User Persona Development

Why Most Buyer Personas Fail (and How to Fix It) Demographics are not a strategy. Knowing someone’s job title and location is like reading the blurb on the back of a book and thinking you understand the story. It’s shallow. And it misses everything that matters. What really drives your buyers? • The bottlenecks that burn them out • The unseen politics they navigate • The hidden fears they won’t put on a webinar Q&A If your persona doesn’t map their pain, pressure, and personal stakes… You’ll write content that lands with a thud. The moment you get specific... about what keeps them stuck, overwhelmed, or secretly frustrated... you shift from broadcasting to resonating. And that’s when connection happens. The kind of connection that makes someone think... “This brand gets me.” Skip the guesswork. Go deeper. That’s where the engagement is. 👉 How do you uncover the real motivations behind your buyer personas?

We’ve all heard of audience personas. But what if you could look beyond demographics and see how a persona thinks, behaves, and buys in real time? That’s exactly what I did today using Momentro, diving into the “Coffee Lovers” persona while comparing Barista Coffee Company Limited and t-Lounge by Dilmah but instead of focusing on search or content, I went deeper into behaviour. ☕ The “Coffee Lovers” Persona in Sri Lanka. 📌 Behavioural Trends: Actively follow slow living, café culture, and minimalism creators on YouTube. Prefer review led content over ads. Blend indulgence with wellness interested in both high-end desserts and clean living. 📌 Influencer Signals: Gravitate towards authentic, often micro-influencers who feel like trusted voices. Example: I checked out Alison Wijemanne who popped up in the F&B influencer space. Momentro provided me her category strength (food, beverage & travel) her brand history, her sentiment index (largely green = safe bet for partnerships) and some of the brands she has worked with in the past too. 📌 Brand Affinities: Engage with Barista, Dilmah T-Lounge, Java Lounge (Pvt) Ltd, Peppermint Cafe, Ibsons Choice Cafe, and even Starbucks — suggesting they blend local pride with global taste. 📌 Pain Points & Opportunities of coffee lovers in Sri Lanka: Tired of copy-paste content Seek genuine café experiences and behind-the-scenes narratives Want to feel spoken to, not marketed at For Content Teams: This is a Gold Mine. Most content teams are briefed with assumptions: “Target millennials,” “Make it Gen Z-friendly,” “Do something trendy.” But with Momentro, your creative team gets the nuance: -What this persona wants to hear -What frustrates them -What formats they consume -What tone feels authentic vs performative -Build campaigns based on what this persona already consumes -Choose influencers that align with their behavioural identity -Tailor content formats (YouTube > Facebook, micro > macro) No more content roulette. You build stories rooted in reality, pain points, motivations, peer influence, and preferred channels. Suddenly, your next campaign isn’t just more relevant. It’s more wanted! #marketing #influencermarketing #personaanalysis #microinfluencers #momentro

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.

One of the most effective ways to define your brand is by mapping it to a specific person. Not just a vague demographic, but an actual persona—real or fictional—who embodies everything your company stands for. When I launched my activewear brand, Ellie, we created Amy, a 27-year-old woman from California who represented our ideal customer. Every marketing decision we made was filtered through the question: Would Amy be into this? Amy loved the outdoors, so our ads featured scenic landscapes. She wasn’t too serious, so our content was lighthearted and casual. She had big aspirations but also made time for fun. Getting specific with her persona made our messaging feel natural and authentic. And it worked. When defining Hawke Media’s persona, we landed on me because I am the customer we serve. Before launching Hawke, I built, scaled, and sold e-commerce brands. I know firsthand the pain points our clients face, from tight budgets to inefficient marketing strategies to the constant pressure to grow. That perspective shaped the way we built Hawke Media. We are not a buttoned-up, corporate agency. We take marketing seriously, but we also have fun, challenge norms, and embrace creativity. That personality attracts the right clients and the right talent because it reflects exactly who we are built to serve. Defining your brand’s persona, whether it is a fictional character, a celebrity, or even yourself, keeps your messaging sharp and consistent. It gives your company a voice, a personality, and a clear direction. Without it, your marketing risks being generic and forgettable.

