Insight Generation Methods

Uncovering the Real Problems: A Tech Leader's Guide In the labyrinth of IT challenges, we often find ourselves chasing shadows. 93% of IT project failures stem from solving the wrong problem. It's a sobering statistic that demands reflection. As technology leaders, our true value lies not in firefighting, but in prevention. Here are five methods to show the way: 𝟭. 𝗧𝗵𝗲 𝗦𝗼𝗰𝗿𝗮𝘁𝗶𝗰 𝗜𝗻𝗾𝘂𝗶𝗿𝘆 - Ask probing questions. - Seek understanding, not just answers. - The "5 Whys" technique can reveal surprising truths. 𝟮. 𝗧𝗵𝗲 𝗘𝗺𝗽𝗮𝘁𝗵𝘆 𝗘𝘅𝗽𝗲𝗱𝗶𝘁𝗶𝗼𝗻 - Step into your users' world. - Observe, listen, feel. - True solutions emerge from genuine understanding. 𝟯. 𝗧𝗵𝗲 𝗗𝗮𝘁𝗮 𝗟𝗲𝗻𝘀 - Let numbers tell the story. - Patterns hide in plain sight. - 40% of IT time is spent treating symptoms. Don't be part of that statistic. 𝟰. 𝗧𝗵𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗦𝗶𝗺𝘂𝗹𝗮𝘁𝗼𝗿 - Test theories in safe space. - Create a mock environment, experiment freely. - Break stuff (on purpose). 𝟱. 𝗧𝗵𝗲 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗟𝗼𝗼𝗽 - Deploy, measure, learn, improve. - Repeat. - Progress is a journey, not a destination. These methods aren't just tools; they're mindsets. They transform reactive problem-solving into proactive leadership. Companies prioritizing root cause analysis see a 35% higher project success rate. It's not just about efficiency—it's about impact. The challenge: Choose one method. Apply it this week. What hidden truth did you uncover? How did it shift your perspective? Share your insights. Let's learn from each other's journeys. After all, in the world of technology, the most powerful upgrades often happen between our ears.

Let's face it: most user interviews are a waste of time and resources. Teams conduct hours of interviews yet still build features nobody uses. Stakeholders sit through research readouts but continue to make decisions based on their gut instincts. Researchers themselves often struggle to extract actionable insights from their conversation transcripts. Here's why traditional user interviews so often fail to deliver value: 1. They're built on a faulty premise The conventional interview assumes users can accurately report their own behaviors, preferences, and needs. People are notoriously bad at understanding their own decision-making processes and predicting their future actions. 2. They collect opinions, not evidence "What do you think about this feature?" "Would you use this?" "How important is this to you?" These standard interview questions generate opinions, not evidence. Opinions (even from your target users) are not reliable predictors of actual behavior. 3. They're plagued by cognitive biases From social desirability bias to overweighting recent experiences to confirmation bias, interviews are a minefield of cognitive distortions. 4. They're often conducted too late Many teams turn to user interviews after the core product decisions have already been made. They become performative exercises to validate existing plans rather than tools for genuine discovery. 5. They're frequently disconnected from business metrics Even when interviews yield interesting insights, they often fail to connect directly to the metrics that drive business decisions, making it easy for stakeholders to dismiss the findings. 👉 Here's how to transform them from opinion-collection exercises into powerful insight generators: 1. Focus on behaviors, not preferences Instead of asking what users want, focus on what they actually do. Have users demonstrate their current workflows, complete tasks while thinking aloud, and walk through their existing solutions. 2. Use concrete artifacts and scenarios Abstract questions yield abstract answers. Ground your interviews in specific artifacts. Have users react to tangible options rather than imagining hypothetical features. 3. Triangulate across methods Pair qualitative insights with behavioral data, & other sources of evidence. When you find contradictions, dig deeper to understand why users' stated preferences don't match their actual behaviors. 4. Apply framework-based synthesis Move beyond simply highlighting interesting quotes. Apply structured frameworks to your analysis. 5. Directly connect findings to decisions For each research insight, explicitly identify what product decisions it should influence and how success will be measured. This makes it much harder for stakeholders to ignore your recommendations. What's your experience with user interviews? Have you found ways to make them more effective? Or have you discovered other methods that deliver deeper user insights?

