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  • How to handle a client who wants AI everywhere

    How to handle a client who wants AI everywhere
    2026/06/18
    I have lost count of the conversations that start the same way at the moment. Someone with a budget and a deadline leans in and says the business needs AI. Where, exactly? Everywhere. In the product, on the site, in the onboarding flow. It's not that they've no idea what they want. They usually arrive with something in mind. The trouble is it's a half-baked solution to a problem that may or may not exist. Push back and you're the difficult one, the blocker, the person who doesn't get it. Go along with it and you build a chatbot nobody asked for. Neither ending is much fun. So I stopped arguing. Now I do something that works far better. I send the whole thing to the users. Don't win the argument. Sidestep it. When someone hands me a shaky AI idea, I don't tell them it's shaky. That's a quick way to make an enemy and lose. Instead I say something like this. "That's a really interesting idea. I think there's something in it. Let me go away and test it with a few users so we build it in the right way." Often that's enough. You sound keen, not obstructive, and the stakeholder feels heard. You've quietly moved the decision out of a meeting room, where the loudest voice wins, and handed it to the only people whose opinion really counts. But sometimes they won't budge. They're so sure they've got it right that testing feels like a waste of time. When that happens, I fall back on one of two tactics. The first is to ask questions. I throw a lot of very specific ones at them about how the thing should work. What happens in this case? What about that one? Before long they start to struggle, and that's the moment to step in. It will be quicker to ask a few users than to guess our way through all of this. The second is to talk about risk. If we build this without testing, there's a real chance we go down the wrong path and waste the budget. So I ask whether they're happy to own that risk. In my experience, nobody ever is. The moment they hesitate, you've got your user research. If the idea is hollow, the users will tell you, and you get to be just as surprised as your client. No bruised egos. Just evidence. And if the idea is solid, even better. You now know it's worth building. But validation isn't a thumbs up or a thumbs down. The real prize is what you learn in those conversations. The same questions that tell you whether to build also tell you how to build. The questions worth exploring When you sit down with users, you're not just asking "would you use this?" You're working out the shape of the thing. Three questions matter most. How much control do they want? Ask people how much say they want over what the AI does on their behalf. Some will want to set it and forget it. They'd happily never see it. For them, the best answer is an invisible solution. No interface, no buttons, no chat window. The AI gets on with the work in the background and the problem quietly goes away. Others will want their hands on the wheel. They don't trust a black box making choices for them, and fair enough. The moment people want control, your invisible solution becomes a visible one, and you've got an interface to design. You only know which camp they're in because you asked. How do they want to see it? If it does need to be visible, the default everyone reaches for is text. That's usually the client or stakeholder talking, not the user, and it's rarely a deliberate choice. They land on text because it's familiar, not because it gets the point across best. This is exactly where asking the user opens things up. Ask users what they're actually trying to understand. Often a chart, a simple dashboard, or a quick visual does in a glance what a paragraph fumbles. AI is getting genuinely good at generating that sort of thing on the fly, so there's no reason to settle for a wall of prose when a graph would land faster. How do they want to interact with it? There's one last thing to ask, which is how people want to interact with it. Most will expect a conversation. Type a question, wait, read the answer, type again. For plenty of tasks that's slower and more irritating than the alternatives. A few form fields can beat a back-and-forth with a bot that keeps asking you to clarify. A dashboard can feel like the most natural way in the world to poke at data. Ask users how they'd rather do it, and many will tell you the chat box was never the point. Let the users build your case Notice what's happened here. You haven't had a single argument about whether AI belongs in the product. You've turned a turf war into a research question, and come back with answers nobody can wave away. Maybe the project dies because users don't care. Maybe it lives, but as a quiet background helper rather than the chatbot your client pictured. Either way, the decision was made by the people who'll live with it, not by whoever was most confident in the room. That's a far stronger place to design from. And it's a much easier life than being the one who's forever saying ...
