Creating User Personas in Design

Five years ago, Warburg Pincus LLC invested in BetterCloud and urged us to work on a project to narrow our ideal customer profile (ICP). It's the most impactful thing I've ever done to improve conversion rates, shorten sales cycles, increase deal size and ultimately transform the company. A big mistake many CEOs make is believing their product is for everyone. It’s tempting. More potential customers should mean more sales, right? But in reality, chasing too broad a market drains resources, distracts your team, muddles messaging, confuses your product roadmap, and kills go-to-market efficiency. Being laser-focused on your ICP drives alignment across product, messaging, and the go-to-market motion. When the right prospect engages, they’ll feel like you built it just for them. Anyone who has built a product or service knows that the things a small business needs are very different than what a huge enterprise needs. A company is different from a school. An IT buyer is different from a security buyer, a sales buyer is different from a marketing buyer, a director level decision maker is different than a C level decision maker… but we still believe we can sell to different segments and personas as the same time. The process to define and use your ICP is relatively straightforward but does take time. The larger your business, the more data you have, the more resources you have to crunch that data the more time you should spend to do it as scientifically as possible. The high level steps are: 1. Build a Customer Dataset: Gather all your customer data. Current and churned customers, won and lost opportunities. Enrich it with firmographic, business-specific, and buyer demographic data. 2. Engage Your Team: Your best sales and customer success people hold invaluable insights about your most successful (and worst) customers. 3. Analyze & Identify Pockets of Gold: Identify common attributes of high-performing accounts and avoid the traps of poor-fit customers. 4. Communicate the ICP to the entire company with the “why” behind the attributes that make up an ideal customer.  5. Rework your messaging to appeal to your newly defined ICP and narrow your growth initiatives to be focused only on the accounts that matter.  6. Assign the right ICP accounts to your reps and ensure they’re focused on the right buyer personas. 7. Product Development: Reassess your roadmap to align with the needs of your ICP. You should see impact fast. GTM funnel metrics will improve. Conversion rates should rise, with better leads turning into stronger opportunities. You may not get more leads, but their quality will increase. I’ve been discussing this with many Not Another CEO Podcast guests, so don’t just take my word for it. I wrote a deep dive on how to “Narrow Your ICP and Transform your Company”, with real examples from other companies. You can read the full article here https://lnkd.in/e5EN3XSR

Data alone can often feel impersonal and hard to relate to but professionals have found an interesting way around it - at least in the consulting world. I found it interesting that Bain & Company tackles this by using "customer journey mapping" - an approach that transforms data into vivid narratives about relatable customer personas. The process starts by creating detailed personas that represent key customer groups. For example, when working on the UK rail network, Bain created the persona of "Sarah" - a suburban working mom whose struggles with delays making her miss her daughter's events felt all too real. With personas established as protagonists, Bain meticulously maps their end-to-end journeys, breaking it down into a narrative arc highlighting every interaction and pain point. Using techniques like visual storyboards and real customer anecdotes elevates this beyond just experience mapping into visceral storytelling. The impact is clear - one study found a 35% boost in stakeholder buy-in when Bain packaged its conclusions as customer journey stories versus dry analysis. By making customers the heroes and positioning themselves as guides resolving their conflicts, Bain taps into the power of storytelling to inspire change. Whether mapping personal experiences or bringing data to life, leading firms realize stories engage people and shape beliefs far more than just reciting facts and figures. Narratives make even complex ideas resonate at a human level in ways numbers alone cannot.

The best customer personas don’t stay static. They need to evolve as your customers do. One of the most valuable practices we’ve adopted at RingCentral is regularly reviewing and refining our personas to keep up with what our customers truly need. It’s helped us deliver solutions that feel more relevant and make a bigger impact. Here are a few practices we’ve adopted as part of this process that have worked well for us: 🔸 Set clear triggers for reviews. Don’t wait for an annual check-in. Plan to revisit personas after specific moments, such as launching a new product, entering a new market, or seeing a shift in customer feedback. 🔸 Look beyond surveys. Tools like RingCentral’s Customer Journey Analytics and AI Interaction Analytics help spot where customers get stuck, ask for help, or drop off. These patterns can reveal what your personas might be missing. 🔸 Listen to your frontline teams. Teams that speak with customers every day, like customer success or sales, can share stories that highlight gaps or changes you might not see otherwise. When your personas reflect the real people you’re serving right now, you’re better positioned to earn trust, solve problems, and grow. #CustomerExperience #CustomerJourney #CustomerSuccess