Is our audience wrong or is our content wrong? 🤔 I've seen founders post daily, try new angles, get decent engagement... but the RIGHT people never show up in their DMs or sales pipeline. Views don't pay bills. The wrong audience does nothing for your business. Here's what most brands miss: 𝗜𝗳 𝘆𝗼𝘂𝗿 𝗮𝘂𝗱𝗶𝗲𝗻𝗰𝗲 𝗽𝗲𝗿𝘀𝗼𝗻𝗮 𝗶𝘀𝗻'𝘁 𝗰𝗹𝗲𝗮𝗿𝗹𝘆 𝗱𝗲𝗳𝗶𝗻𝗲𝗱, 𝘆𝗼𝘂'𝗿𝗲 𝗴𝘂𝗲𝘀𝘀𝗶𝗻𝗴 𝘄𝗵𝗼 𝘆𝗼𝘂'𝗿𝗲 𝘁𝗮𝗹𝗸𝗶𝗻𝗴 𝘁𝗼. At Click Fox Media, we start with audience persona mapping — 𝙣𝙤𝙩 𝙖𝙨𝙨𝙪𝙢𝙥𝙩𝙞𝙤𝙣𝙨 𝙤𝙧 𝙗𝙖𝙨𝙞𝙘 𝙙𝙚𝙢𝙤𝙜𝙧𝙖𝙥𝙝𝙞𝙘𝙨, 𝙗𝙪𝙩 𝙙𝙖𝙩𝙖-𝙗𝙖𝙘𝙠𝙚𝙙 𝙗𝙚𝙝𝙖𝙫𝙞𝙤𝙧𝙖𝙡 𝙞𝙣𝙨𝙞𝙜𝙝𝙩𝙨 𝙩𝙝𝙖𝙩 𝙩𝙚𝙡𝙡 𝙪𝙨 𝙬𝙝𝙤 𝙖𝙘𝙩𝙪𝙖𝙡𝙡𝙮 𝙘𝙖𝙧𝙚𝙨 𝙖𝙣𝙙 𝙬𝙝𝙮. Research shows that 90% of companies using detailed buyer personas develop a better understanding of their consumers and see higher engagement and conversions. (Delve AI) Three ways to fix this today: 𝗗𝗶𝗴 𝗶𝗻𝘁𝗼 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝗿 → Go beyond age and location. Understand their fears, triggers, and what keeps them up at night. 𝗦𝗲𝗴𝗺𝗲𝗻𝘁 𝗯𝘆 𝗿𝗲𝗮𝗹 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻𝘀 → Look at who actually engages, saves, and buys. Not who you think should care. 𝗢𝗻𝗲 𝗽𝗲𝗿𝘀𝗼𝗻𝗮 𝗽𝗲𝗿 𝗽𝗼𝘀𝘁 → Stop trying to speak to everyone. Clarity beats volume every time. When your content speaks to the right person at the right time, everything changes 💥 What's stopping your content from reaching the people who'd actually buy?

“Personas are pointless.” I used to disagree. Then I agreed. Now? "It depends." Once, I spent six weeks building a set of personas (you can see one below). Blood, sweat, and not-so-fun tears. I put everything I knew into them which, to be fair, wasn’t much back then. I couldn’t sleep the night before the big reveal. And then... ↳ "Oh yeah, we already knew that." ↳ "This isn't our exact focus anymore" ↳ Nods but no action A big old flop. So, can personas be pointless? Absolutely. - If they’re made in isolation - If they aren’t tied to real decisions - If they don’t change how people work But when they do work, it’s because they’re built for decision-making, not lamination. Here are 5 ways to make personas actually useful, based on years of trial, error, and one too many sad personas gathering dust in Google Drive: 1. Run an “Information Needs” workshop before you start Ask your PMs, designers, and devs: “What do you wish you knew about our users to make better decisions?” Document their needs → design your research to answer them → bake those answers into your persona. 2. Build proto-personas collaboratively to surface assumptions early Before you do any research, map out what people think they know. Use sticky notes color-coded by: - Assumption - Analytics - Existing research This reveals gaps, misalignment, and gives you a jumpstart on where to dig deeper during interviews and information to include in your personas. 3. Anchor personas in journey stages, not personality traits Forget personality sliders or random hobbies. Instead, map: - What users are trying to accomplish - What frustrates them at each stage - Which tools they use and why If your persona doesn’t help answer: “What would break their flow here?," rewrite it. 4. Activate personas through workshops, not PDFs Don’t “present” personas, use them. Host an ideation workshop where teams solve for a key need or pain point. Or run a mini-hackathon based on persona insights. 5. Embed personas into rituals and review them quarterly Add a persona lens to roadmap planning: “Which persona does this initiative support?” Post them in your workspace, tag bugs/features with persona names, and revisit them every quarter to update insights. So no, personas aren’t inherently pointless. But pointless personas are everywhere. Always ask yourself: “Will this persona change what we do next?” // If you're struggling to put personas together and don't know what "bad" or "good" really look like, watch this video where I share and diagnose all the problems (and good parts) of the personas I created through the years: https://lnkd.in/etMeeSS9