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    6 分
  • The quick wins racket (and why I'm part of it)

    The quick wins racket (and why I'm part of it)
    2026/06/04
    Here is roughly how every conversion rate optimization project I take on begins. We get through introductions, I sketch out an approach, everyone nods politely, and then, usually about forty minutes in, someone leans forward and asks the question. The quick wins question. The "what can we do this quarter" question. The "what's the easy thing we can ship before the board meeting" question. I always nod sympathetically. I always say yes, of course, there are some quick wins we can target. I always deliver them. And for a long time I told myself I was being responsive to client needs, which is the polite consultant phrase for "I know what they want to buy and I'm cheerfully selling it to them." But after enough years of this, I've started to notice that the clients who fixate on quick wins don't actually win much. The ones who do best treat quick wins as the opening move and then get on with the actual work. So, awkwardly, here we are. A grudging defense of quick wins I should be careful here, because it would be very easy to read what follows as "quick wins are bad and you should feel bad for wanting them." That isn't quite the argument. What quick wins actually do well Early in an engagement, a few well-chosen tests genuinely earn their keep. They build trust with stakeholders who've spent years being told that CRO is a black art performed by people who own too many ergonomic chairs. They prove that experimentation actually moves the numbers, which is how you get budget approval for anything bigger. They drag a team through the discipline of hypothesis, test, learn, iterate, which a surprising number of teams have not actually done before. And they cough up early data you can wave at finance when you eventually ask to look at the difficult stuff. That is a perfectly reasonable amount of value. The trouble starts when "a few quick wins to get us going" quietly becomes the entire strategy, and we all agree, very politely, to pretend that's fine. Why we end up here (and yes, that includes me) Clients call us in too late There's a timing problem sitting underneath all of this, and it's worth naming first. By the time a company calls someone like me in, the conversion rate has usually been quietly underperforming for a year or more. People will tolerate a slow leak for ages and then panic the moment it becomes a flood. Of course they want quick wins at that point. They want the bleeding to stop, and they want it to stop yesterday. Which is rational, in its way. But it biases the whole engagement before it's even started. We're not having a calm conversation about long-term value. We're triaging. Stakeholders are responding to terrible incentives It's tempting to roll one's eyes at stakeholders for being short-sighted, but honestly, they're not being stupid. The problem is that their incentives are just appalling. Quarterly bonuses reward this quarter's number. Senior leadership wants to see green arrows every month. Championing a structural fix that takes nine months to land is a career risk in a way that "we lifted click-through by three percent" simply isn't. Small experiments feel politically safe. Big bets feel like the kind of thing that ends up in a LinkedIn post about your unexpected career pivot. Agencies and consultants are complicit And while I'm cheerfully pointing fingers, some of them point straight back at me. Agencies and consultants are part of the problem. We are, in fact, a substantial part of the problem. Our business model rewards short engagements, monthly reports stuffed with reassuring green ticks, and the constant low-grade panic of needing to demonstrate value inside ninety days. We are structurally set up to find things to optimize. We are not structurally set up to walk into a steering committee and say, "Look, your returns process is the actual reason your customers leave. None of us can fix that with a button test. Sorry about that." The slow, accumulating cost The trouble with an all-quick-wins strategy is that the damage compounds out of view. The easy wins run out For a start, the easy stuff gets used up. Most pages have already had their obvious tests run, so what's left tends to move the needle less and less. Diminishing returns are a real thing in CRO, and I'm always slightly amazed we don't talk about them more, given how much of our work rests on the cheerful assumption that they don't apply to us. The structural issues never get touched Meanwhile, the bigger problems never get looked at. Refund policies, product photography, page weight, customer service quality, the post-purchase experience. These are the things that actually move lifetime value, and they sit serenely untouched while we hold a fourth meeting about whether the button should say "Buy now" or "Shop now." UX debt accumulates quietly But the cost I find most uncomfortable is the slow accumulation of UX debt. Take any homepage that's been A/B tested for eighteen months and look at what's actually there. ...