Don’t get me wrong, campaigns flop sometimes. But the ones that never hit, again and again? That’s a signal. And then they argue back: “I’ve defined my ICP…” You're not wrong, but they're based on vanity personas built from assumptions, job titles, or outdated data. The results are campaigns that underperform, and budgets that disappear without results. Here’s how to do it right: 1. The buyer’s real behavior, not their title Most ICPs list job titles, seniority, and company size. That’s it. Reality: Two VPs of Marketing at two similar companies behave completely differently. One responds to thought-leadership content, the other to competitor benchmarking. The difference? Behavior, not title. Your ICP must capture how they act, not just what their LinkedIn profile says. 2. Focus on micro-decisions, not just big ones Every ICP has tiny, often invisible decisions that determine whether they buy: Who makes the decision internally? Who reads emails but never replies? What small objections derail momentum early? Ignoring these makes messaging “look right” but fail to convert. 3. Emotional triggers outweigh rational ones People think ICPs are all about ROI, features, and KPIs. That’s only half the picture. Ask: What keeps them awake at night about this problem? What fears, frustrations, or aspirations drive action? How do they perceive risk and reward emotionally? 4. Validate with real data Don’t assume. Observe: CRM activity and conversion patterns Demo requests and feedback Support questions Social engagement The truth about your ICP lives in what your buyers actually do, not what your decks or assumptions say. 5. Make your ICP actionable Every campaign, message, and piece of content must map to your ICP: Does it reflect their behavior and triggers? Does it consider their micro-decisions? Will it resonate on an emotional and rational level? If it doesn’t, the problem isn’t your copy, it’s your ICP. Defining your ICP is not a checkbox. It’s the foundation of every marketing decision. Miss the details, and your campaigns, no matter how polished, will fail.

Assumptions can lead to costly mistakes. My first SaaS company didn't go anywhere. I thought I was so smart, but going after the wrong people + building for wrong people cost me everything. Lots of hours put in, very little output. Most marketers think they know who their ideal customer is, and they think they know them. But until you validate your assumptions, you’re operating on guesswork—and guesswork is expensive. When I launched the first version of Wynter, I targeted copywriters... after all, who cares more about copy than them? Turns out most of them did not want any messaging validation work ("I don't like people judging my work" lol) + they didn't have any money. There was no pain they felt on their end regarding messaging validation. The pain was all in-house, felt by people hiring the copywriters: will this work? What does my ICP really care about? How can we make this copy stronger? My ICP research work had been lackluster, and I paid the stupid tax (six months of wasted efforts). A strong ICP (ideal customer profile) is built on real insights—validated, actionable, and directly tied to your audience’s needs. Avoid my mistakes and continuously refine your ICP: 1. Interview your customers: Talk to recent buyers or lost deals. Learn why they chose—or didn’t choose—your solution. Focus on the specific triggers that drove their decision and the language they use to describe their needs. 2. Survey your target market: Use target market surveys to dig into pain points, priorities, and decision-making processes. If you're in B2B, Wynter will deliver responses in 48 hrs. 3. Analyze sales conversations: Dive into sales call transcripts using tools like Gong or Chorus. Spot patterns in objections, common themes, and recurring questions your prospects raise. 4. Test your messaging: Use tools like Wynter to test key website pages with a vetted audience that matches your ICP. 5. Study competitor positioning: Analyze competitors’ messaging to uncover what they emphasize and where you can stand out. For example, if their messaging focuses on efficiency, can you carve a niche around customer experience and support? 6. Audit internal data: Review internal resources—support tickets, chat logs, and retention data. Who uses you the most, who gets the most value out of you? 7. Create iterative feedback loops: Insights aren’t static. Use tools like Wynter and Gong regularly get a pulse on your ICPs changing needs and perceptions. Building a strong ICP isn’t about guessing; it’s about listening—through tools, conversations, and data. The payoff? Better targeting, clearer messaging, and avoid paying the stupid tax.