Are We Skipping Crucial Steps in the Sales Cycle? (Here’s how slowing down might actually accelerate your sales 🚀) In today’s world, it seems like everyone is focused on shortening the sales cycle—rushing from introduction to purchase at lightning speed. But with the pressure to "book that demo," "schedule that discovery call," or "convert visitors to buyers," it’s easy to overlook two critical stages in the buyer's journey: Awareness and Consideration. (Have we forgotten how our buyers actually navigate through these stages?) Let’s pause for a moment. What if instead of rushing, we helped buyers move through their journey in a more organized and supportive way? Here’s a breakdown, stage by stage: 1. Top of the Funnel (Awareness) Persona Identification: Identify the pain points and needs of each persona. Content Strategy: Focus on educational content—blogs, videos, and social media posts that inform and engage, rather than sell. Engagement Tactics: Use targeted ads and SEO to reach your personas where they’re actively seeking solutions. 2. Middle of the Funnel (Consideration) Persona Differentiation: Understand how each persona evaluates options. What are their criteria? What objections do they have? Content Strategy: Offer in-depth resources—whitepapers, case studies, webinars—that speak to their specific evaluation criteria. Engagement Tactics: Use email campaigns and remarketing to stay top of mind as they weigh their options. 3. Bottom of the Funnel (Conversion) Persona Focus: Now that they’re ready to decide, align your messaging with their final concerns—pricing, ROI, and implementation ease. Content Strategy: Deliver clear CTAs, personalized offers, and detailed product info. Offer live demos or trials to give them that final push. Engagement Tactics: Follow up promptly with tailored messaging that makes it easy for them to say "yes." Conclusion: Rushing prospects through the funnel can lead to missed opportunities and lost sales. But by understanding and guiding each persona through awareness, consideration, and conversion, we create a smoother, more effective buyer journey—one that naturally leads to conversions. ♻️ If you found this helpful, consider resharing to help others slow down for better results. Thank you!

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

The best PMM research doesn’t come from collecting more data. It comes from collecting data from more SOURCES...aka triangulation. Triangulation helps you improve the validity, depth, and confidence of your findings by cross-checking insights across distinct but complementary data sources. This helps reduce bias and reduce how much you need from a single data source. For instance, for most B2B personas, just 5 solid interviews will get you 80% there, if you complement it with other sources. So, how can you apply this practically? Let’s go through a real example: Research question: What key benefits should we emphasize in the messaging for our primary persona, Business Ops leads? 1️⃣ Data source 1: Qualitative (what they say) Sources (pick one or more): --> 4 customer interviews with biz ops leads --> Gong snippets from late-stage technical eval calls --> Internal CSM notes during onboarding and renewal   Common quotes include: “Every tool we add creates another integration headache.” “I just want something that doesn’t break other things.” This suggests they care less about flashy features and more about stability, reliability, and ease of maintenance. Now let’s verify this by going thru behavior data. 👇 2️⃣ Data source 2: behavioral (what they do) Sources (pick one or more): --> Support logs and ticket categories for similar accounts --> Feature usage of admin controls, integrations, and audit logs --> Help center searches by role/persona tag Insights: → Ops users are most active in integration, data sync, and permission → High NPS users rarely file tickets, but when they do, it’s for downtime or bugs, not UI complaints This confirms that reliability and ease of system management drive real behavior. 3️⃣ Data source 3: outcome ( what they choose) Sources: --> Win/loss notes --> Procurement objections tagged by role --> Post-sale NPS comments filtered by Business Ops titles Insights: → In wins: “Didn’t have to loop in Engineering” or “We were able to integrate in 1 sprint” → High NPS Ops users cite: “It just works. Rarely need to touch it.” This confirms that the decision patterns match the earlier sentiments. ✅ Triangulated insight: “Business Ops leaders prioritize system trust and low-maintenance integrations; they will choose a solution that promises stability, control, and minimal firefighting over advanced features.” In summary, triangulated findings are more defensible, easier to get buy in and more resistant to bias. You won’t always have time for deep research, especially in a startup. But even a scrappy mix of 2–3 sources can level up your insight. The good news is you can use AI to speed up the grunt work, and then YOU bring the insight. This is the type of work that helps you drive business strategy and get seen. ❓ When you build personas or messaging, what sources do you pull from? #productmarketing #research #strategy #coaching 

We’ve all been there. You’ve just wrapped a round of surveys, or coded dozens of interviews, and now it’s time to find patterns in the data. But the methods you’ve been taught - like PCA or k-means - assume the data is numerical, clean, and fits neatly into a spreadsheet. That’s not what most UX data looks like. In reality, UX data is messy and mixed. We deal with checkboxes, dropdowns, 5-point Likert scales, open-ended tags, and behavioral categories. Most of it is categorical or ordinal, not truly numerical. And when we force these into methods designed for numbers - treating "Agree" like it’s a 4 and "Strongly Agree" like a 5 - we risk drawing the wrong insights or missing what really matters. The good news? There are clustering methods built specifically for qualitative and mixed data. Latent Class Analysis (LCA) helps you find hidden subgroups in categorical survey data. It’s great for segmenting personas or attitudes - based on real patterns, not assumptions. Multiple Correspondence Analysis (MCA) is like PCA, but for categorical variables. It reduces complexity by turning survey responses into dimensions you can actually visualize and cluster - without treating text like math. Factor Analysis of Mixed Data (FAMD) bridges the gap when your data includes both numeric and categorical responses. It lets you uncover structure across both types without losing context. So if your research involves segmenting users based on qualitative input, or making sense of messy attitudinal patterns - don’t default to methods that weren’t made for your data. These three techniques can help you cluster the right way, without compromising on the richness of your research.