞𝙨𝙝𝙚𝙙 𝙋𝙚𝙧𝙛𝙤𝙧𝙢𝙚𝙧 — “𝙏𝙝𝙚 𝙍𝙤𝙪𝙜𝙝 𝘿𝙞𝙖𝙢𝙤𝙣𝙙” A spark waiting for oxygen. They have ideas scribbled everywhere: whiteboards, notebooks, sticky notes - but no one has ever shown them how to turn sparks into fire. They’re eager, curious, and full of potential, but often lost in the maze of unclear expectations. 🕰️ 𝙏𝙝𝙚 𝙌𝙪𝙞𝙚𝙩 𝙌𝙪𝙞𝙩𝙩𝙚𝙧 — “𝙏𝙝𝙚 𝙂𝙝𝙤𝙨𝙩 𝙀𝙢𝙥𝙡𝙤𝙮𝙚𝙚” Present but not here. They glide through the day with minimal ripples; no noise, no conflict, no extra effort. Their energy is rationed, their boundaries firm, their soul already halfway out the door. They survive the system by disappearing inside it. ☕ 𝙏𝙝𝙚 𝙁𝙧𝙚𝙚𝙡𝙤𝙖𝙙𝙚𝙧 — “𝙏𝙝𝙚 𝘾𝙤𝙧𝙥𝙤𝙧𝙖𝙩𝙚 𝙋𝙚𝙖𝙘𝙤𝙘𝙠” All feathers, no flight. They strut around with buzzwords, charm, and perfectly timed coffee runs disguised as “alignment meetings.” They know everyone, deliver nothing, and somehow stay visible enough to avoid accountability. Their performance is performance. 🧨 𝙏𝙝𝙚 𝘿𝙮𝙨𝙛𝙪𝙣𝙘𝙩𝙞𝙤𝙣𝙖𝙡 𝘿𝙞𝙨𝙧𝙪𝙥𝙩𝙤𝙧 — “𝙏𝙝𝙚 𝙒𝙖𝙡𝙠𝙞𝙣𝙜 𝙍𝙚𝙙 𝙁𝙡𝙖𝙜” A storm cloud with legs. They enter rooms with criticism loaded, ready to fire. Every idea is “flawed,” every plan “doomed,” every initiative “pointless.” They contribute little but drain plenty: energy, morale, and momentum. These archetypes don’t appear because people are inherently good or bad. They appear because systems shape behavior. - High performers burn out when accountability is uneven. - Unpolished performers stagnate when development is optional. - Quiet quitters emerge when psychological safety collapses. - Freeloaders thrive when visibility is rewarded over value. - Dysfunctional disruptors grow when trust in leadership erodes. The bell curve is not just a performance model. It is a cultural diagnostic tool - a reflection of what an organization tolerates, enables, or ignores. And this version of the curve? This is my own reframing of the traditional performance bell curve; anchored not in statistics, but in human behavior and workplace reality. Now where do you or your team belong to?

Forget Personas. When asked to define an early adopter or an ideal customer profile, too many entrepreneurs reach for personas (or avatars). These are overkill at the early stages of a business model and can be outright harmful. While it's tempting to list a bunch of demographic and psychographic attributes, be wary that these are still guesses. The danger here is going too narrow, actually finding customers, and ending up on a small hill—a local maxima trap. For example, let’s assume I define a startup founder using the “two guys in a garage in Silicon Valley” stereotype. If I go looking, I’ll find entrepreneurs who meet these criteria, and if I don’t bother looking any further, I’ll miss the much larger global market (mountain) of entrepreneurs. The art of customer segmentation isn’t chasing after the most distinguishing traits, but the smallest number of distinguishing characteristics that cause them to buy or act. There is one distinguishing trait that all early adopters have. Can you guess what that is? A triggering event. Start your early-adopter definition/ICP with just a triggering event. Then use customer-problem discovery interviews to build an evidence-based archetype - one causal attribute at a time.

I often see someone expressing curiosity about #BigData or survey results like this: "how does that break down by gender?", or "does this skew toward low income?" I believe these are inevitably biased questions. The issue is that these #demographics are being used to assign cognitive processes. A "low income" person is "worried about paying rent." Or a "woman" will be "taking a social or empathetic perspective." Neither of these cognitive assignments is true for everyone of that demographic. Often these demographics are too high-level to influence a person's interior cognition, even contextually. For example, "spanish-speakers" are a huge population with as much variety as the whole population of any country. But I have seen product teams in the US associate "spanish-speakers" with "migrant" and "low-income." And then teams go create solutions with broad assumptions and not enough details to truly provide a variety of valuable support to people in their variety of contexts & thinking styles. Here's one way to do better in our thinking about strategy and product & service design: 👉 Start with much more nuanced #contexts to explore, like "person with diagnosed early stage pancreatic cancer, who can access good care, and wants to" or "person taking unpaid short-term care of an adult who is related to them." 👉 The next step is to understand the variety of thinking styles within these nuanced contexts, by adding #QualitativeResearch to your knowledge-creation process. Qual + Quant 👉 Of course I recommend listening sessions about what cognition and emotion went through people's minds in those nuanced contexts. It is true there are versions of qualitative data that do not lend much understanding. A researcher will know the difference. 👉 A thinking style is a person's core cognitive/emotional #approach to their early stage pancreatic cancer or to taking short-term care of their adult relative. And this core approach can change! 👉 Then ask, "how can we support each thinking style?" and "do we want to support all of them?" 👉 As a way of discussing the variety within your org, you can make up #characters that represent the thinking styles. Try making up two characters that represent the same thinking style. Explore this well, because it affects your strategy. 👉 Note that thinking styles are never construed as negative, nor as a personality. "The Grumbler" is not a thinking style. "Worried I will be committed to more than I had planned" is a thinking style. In the case that your org chooses not to support a particular thinking style: 👉 Skipping a thinking style will be part of your strategy. It's an important sign of maturity within an org to formally recognize this as your strategy and define why. 👉 You might include here a point at which the org will eventually turn toward supporting this thinking style. 🌱 ⏤ 📩 Sign up to my newsletter: indiyoung . substack . com