💡 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

You can't treat AI like a magic fix for everything. It's a thinking partner, not a shortcut. A lot of consultants are using AI to generate surface-level answers. That's not where the value is. The real value comes when you pair it with your human expertise. You can use it to sharpen your judgment, test your assumptions, and uncover parts you might have overlooked previously. I've been refining how I use AI in my own practice and teaching my clients to do the same. Here are a few prompts I've found effective for the core areas of consulting work: Problem Framing and Diagnosis → "Act as a senior partner. What problem is the client actually paying to solve beyond what they stated?" → "What missing information would most change the recommended decision?" Strategic Thinking → "Identify the single constraint that limits value creation the most." → "What would a first principles competitor do in this situation?" Analysis and Insight Generation → "Which inputs account for most of the outcome variation?" → "What data could create false confidence, and what data would reduce uncertainty?" Client Communication and Influence → "Rewrite this recommendation so a CEO can understand it in thirty seconds." → "What would make this insight feel obvious after it is explained?" Decision Making → "What decision offers the highest upside with limited downside?" → "What would we recommend if compensation depended on the outcome?" Execution and Impact → "What three actions should happen in the next fourteen days?" → "What activities must stop for this strategy to work?" I've put together 30 prompts across all six categories in the cheat sheet below. Try out a few with problems you're helping clients with. Save the rest for when you need a reference point. If you're thinking of building your consulting practice right now, I'm running a live session that connects these ideas to the bigger picture. On Thursday, February 5, I'll walk through how experienced professionals can turn decades of hard-earned knowledge into premium consulting fees. We'll cover: → Why companies spending billions on AI are seeking seasoned consultants for guidance. → How to make the switch from hourly rates to monthly retainers. → The "Category of One" positioning technique that makes price comparisons irrelevant. 📅 Thursday, February 5, 2026 ⏰ 1:00–2:00 PM ET Save your seat here: https://lnkd.in/gkrG4fUb 📨 If you're ready to book a call, send me a DM with the word "ready." ♻️ Repost this to help out your network. ➕ Follow Dale Gibbons to turn your genius into a 7-figure consulting business.

Your Product Managers are talking to customers. So why isn’t your product getting better? A few years ago, I was on a team where our boss had a rule: 🗣️ “Everyone must talk to at least one customer each week.” So we did. Calls were scheduled. Conversations happened. Boxes were checked. But nothing changed. No real insights. No real impact. Because talking to customers isn’t the goal. Learning the right things is. When discovery lacks purpose, it leads to wasted effort, misaligned strategy, and poor business decisions: ❌ Features get built that no one actually needs. ❌ Roadmaps get shaped by the loudest voices, not the right customers. ❌ Teams collect insights… but fail to act on them. How Do You Fix It? ✅ Talk to the Right People Not every customer insight is useful. Prioritize: -> Decision-makers AND end-users – You need both perspectives. -> Customers who represent your core market – Not just the loudest complainers. -> Direct conversations – Avoid proxy insights that create blind spots. 👉 Actionable Step: Before each interview, ask: “Is this customer representative of the next 100 we want to win?” If not, rethink who you’re talking to. ✅ Ask the Right Questions A great question challenges assumptions. A bad one reinforces them. -> Stop asking: “Would you use this?” -> Start asking: “How do you solve this today?” -> Show AI prototypes and iterate in real-time – Faster than long discovery cycles. -> If shipping something is faster than researching it—just build it. 👉 Actionable Step: Replace one of your upcoming interview questions with: “What workarounds have you created to solve this problem?” This reveals real pain points. ✅ Don’t Let Insights Die in a Doc Discovery isn’t about collecting insights. It’s about acting on them. -> Validate across multiple customers before making decisions. -> Share findings with your team—don’t keep them locked in Notion. -> Close the loop—show customers how their feedback shaped the product. 👉 Actionable Step: Every two weeks, review customer insights with your team to decipher key patterns and identify what changes should be applied. If there’s no clear action, you’re just collecting data—not driving change. Final Thought Great discovery doesn’t just inform product decisions—it shapes business strategy. Done right, it helps teams build what matters, align with real customer needs, and drive meaningful outcomes. 👉 Be honest—are your customer conversations actually making a difference? If not, what’s missing? -- 👋 I'm Ron Yang, a product leader and advisor. Follow me for insights on product leadership + strategy.

Collecting insights is easy. Measuring their impact on strategy, clinicians, and patients? That’s the real challenge. Most teams are logging notes. Tagging KITs and KIQs. Tracking MSL activity. But if you asked, “What changed as a result of those insights?” The answer is often unclear. Here’s how we’re changing that 👇 Step 1: Start with a focused strategy Before you ask “What did we learn?” Define what you want to learn. That means: KITs = Key Insight Themes (ex: Safety vs. Competitors) KIQs = Key Insight Questions (ex: “Are HCPs confident in Drug A’s tolerability?”) Embed these into your CRM, onboarding, and field coaching so everyone’s aligned on what signals to capture. Step 2: Define what impact actually looks like You need metrics that move beyond volume: 1/ Business impact: Did we adjust content, launch a new IIT, improve cross-functional alignment? 2/ Clinician impact: Did HCP sentiment or behavior shift? 3/ Patient impact: Are more patients being identified or diagnosed earlier? The best teams track activity → sentiment → adoption, with dashboards, briefs, and executive decks tailored by audience. Step 3: Audit your data and sources Insights don’t only live in CRM. They’re hiding in med info, IME outcomes, ad board transcripts, and congress debriefs. Map what exists. Spot the gaps. Enable tagging and structure where it's missing. Step 4: Build a coaching loop around insight quality Most insights fall flat because they’re too vague. Example: ❌ “HCP had a question about safety.” ✅ “Dr. Smith expressed concern over neutropenia in older patients and asked for real-world data.” Start peer reviews. Create a simple rubric. Reward specificity and strategic alignment. Step 5: Operationalize it Make insights part of your team’s muscle memory. Dashboards that show KIT trends, sentiment shifts, and top themes Monthly briefs to highlight what's changing Quarterly reports that tie insights to business and clinical outcomes Every insight should be traceable to: → What was heard → What action was taken → What impact it had Bottom line: Insights aren’t valuable because they’re collected. They’re valuable when they drive action. And when that action changes strategy, improves HCP confidence, or accelerates patient care? That’s when insights stop being noise and start becoming leverage. If you want access to a more detailed version of this playbook with more examples and a plan for how to operationalize insights within your team, comment below or DM me!