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    9 分
  • Why UX Should Own Retention

    Why UX Should Own Retention
    2026/05/21
    Most of the organizations I work with are obsessed with the top of the funnel. Ads, SEO, social media, the next campaign, the next traffic spike. The marketing team has dashboards full of acquisition metrics, and the design team usually gets drafted in to support that effort. New landing pages, better hero sections, smoother sign-up flows. That's all fine as far as it goes. I've written an entire email course on campaign landing pages because I genuinely believe most of them are leaking conversions like a colander. But it does mean something important keeps getting ignored. Most organizations have no cohesive strategy at all for retention and upselling. They pour effort into getting the customer through the door, then more or less forget about them once they're inside. The numbers nobody is acting on This is strange when you stop and think about it. The economics of retention have been well known for years. Acquiring a new customer typically costs around five times more than keeping an existing one.Cross-selling or upselling to an existing customer costs roughly 24% of what it takes to win the same revenue from a new one. You don't need to convince someone who's already bought from you. You just have to not screw it up. Retention falls between the cracks So why does retention keep slipping through? In my experience, it's because nobody really owns it. Every other part of the customer journey has a clear home. Acquisition belongs to marketing.Onboarding sometimes sits with product.Support lives in customer success.Renewals end up with sales. Retention falls into the gaps between all of them, which is a polite way of saying it falls on the floor. A real opportunity for UX This is where I think UX has a genuine opportunity. Not just to help with retention, but to own it. To plant our flag and say this is our patch. I know that sounds like more work for a profession that's already stretched thin. But hear me out. UX has a chronic problem with how it's perceived inside organizations. We're seen as the people who make screens look nice. Helpful, but not strategic. The reason for that perception is partly our own fault. We've spent years talking about users when senior leaders are thinking about revenue. We've reported back on usability scores when the board is looking at MRR and churn. Nobody at the top of an organization wakes up worrying about whether the user's mental model matches the interface. They worry about lifetime customer value. They worry about monthly recurring revenue. They worry, sometimes very loudly, about churn going in the wrong direction. And yet plenty of businesses worry about those numbers without ever actively tracking them. Nobody is responsible for measuring them, so they sit in the background as a vague anxiety rather than a managed metric. If the UX team picked up that responsibility, and started tying our work to those numbers, our standing inside the business would change dramatically. We'd stop being the screen-prettifying team and start being the team that protects revenue. That's a very different conversation to have with a CFO. Why retention is a UX problem in disguise The other reason retention is such a good fit for UX is that the levers are largely ours already. Customers usually leave because something in the experience disappointed them. They couldn't find what they needed.The product didn't deliver what they expected.Support was a maze.The onboarding fizzled out before the value clicked. Every one of those is a UX problem dressed up as a business problem. The same goes for upselling. Customers buy more from companies that have nurtured them properly, where the experience has built trust over time. You can't bolt that on with a clever email campaign three months in. It has to be designed. 🎤 Free workshop: Giving Your Users a Voice in Every Decision with AI Tuesday, 9 June 2026. One hour live, plus Q&A with me. Most personas die quietly in a shared drive. I'll show you how to build AI-powered personas that focus on what users are actually trying to do, and how to make them available on demand so anyone in your organization can consult them at the moment decisions get made. Register for free What this looks like in practice A few starting points. 1. Change your KPIs If you're still reporting on task completion rates and System Usability Scale scores, you're speaking a language the business doesn't really care about. Pick one or two retention metrics and put them at the top of your dashboard. Any of these work: Churn rateRepeat purchase rateLifetime customer value 2. Audit the post-purchase experience Most organizations have spent years polishing what happens before the credit card comes out, and almost no time at all on what happens afterwards. That's where the easy wins tend to be: OnboardingThe first month of useThe renewal flowThe upgrade prompts 3. Get involved in cross-functional work Retention sits across teams, so if you wait for someone to invite you to the ...
