I’m going to tell you how I make sure my words really hit home for people using a product. I mean, in today’s digital world, what we say isn’t just a nice-to-have, it’s pretty much the core of how someone experiences something. But so many businesses are just guessing at what works, and that leads to confusing stuff, people giving up, and frustrated users.
The truth is, really good UX messaging doesn’t just magically appear. It isn’t just from a bunch of creative brainstorming sessions. No, it’s built from solid user research. I’m going to share with you how I figure out what users need, what they prefer, and what gets in their way. This will totally change your messaging from “good enough” to “absolutely essential.” I’m not talking about just tweaking a few words here and there; I’m talking about a complete shift in how you connect with your audience, all based on real data and understanding.
Why User Research Is Absolutely Essential for UX Messaging
Before we get into the “how-to,” let’s talk about the “why.” As writers, our first thought is often to craft a compelling story. But if that story doesn’t line up with how people actually think, feel, and act when they’re using a product, it’s just going to fall flat. User research gives you this clear view, showing you:
- Their Mental Models: How do users actually think about the product or the problem it’s solving? What words do they naturally use? If you call a “shopping cart” a “digital basket,” you’re just making things harder for them.
- Their Pain Points & Frustrations: Where do users get stuck? What makes them hesitate or just abandon what they’re doing? Good messaging can actually address these worries before they even come up.
- Their Motivations & Goals: What are users trying to accomplish? What value are they looking for? Your messaging needs to clearly show how your product helps them get there.
- Their Preferred Tone & Voice: Do they want something formal, super friendly, or somewhere in the middle? If your tone is off, it just feels fake.
- Their Understanding of Jargon & Concepts: Which terms are perfectly clear, and which just leave them scratching their heads? Don’t assume that the words you use internally make sense to everyone else.
Ignoring these insights? That’s like trying to build a bridge without understanding the river’s current. It might look nice on paper, but it won’t last when things get tough.
Phase 1: Planning Your Research – Figuring Out What You Need to Know
Good research starts with really clear goals. If your questions are vague, your answers will be too. Before you even think about how you’re going to do it, pinpoint the exact messaging challenges you’re trying to solve.
1.1 Identify Your Messaging Challenge & Research Questions
Don’t just say, “Our onboarding flow isn’t good.” Get specific about the messaging problem.
Here are some specific messaging challenges I’ve tackled:
- People are dropping off right at the “Confirm Payment” screen. (Challenge: Is the payment summary clear enough? Do they feel secure?)
- New users aren’t even touching “Feature X,” even though it’s a huge selling point. (Challenge: How do I explain Feature X’s benefits and how it works more effectively?)
- Customers keep calling support asking, “How do I reset my password?” (Challenge: Are the password reset instructions visible and easy to understand?)
- Our marketing says we’re simple, but the product feels complicated. (Challenge: Is our tone and simplicity consistent between what we market and what the product actually delivers?)
And then I translate those challenges into research questions:
For that “Confirm Payment” drop-off problem, I’d ask:
- Do users actually understand what they’re confirming?
- Are they worried about their payment info being safe? If so, what would make them feel better?
- Is the value of completing this purchase clear at this stage?
- What specific words or phrases are making them hesitate?
1.2 Define Your Target Audience for Research
Your “user” isn’t just one type of person. I always ask myself: Are we talking about:
- New users vs. existing users? Their needs and what they already know are totally different.
- Users of a specific group? (Like tech-savvy pros vs. casual internet users.)
- Users who had a specific problem? (Like those who couldn’t finish signing up.)
I make sure to find people who genuinely represent the group I’m trying to reach with my new or improved messaging. If I get the right people, my insights will be spot-on.
1.3 Choose Your Research Methods – Matching Tools to Objectives
There’s no single “best” research method, in my experience. Usually, the most effective approach is a mix. Think about qualitative (the “why” and “how”) vs. quantitative (the “what” and “how much”), and what people say (attitudinal) vs. what they do (behavioral).
Qualitative Methods (for understanding the “Why” and “How”):
- User Interviews: These are deep, one-on-one chats to really get at motivations, how they think, and what bothers them. They’re amazing for uncovering subtleties in how users see messaging.
- Best for: Understanding existing messaging frustrations, figuring out the right tone, and uncovering how users think about complex ideas.
- How I use it: I might show a user a specific error message and ask: “What do you think happened here? What would you expect to happen next? How does this message make you feel?”
- Usability Testing (focusing on messaging): I watch users as they interact with the product, paying close attention to where the messaging either helps them or trips them up. I always use a “think-aloud” approach.
- Best for: Spotting confusion, clarity issues, or missing information within a workflow. Pinpointing exact words or phrases that cause problems.
- How I use it: I’ll ask a user to do a task (like “Find and subscribe to a newsletter”). As they navigate, I’ll ask: “What are you looking at right now? What does this button mean to you? Is anything unclear?”
- Card Sorting: Participants group and label information, which shows me how they naturally organize things and what terms they prefer. It’s super useful for structuring navigation or content categories.
- Best for: Understanding user-friendly labels for product features, navigation, or content. It helps me choose the right language for headings and menu items.