Survey data often ends up as static reports, but it doesn’t have to stop there. With the right tools, those responses can help us predict what users will do next and what changes will matter most. In recent years, predictive modeling has become one of the most exciting ways to extend the value of UX surveys. Whether you’re forecasting churn, identifying what actually drives your NPS score, or segmenting users into meaningful groups, these methods offer new levels of clarity. One technique I keep coming back to is key driver analysis using machine learning. Traditional regression models often struggle when survey variables are correlated. But newer approaches like Shapley value analysis are much better at estimating how each factor contributes to an outcome. It works by simulating all possible combinations of inputs, helping surface drivers that might be masked in a linear model. For example, instead of wondering whether UI clarity or response time matters more, you can get a clear ranked breakdown - and that turns into a sharper product roadmap. Another area that’s taken off is modeling behavior from survey feedback. You might train a model to predict churn based on dissatisfaction scores, or forecast which feature requests are likely to lead to higher engagement. Even a simple decision tree or logistic regression can identify risk signals early. This kind of modeling lets us treat feedback as a live input to product strategy rather than just a postmortem. Segmentation is another win. Using clustering algorithms like k-means or hierarchical clustering, we can go beyond generic personas and find real behavioral patterns - like users who rate the product moderately but are deeply engaged, or those who are new and struggling. These insights help teams build more tailored experiences. And the most exciting part for me is combining surveys with product analytics. When you pair someone’s satisfaction score with their actual usage behavior, the insights become much more powerful. It tells us when a complaint is just noise and when it’s a warning sign. And it can guide which users to reach out to before they walk away.

As founders, we're bombarded with advice: "Know your customer!" "Listen to your audience!" But amidst the buzzwords, a crucial question lingers: how do we truly understand what matters to our customers, beyond the surface-level preferences and fleeting opinions? My journey as a founder has been a constant dance between chasing "customer feedback" and uncovering the deeper desires fueling that feedback. I've learned that listening isn't enough; we need to actively decode and prioritize what truly resonates with our users. Enter the Customer Value Compass: Step 1: Chart the Terrain: 1. Gather diverse data: Collect feedback through surveys, interviews, user observations, social media sentiment analysis, and support tickets. 2. Identify recurring themes: Analyze the data for common threads, challenges, and desires expressed by your customers. Don't get bogged down in individual details; look for patterns. 3. Categorize by impact: Segment your identified themes into two categories: "surface-level preferences" and "core value drivers." Surface-level preferences: These are fleeting opinions, often influenced by trends or personal experiences. They can provide valuable insights for specific features or campaigns, but shouldn't define your core offering. Core value drivers: These are deeply held needs, desires, and motivations that underpin customer behavior. These are the true north stars you need to align with. Step 2: Calibrate the Compass: 1. Dig deeper into core value drivers: Conduct in-depth interviews, focus groups, or user testing to truly understand the "why" behind these themes. 2. Prioritize based on impact: Not all core value drivers hold equal weight. Assess their prevalence, intensity, and alignment with your business goals to determine which ones deserve the most attention. 3. Validate with data: Look for quantitative evidence to support your qualitative findings. Analyze usage data, conversion rates, and customer satisfaction metrics to ensure your understanding aligns with actual behavior. Step 3: Navigate with Confidence: 1. Align your product and strategy: Use your Customer Value Compass to inform product development, marketing messages, and customer support initiatives. 2. Communicate with clarity: When making changes or introducing new features, explain how they address the core value drivers you've identified. 3. Continuously iterate: The Customer Value Compass is a living document. Gather new data, conduct regular reviews, and be prepared to adjust your understanding as your customer base and market evolve. Remember, the Customer Value Compass is not a destination, but a journey. By prioritizing what truly matters to your users, you build a foundation for sustainable growth, loyalty, and success. So, silence the buzzwords, listen deeply, and let your customers guide your voyage. #FoundersJourney #CustomerInsights #DecodingValue #ValueCompass #CustomerCentricity #BuildingForUsers