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    6 分
  • From Doer to Director: The AI Mindset Shift

    From Doer to Director: The AI Mindset Shift
    2026/05/07
    There’s a scene in the Steve Jobs biopic where Steve Wozniak asks Jobs what he actually does. Wozniak understood his own role clearly: he was an engineer. He wrote code. He built things. But Jobs? Jobs described himself as the conductor of an orchestra. I’ve been thinking about that exchange a lot lately, because I think it captures exactly where we’re all heading. AI isn’t turning us into supercharged doers. It’s turning us into conductors, and that requires a completely different mindset. The problem nobody talks about I’ve been coaching a number of people on integrating AI into their workflows recently, and I keep running into the same pattern. The people who aren’t getting time savings from AI aren’t failing because they don’t understand what it can do. They’re not failing because they lack access to the right tools. They’re failing because they’re fundamentally disorganized. AI is only as useful as the foundation it’s built on. If your work processes are messy, your context is scattered, and your task management is a loose collection of mental notes and sticky tabs, AI can’t do much for you. It needs structure to work from. I hear this complaint constantly: “AI has been mis-sold to me. I’m not saving any time.” But it hasn’t been mis-sold. It’s just that AI can only deliver on its promise if there’s an organized workflow underneath it. Build that first, and the time savings follow. That’s why I’ve written before about building AI playbooks and developing proper AI skills. These aren’t nice-to-haves. They’re the infrastructure that lets AI actually work. The conductor problem But here’s the deeper shift, the one that’s genuinely harder to adapt to. When you’re doing tactical work, you’re usually focused on one or two tasks at a time. You go deep, you finish a thing, you move on. It’s cognitively manageable. A conductor doesn’t work like that. A conductor holds the entire orchestra in mind simultaneously: what the strings are doing, where the brass comes in, what the percussion is building toward. They’re not playing any of the instruments. They’re managing the relationships between all of them. In a world of AI agents, we’re going to be managing multiple projects running in parallel, all moving faster than any human team would. We’re task-switching constantly. We’re accountable for outputs we didn’t directly produce. And we have to resist the urge to dive in and do the work ourselves, because that’s precisely where we get bogged down. The design leader parallel This isn’t a new challenge, as it happens. Design leaders face exactly this transition when they move from senior practitioner to managing a team. I’ve watched a lot of talented designers struggle with that shift. They get promoted because they’re brilliant at the work, and then they spend the next year quietly sneaking back into Figma because they can’t let go of doing. They micromanage their reports. They redesign things that were already fine. They can’t operate at the level of abstraction that leadership requires. Working with AI agents is going to feel very similar. The temptation to wrestle with the AI until it produces exactly the output you had in your head, rather than accepting a good result and moving on, is going to be real. Learning to let go of that control is a skill in itself. The good news is that unlike a team of designers, you can’t upset an AI agent by micromanaging it. But you can waste enormous amounts of time doing it, and that defeats the whole point. AI burnout is already real There’s one more aspect of this I want to flag, because I don’t think it gets talked about enough. When you’re managing a team of agents all moving at AI speed, the cognitive load is significant. You’re context-switching constantly across multiple workstreams. Things are completing faster than you can review them. It’s relentless in a way that managing a human team simply isn’t. This is what’s increasingly being called AI burnout. Learning to pace yourself, to batch your reviews, to build in breathing room: these are the organizational skills that will separate people who thrive in an AI-augmented world from those who burn out in it. Where to start If I had to distill this to one practical thing: start building the habits of a manager now, before the agents fully take over. Get organized. Build the infrastructure that AI needs to work from. Practice delegating, even to imperfect tools, rather than doing everything yourself. Work on your ability to hold multiple projects in your head without losing the thread on any of them. If you want help working through that transition, I offer coaching specifically for this. It’s something I’m increasingly focused on, because I think it’s one of the most valuable things I can help people with right now. I’m also running a workshop with Smashing Magazine in July. Modern UX Practitioner covers a lot of ...