- How I use it: I’ll write each feature (like “Account Settings,” “Notification Preferences,” “Billing Information”) on separate cards. Then, I’ll ask users to group them and give each group a label that makes sense to them.
- Tree Testing: This helps me figure out if people can easily find topics within a structured system, like a website’s navigation or a help center.
- Best for: Confirming if chosen labels and the overall structure make sense to users. Finding confusing terms in navigation.
- How I use it: I’ll give users tasks like “Find information about shipping costs.” I then track if they successfully get to the right section based on my proposed labels.
Quantitative Methods (for the “What” and “How Much”):
- Surveys/Questionnaires: I use these to gather lots of data very quickly on user preferences, attitudes, and behaviors. They’re great for getting targeted feedback on specific messaging elements.
- Best for: Measuring how clear specific messages are perceived, gauging preferences for different ways of phrasing things, and finding widespread pain points.
- How I use it: After users complete a task, I might ask: “On a scale of 1-5, how clear was the message on the checkout page?” or “Which of these two phrases (A or B) better describes Feature X?”
- A/B Testing (after launch): This is where I directly compare how two different versions of messaging perform in a real-world setting. It’s the ultimate decider of effectiveness.
- Best for: Optimizing headlines, calls-to-action (CTAs), error messages, or benefit statements. Directly measuring the impact on conversion rates, click-through rates, or task completion.
- How I use it: I might test two different CTA button texts (“Get Started” vs. “Start Your Free Trial”) and see which one gets more clicks.
- First Click Tests: I show users a screenshot of a page and give them a task, then record where they click first. This is excellent for seeing if they immediately understand something or how they intuitively navigate.
- Best for: Seeing if users understand where to click based on current messaging, especially for navigation or key actions.
- How I use it: I’ll show a user my homepage and ask: “Where would you click to find pricing information?” I then analyze their first click to see if it matches where I intended them to go.
My Key Principle: I don’t just pick methods because they’re popular. I choose based on my specific research questions and what I absolutely need to know about the messaging.
Phase 2: Executing Your Research – Getting Actionable Insights
Once my plan is set, it’s time to gather the data. This part requires careful execution and sharp observation.
2.1 Recruit the Right Participants
This is often the trickiest part, in my opinion.
- I define clear screening criteria: I make sure participants fit my target audience (e.g., “owns a smartphone,” “uses online banking,” “has never used a similar product before”).
- I use the right recruitment channels:
- Internal: My existing user base (email lists, in-product surveys).
- External: Recruitment agencies, user testing platforms (like UserTesting.com), social media, professional networks.
- I always offer incentives: People’s time is valuable. Small gift cards, product discounts, or charitable donations really help encourage participation.
- I aim for diversity: I try to get a range of perspectives within my target audience to avoid any biases.
2.2 Craft Effective Research Protocols
This is essentially my script for how I’ll interact with people. A well-designed protocol ensures everything is consistent and I get the most relevant data.
For Interviews/Usability Tests, I always include:
- Warm-up: I start with easy, low-pressure questions to build a good connection. “Tell me a little about how you use [type of product].”
- Task-based scenarios: I design realistic tasks that require them to interact with the messaging I’m looking at. I’m careful not to lead the user.
- What I avoid: “Click this button that says ‘Submit Payment’.” (Too direct)
- What I do: “Imagine you’ve decided to buy this item. Show me how you would complete the purchase.” (This allows them to interact naturally.)
- Probing questions: I use open-ended questions to get detailed answers.
- “What were you thinking when you saw that message?”
- “What does [this word/phrase] mean to you?”
- “What would you expect to happen after clicking this?”
- “If you could change one thing about this message, what would it be?”
- Silence is golden: I let users think, even if there’s an awkward pause. They might be about to share a really deep insight.
- I avoid leading questions: I never put words in their mouth.
- What I avoid: “Did you find that message confusing?” (Implies it should be confusing.)
- What I do: “What did you think about that message?” (Neutral.)
- I record everything: Video, audio, screen recordings, and detailed notes are absolutely crucial for later analysis.
For Surveys, I focus on:
- Clarity and conciseness: Every question has to be crystal clear.
- Avoiding jargon: I use language my audience actually understands.
- Mixing question types: I use open-ended for qualitative insights, and multiple-choice/Likert scales for quantitative data.
- Pilot testing: I always run my survey with a small group first to catch any confusing questions or technical glitches before the full rollout.
2.3 Observe, Don’t Just Listen
Especially in usability testing, what users do often tells me more than what they say.
- Non-verbal cues: I watch for frustration (sighs, squinting), confusion (head tilts, re-reading), or relief (exhales).
- Action sequences: Do they click every link on the page before they find the right one? Do they scroll right past critical information?
- Time on page/task: Unusually long times usually mean confusion or hesitation, which is exactly what messaging can fix.
Phase 3: Analyzing & Synthesizing – Unearthing the Messaging Gold
This is where all that raw data transforms into real, usable insights. It’s not just about reporting what users said; it’s about seeing the patterns and figuring out the root causes.
3.1 Transcribe & Organize Your Data
- ** For interviews/usability tests:** I transcribe the key moments or even entire sessions. I use tools that make searching easier.