I've seen too many “analytics teams” who treat their job like a Q&A help desk—“Send me your business questions and I’ll pull some data.” Sounds good on paper, right? But here’s the blunt truth: Data questions ≠ Business insights. When you’re asked: “How much traffic came from each channel?” “What was the conversion rate for mobile vs. desktop?” …you're really being asked to run a report. And guess what? Reports are easy. Insight is hard. The Mistake: We assume our business partners have laser-focused, outcome-driven questions. In reality, they know their area inside and out and are motivated to make decisions—but they might not know the right questions to ask. Instead, they ask for data because it’s tangible. The Opportunity: Instead of just answering their “data questions,” dig deeper. Spend time understanding their business goals and the obstacles holding them back. Ask them: “What outcomes are you trying to achieve?” “What’s stopping you from hitting that target?” “What ideas do you have for overcoming these challenges?” When you translate vague “questions” into concrete business problems, your data work transforms. Suddenly, you’re not just a report generator—you’re a trusted advisor guiding impactful decisions. A Simple Shift: Stop treating requests as a checklist of reports. Start with conversations about goals, obstacles, and outcomes. Then, co-create metrics and hypotheses that truly matter. When you do that, you move from chasing numbers to driving decisions. Let’s challenge ourselves: Next time you get a “question,” ask, “What’s the underlying business problem here?” You might just uncover a goldmine of insight.

We are experimenting the new reasoning techniques from DeepSeek in IT Operations troubleshooting and found interesting methods it takes (the Aha! Moments ✨ is a genius idea): 1️⃣ Iterative Self-Questioning (Peeling the Layers) Core idea: Keep asking "Why?", "What else?", and "What if?" to refine understanding. Example: "Why is the connection failing?" → Possible cause: TCP issue on port 443 "But why specifically?" → Could be DNS, firewall, or SSL "What if it’s not just one issue but a combination?" 2️⃣ Perspective Shifting ("Wait, but…") Core idea: Step back and reconsider assumptions. Challenge initial bias. Example: "Wait, but what if the issue isn't on our side?" "Wait, but how would this look from a network engineer's perspective instead of a developer’s?" 3️⃣ Empathetic Thinking (User-Centric Debugging) Core idea: Consider what the user (or another team) needs to understand and do. Example: "The user might not know how to verify DNS or check firewall rules—should I give them clear steps?" "Would a junior engineer understand this explanation?" 4️⃣ Epiphany-Driven Reasoning ("Aha! Moments") Core idea: Sudden insights emerge when fragmented clues into a coherent pattern, often triggered by subconscious processing or unexpected connections. Example: "Aha! The script is firing before the images are injected into the DOM!" #deepseek

Stop guessing what your customers want. Start stealing insights directly from your competitors' customers. Here’s my 5-step method to extract gold from public reviews (with prompts included): Step 1: Auto-collect reviews across platforms Prompt: Deep Research mode “Collect up to 100 English-language reviews for [Competitor Product/Service] from platforms like Amazon, Reddit, Google, and their official website. Include both praise and complaints. Organize into a platform-based table: Positive | Negative.” Why this matters: → Captures raw, unfiltered customer voice → Reveals praise + pain in one view → Forces GPT to mine multiple sources, not just one Step 2: Extract emotional pain points Prompt: “Analyze these reviews and identify 5 recurring customer pain points. Include real quotes and rate the emotional intensity (1-10).” Why this matters: → Emotional language = marketing gold → Filters out one-off rants → Prioritizes based on what customers feel most Step 3: Find the gaps no one’s solving Prompt: “Create a matrix showing unmet needs across all competitors. Highlight the most glaring market gaps.” Why this matters: → Exposes blind spots in the market → Compares multiple players → Spots real whitespace, not just noise Step 4: Validate before you build Prompt: “Generate 5 survey questions to test these unmet needs with my audience.” Why this matters: → Cheap way to de-risk ideas → Keeps research laser-focused → Helps you build what people actually want Step 5: Rank by ROI potential Prompt: “For each opportunity, estimate: revenue impact, dev complexity, time to market, and competitive advantage duration. Then rank them.” Why this matters: → Turns insights into action → Balances speed + strategy → Helps you make smart, fast moves Want help customizing this for your product or market? Drop what you're working on in the comments.