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    6 分
  • Why UX Teams Need a Maturity Audit Right Now

    Why UX Teams Need a Maturity Audit Right Now
    2026/04/23
    Something uncomfortable is happening in organizations right now. UX teams are being quietly reassessed. AI has disrupted the field, leadership expectations have gone unmet, and there’s a growing sense that UX hasn’t delivered what it promised. The conversations are happening, but often not with the people who actually do UX work. If you’re in a UX role, decisions about your team’s future might be forming in rooms you’re not in. That’s the situation I’ve been thinking about lately, and it’s why I want to talk about UX maturity audits. Not as a defensive measure or a tick-box exercise, but as a genuinely useful tool for getting ahead of a conversation that’s already underway. The expectation gap is real A lot of the cynicism toward UX right now traces back to one thing: overselling. Leadership was told UX would deliver a hundredfold return on every dollar spent. That figure gets thrown around a lot, and someone took it seriously enough to hire one UX person and wait for the magic to happen. It didn’t. That disappointment is partly our industry’s fault, though it’s not something we often admit openly. We’ve marketed UX with promises that assume a level of organizational change nobody warned leadership they’d have to make. Hiring one person doesn’t transform an organization into a user-centric one. It never did. There’s a certain naivety in the idea that a single hire will magically produce amazing experiences, without understanding the breadth of change required for an organization to truly become user-focused. But plenty of people implied it would. The result is a leadership team that feels, not unreasonably, like they were sold something that didn’t arrive. Why waiting is a bad idea The natural response to this situation is to keep your head down and hope things settle. Understandable, but a mistake. If leadership is already souring on UX, the absence of any structured conversation about what UX is actually delivering gives that skepticism room to grow unchallenged. Decisions start getting made. Quietly, and without much input from the people who understand what’s actually happening. A proactive UX maturity audit changes that dynamic. Instead of waiting to be judged, you’re shaping the conversation. You’re the one bringing evidence, framing the questions, and defining what success looks like. That’s a considerably better position to be in. And it’s not just damage control. Even mature, well-functioning UX teams benefit from this kind of review. There’s always a next stage. Whether it’s wider adoption, better integration with product teams, or moving toward something more democratized, an audit helps you see where you are and decide where to go. What a solid audit covers A UX maturity audit should cover five areas. Not exhaustively, but enough to give you a real picture. Strategy and leadership. Does UX have a seat at the table? Is there genuine sponsorship from someone with budget and influence, or is UX being practiced in a corner while real decisions happen elsewhere?Culture and capability. How widely does the organization understand what UX actually involves? Are there training pathways and career development? Or is it just a job title a few people happen to have?Research and design processes. Is UX practice consistent, or does it depend entirely on who’s available? Are designers and researchers involved early, or called in after the big decisions are already made?Outcomes and measurement. Can the team point to specific improvements in user outcomes? Are there agreed definitions of what success looks like, and is anyone actually tracking it?Cross-functional integration. Is UX embedded across teams, or sitting in its own silo waiting for people to come to it? None of these are particularly complicated questions. The hard part is being honest about the answers. The difference between a real audit and a survey An audit that just collects opinions tells you what people think, which is interesting but not necessarily accurate. A good audit looks for evidence. That means checking whether research plans actually exist. Whether findings get used or disappear into a folder. Whether design systems are maintained or quietly falling apart. Whether the team can point to specific recent changes that improved user outcomes rather than just shipped features. But the more revealing question is often why these things aren’t happening, because the answer usually points straight to the organizational problems that stop UX from gaining traction in the first place. A missing research plan isn’t just an admin gap. It’s often a signal that no one with authority has made space for it, or that the team has learned it wouldn’t be taken seriously anyway. The questions worth asking aren’t simply “how good is our UX?” They’re “how well is UX supported here? How consistently is it practiced? What would move us forward?” This shifts the audit from a performance ...