- For surveys: I export the data into spreadsheets for quantitative analysis.
3.2 Look for Themes and Patterns
This is the heart of qualitative analysis for me.
- Coding: I read through my data and assign “codes” or “tags” to recurring themes, ideas, or pain points related to messaging.
- Example Codes I use: “Unclear CTA,” “Security concern,” “Missing context,” “Jargon,” “Tone too formal.”
- Affinity Mapping: I write each observation or quote on a sticky note. Then, I group similar notes together to reveal broader themes. This is a powerful way to collaborate as well.
- Example: Several users say “I don’t know what this button does.” Another says “What’s ‘authenticate’?” Another says “Is this safe?” All of these might fall under a wider “Lack of Trust/Clarity on Secure Actions” theme.
- Quantify as much as possible: Even with qualitative data, I count how many users mentioned a specific issue or struggled with a particular message. Saying “5 out of 7 users struggled to understand the term ‘API integration'” is much more impactful than “Some users struggled…”
3.3 Prioritize Messaging Pain Points
Not all issues are equally important, in my experience. I always consider:
- Frequency: How many users actually ran into this problem?
- Severity: How much did it impact their ability to finish a task or understand the product? Did it make them give up?
- Impact on business goals: Does this messaging issue directly affect how many people convert, stay with us, or how many support calls we get?
I always focus my efforts on the messaging problems that have the biggest impact and are mentioned most frequently.
3.4 Formulate Actionable Recommendations for Messaging
This is the bridge between my research and the actual writing. I don’t just state the problem; I propose a solution.
A bad recommendation I’d avoid: “Users don’t understand the pricing page.”
A good recommendation I’d give: “Users found the ‘Tier 3’ label confusing. I recommend using benefit-driven language like ‘Professional Plan for Growing Businesses’ and clearly listing included features directly below each tier, instead of relying on a separate comparison table.”
Here are some types of messaging recommendations I often make:
- Clarity: “Replace technical jargon (‘synchronize database’) with user-centric language (‘update your files’).”
- Conciseness: “Shorten error messages to focus on the problem and a clear next step (e.g., ‘Invalid email address. Please check your spelling’ instead of ‘Error 404: Input validation failed for field ’email_address”).”
- Call-to-Action (CTA) Optimization: “Change ‘Submit’ to ‘Get Started’ on the onboarding form, as users are looking for an action that initiates a process, not just sends data.”
- Tone/Voice: “Inject more empathetic language in cancellation flows to reduce negative sentiment (e.g., ‘We’re sorry to see you go. What can we do better?’ instead of ‘Account cancellation successful’).”
- Information Hierarchy: “Reorder the privacy policy summary to lead with critical user benefits and assurances, moving detailed legal clauses to an expandable section.”
- Proactive Help: “Add contextual tooltip messaging next to complex form fields explaining their purpose.”
- Error Messaging: “For login errors, differentiate between ‘Incorrect password’ and ‘Account doesn’t exist’ to guide users more effectively.”
Phase 4: Implementing & Iterating – The Continuous UX Messaging Loop
User research, for me, isn’t a one-time thing. It’s a continuous process that drives constant improvement.
4.1 Prioritize & Implement Messaging Changes
I work closely with my product and design teams to put the recommended messaging changes into action. I always remember:
- Start small: Tackle the high-impact, easy-to-implement changes first.
- Maintain brand voice: New messaging has to align with the overall brand guidelines.
- Document changes: I keep a record of all messaging updates and the research that informed them.
4.2 Measure the Impact (A/B Testing!)
Once changes are in place, I have to measure their effectiveness. This is where quantitative methods really shine.
- I A/B Test my new messaging: I compare how the old message performed against the new one.
- For onboarding messaging: I look at completion rates, and time to complete.
- For error messages: I look at task completion rates after someone hits an error, and how many support tickets come in.
- For CTAs: I look at click-through rates, and conversion rates.
- I monitor analytics: I look for changes in bounce rates, time on page, engagement with specific features, or how users flow through the product.
- I ask for ongoing feedback: In-app surveys, feedback widgets, and customer support logs can give me continuous signals about how effective my messaging is.
4.3 Iterate & Refine
The results from my A/B tests and continuous monitoring tell me what to do for the next round of research and refinement. Did the new message work as expected? If not, why? Then it’s back to phase 1: I identify the new challenge, define new questions, and conduct more targeted research. This back-and-forth cycle is what truly user-centered design and messaging is all about.
My Role as a Writer: Becoming a Research-Driven UX Strategist
For me, as a writer, embracing user research isn’t just about adding a new skill. It’s about taking my craft to the next level. I’m no longer just putting words together; I’m understanding the very way my users think. I become:
- An Empathic Investigator: I don’t just guess; I uncover concrete user needs.
- A Precision Communicator: Every single word, every phrase, is checked against how users understand it.
- A Strategic Contributor: My insights into messaging actually lead to real business results.
By systematically doing user research for UX messaging, I transform my writing from just insightful guesses into rock-solid, data-backed certainties. This creates digital experiences that aren’t just beautiful, but also intuitive, effective, and deeply meaningful to the people who use them.