Most persona documents are fan fiction about your customers. Somebody ran a workshop three years ago, a survey went out, and now there's a slide that says Sarah, 34, values authenticity and work-life balance. Nothing on that slide tells you what Sarah reads before she buys, which pages she visited before converting, or why she picked the premium plan after telling your survey that price was her biggest concern. That gap between what people say and what people do shows up everywhere. People misremember, they stay polite, and half the time they can't explain why they bought. A survey claiming price is the deciding factor, sitting next to analytics showing customers consistently choosing your most expensive tier, should end the discount conversation on the spot. Hardly anyone puts those two numbers side by side. Demographics make it worse. Age, job title, and city don't write a single line of copy. Knowing your buyer is a 35-year-old homeowner gives you nothing until you learn she researches contractors for weeks and won't trust a quote without reading reviews first. The behavior is the insight, and the birthday is trivia. Real research starts with one decision you need to make, written as a single sentence, like which channel gets next quarter's budget or which angle leads the campaign. That sentence filters everything else. Behavior comes first, because analytics don't flatter you. After that you talk to people, treating every answer as directional. Then you read reviews, forums, and communities, since unprompted complaints are more honest than anything people say to your face. You act where the sources agree. And then you re-run it, because your audience changes channels, language, and problems while the persona deck sits untouched in the drive.

Most UX teams have been there: standing in front of a wall of sticky notes, surrounded by user quotes and caffeine, trying to decide if “Goal Oriented Greg” and “Curious Carla” are genuinely different people or just the same imaginary user with better handwriting. Persona discovery sessions like this often feel productive, the colors, the discussions, the post-its forming patterns, but deep down, we know something is off... The process is usually more art than science, more consensus building than discovery. It produces personas that sound nice in presentations but rarely hold up when real users start behaving unpredictably. Good news?! There is a more rigorous way to approach this, one that turns persona creation from a creative exercise into an analytical process grounded in evidence. Instead of guessing who your users are, you can identify them empirically by examining their real behaviors, motivations, and characteristics across your datasets. This is where clustering analysis becomes invaluable, allowing your data to uncover the story of your users on its own. Clustering uses statistical algorithms to uncover patterns and similarities across multiple dimensions of user data, revealing natural groups that exist beneath the surface. These are not personas invented in a meeting; they are personas discovered in the data. Here is how it works in practice. You begin by gathering rich, multidimensional data, including behavioral metrics. After cleaning and preparing your data, you apply a clustering algorithm such as K Means, Hierarchical Clustering, or Gaussian Mixture Models. These methods analyze the combined patterns across all features and group users who are statistically similar into clusters. Each cluster represents a group of people who share distinctive traits, perhaps they are highly efficient but disengaged, or slower but deeply curious. From there, you interpret and label these clusters in human terms. The data gives you the structure, and your UX insight gives it meaning. You might visualize the results, examine which variables most differentiate each group, and build out personas that reflect the real diversity within your audience. These personas are no longer fictional composites; they are data backed archetypes that show how meaningful subgroups actually behave, think, and feel. The benefits are substantial. Clustering eliminates much of the bias that comes from relying on small samples or internal intuition. It exposes hidden user types that might never emerge from interviews alone, such as a quiet but influential group of users whose needs are consistently overlooked. It also creates alignment across teams because the evidence is transparent and reproducible. When you present personas derived from clustering, you can trace every insight back to data, not opinion. #PersonaDiscovery #UXResearch #DataDrivenDesign #CustomerSegmentation #ProductStrategy #UserExperience #QuantitativeUX

I spent years getting personas completely wrong. Here's what I finally learned about the 3 levels of customer understanding: Level 1: Traditional Personas You know these: "Marketing Mary, 32, enjoys artisanal coffee and weekend yoga with her cat." I used to make these because everyone else did. But they're pure fiction and hurt more than help. They give us false confidence while leading our marketing and messaging astray. Level 2: Quantified Personas  This is where I graduated to next. These are built on real customer data—actual buying triggers, feature preferences, and willingness-to-pay metrics. They are much better than fiction, but they still average data across entire segments. This is good for strategy but less ideal for messaging. Level 3: Representative Customer Profiles This is what I've started using to guide messaging projects. Instead of averaging data across segments, we select one real customer who perfectly represents each segment. Like an expanded case study with rich, specific details about their challenges, buying journey, and success metrics. Here's the key insight: Use quantified personas for strategic decisions, but write your messaging for representative customer profiles. Why? Because great messaging needs to resonate deeply with individuals, not averages. When you write for a real person instead of a fusion of data points, your message becomes sharper, clearer, and more compelling. Stop creating messaging for fictional characters. Stop creating messaging for averages. Start creating messaging for real people.