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    6 分
  • Democratizing UX with AI

    Democratizing UX with AI
    2026/04/10
    I've spent a lot of years arguing that most organizations have the wrong mental model of what a UX team is for. In the vast majority of organizations, UX is dramatically underinvested. You have one UX person, or at most a small team, supporting an organization with dozens of developers, product managers, and business analysts. Or a small digital team made up of a variety of disciplines and generalists, supposed to raise the quality of every digital touchpoint across an organization of several thousand. In that environment, expecting UX to own and shape the entire user experience is not a strategy. It is wishful thinking dressed up as one. The only approach that actually makes sense is democratization. Instead of trying to do everything yourselves, your job is to spread the capability: set the standards, train people, and give everyone who touches digital the knowledge and tools to apply UX best practice on their own. I've written about this for years, and most UX professionals I talk to agree with the principle. The problem has always been the execution. The playbook was the best answer we had For the past decade or so, the most sensible response to this challenge has been the digital playbook. A playbook, in this context, is a collection of policies, principles, standard operating procedures, and training material that documents how the organization should approach digital work. Done well, it does several things at once: it educates people who don't have a UX background, it standardizes how work gets done, and it gives the UX or digital team something to point at when a stakeholder wants to skip testing or cram twelve things onto a homepage. The UK Government Digital Service manual is probably the best public example of this. Comprehensive, well-structured, and genuinely useful. It also took a significant amount of work to produce, and presumably even more work to get people to actually use. The UK Government Digital Service Manual is probably the best example of a digital playbook. That last part is the problem with most playbooks. They ask a lot of the people you want to reach. If a product manager wants to run a quick survey to inform a decision, they now need to find the right section of the playbook, absorb methodology they've never thought about before, learn to apply it to their specific situation, and avoid the dozen ways this kind of thing typically goes wrong. That is a reasonable request if surveys are their job. It is a significant ask if they have three other priorities and a deadline on Friday. The playbook shifts the burden of UX knowledge from the UX team onto everyone else. In theory, fine. In practice, people are busy, and busy people take shortcuts. I say this having spent years advocating for playbooks, so make of that what you will. What AI changes about this picture I've been building out a library of AI skills for my own consulting practice over the past year or so, and somewhere along the way I realized these are doing the same job as a playbook, just in a radically different form. An AI skill, if you haven't come across the term, is a reusable standard operating procedure that an AI can follow on demand. You write it once, document the process in enough detail that an AI can apply it reliably, and from that point on anyone can use it without needing to understand the underlying methodology. This is what makes them interesting at an organizational level. A well-designed AI skills library doesn't ask your product manager to read the playbook before running a survey. It lets them say, "I need to design a survey to find out why users are dropping off at checkout," and have an AI walk them through the process, applying your organization's standards as it goes. The best practice is embedded in the skill. The person using it doesn't need to have absorbed it first. That is a qualitatively different proposition from anything a static playbook can offer. What an organizational AI skills library actually looks like The specific skills worth building will vary depending on the organization. But for a UX or digital team trying to extend their influence, the candidates tend to cluster around the tasks that non-specialists most often get wrong. Survey design is an obvious one. Writing questions that don't inadvertently bias the answers is harder than it looks, and most people who aren't researchers have no idea how their phrasing is leading respondents astray. A skill that guides someone through question design, flags leading language, and checks for common structural problems would save a lot of quietly-useless survey data from being collected. Prototype testing is another. The basics of a usability test, what to observe, what to ask, how to avoid putting words in a participant's mouth, are genuinely learnable. The problem is that someone needs to learn them before running the test, not during it. You could build skills for writing user stories that capture real intent rather than ...
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    7 分
  • Your AI Toolkit Is Your Competitive Edge

    Your AI Toolkit Is Your Competitive Edge
    2026/03/26

    TL;DR: AI skills are reusable, chainable instructions that tell AI exactly how to complete a specific task your way. Building your own library of them now gives you a compounding advantage that will only grow over time. This post explains what they are, why they matter, and how to start building yours.
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    11 分
  • Is your website copy faceless?

    Is your website copy faceless?
    2026/02/26
    I was halfway through writing an article about generic website copy when something uncomfortable occurred to me. I should probably check my own website. My headline at the time read: "Helping You and Your Users Succeed." On the face of it, that doesn't sound terrible. It's positive, it's benefit-focused, and it sounds like exactly the kind of thing a UX consultant should say. The problem is that it also sounds like exactly the kind of thing every other UX consultant says. And their accountant. And possibly even their office cleaner! Generic copy is one of the most common problems I encounter doing conversion rate optimization work, and like a doctor who ignores their own symptoms, I had been sitting on a headline that failed every test I apply to client websites. So let's talk about how to spot problems and how to fix them. Three Questions That Will Expose Weak Copy When I'm reviewing website copy with clients, I use 3 simple questions to find out whether a value proposition is doing any real work. Could this statement apply to other products or services? A value proposition should be specific enough that it only makes sense in your context. “Help you and your users succeed” could work just as well on a SaaS website or on the site of a user researcher. If it can work on a different kind of website, it isn't a proposition at all. It's just a sentence. Could a competitor make this claim? If your direct competitors could copy-paste your headline and it would work just as well for them, it isn't differentiating you. It's just noise. Would the opposite statement be ridiculous? This is my favorite test, because it exposes just how empty a claim can be. If no company would ever say "We're helping your users fail" or "We provide terrible customer service," then the positive version isn't telling anyone anything. You're essentially saying "We are not actively terrible," which is not much of a selling point. Apply those 3 questions to my old headline. "Helping You and Your Users Succeed." Could it apply to other services? Absolutely. A web developer, a copywriter, and a business coach could all put it on their homepage without anyone raising an eyebrow.Could competitors claim it? Every UX consultant on the planet already does.Would the opposite be valid? No company would ever say "Helping You and Your Users Fail," which means the positive version communicates precisely nothing. It fails all 3 tests, which was enough to make me start over. Being Specific Is Harder Than It Sounds The fix sounds simple. Just be more specific. But that's where most people get stuck, because specificity requires you to actually commit to a position. Vague copy is often a symptom of vague thinking about what you offer and why it matters, and confronting that is a bit uncomfortable. In my case, getting specific meant being honest about what I actually do and why it's different. I work across 3 disciplines that most consultants treat as entirely separate. Conversion rate optimization is about improving customer acquisition.UX strategy is about improving retention once customers arrive.Design leadership is about getting the organizational buy-in to implement changes at all. Most consultants offer one of those. I work across all three. That led to a new headline: "Your Digital Funnel Leaks in 3 Ways. I Fix Them All." It passes the first 2 tests cleanly. It couldn't apply to a web developer or a copywriter, and a pure CRO specialist or a pure UX designer couldn't honestly claim it. The third test is more nuanced. If you literally flip it, "Your digital funnel works perfectly, and I'll make it worse" is clearly absurd. But a specialist could legitimately say "Your funnel leaks in one place, and that's what I fix," which is a valid positioning rather than a ridiculous one. That's worth being aware of: the third test is good at catching empty aspirational claims, but specific copy can still be outflanked by variations rather than direct opposites. The real differentiating work happens in tests 1 and 2. Back Up Your Claims With Evidence Specificity is a strong start, but evidence makes claims even harder to ignore. The more proof you can attach to a statement, the more credible it becomes. "We provide great customer service" is vague. "Our clients rate us 4.9 out of 5 for responsiveness" is specific and verifiable. "We're experienced professionals" is empty. "We've delivered over 200 UX audits for organizations ranging from NHS trusts to e-commerce startups" gives the reader something real to hold onto. I won't pretend I always have perfect statistics to hand. Often I don't, and in those cases I try to ground claims in specific outcomes or named examples rather than numbers. But any evidence is better than a confident assertion with nothing behind it. Try This on Your Own Homepage Pull up your website's homepage right now and read your headline and opening paragraph. Then apply those 3 questions. If your copy could live comfortably on a ...
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    6 分