User Research UX Copywriting Style Guide IA Usability Testing
Content Strategy
Transforming skepticism into confidence through research-driven content strategy
As a student at the UX Writing Academy, I designed and tested a trust-building content strategy for an AI tool that helps people develop emotional intelligence.
Process Highlights
- Researched competitor products, mined user conversations, and surveyed 40+ potential users to identify SWAY's market niche
- Built a mini style guide to align voice, tone, and content decisions with research insights
- Created user journey maps to address AI skepticism and guide content strategy
- Designed onboarding and feature flows that prioritize trust and privacy-first messaging
- Conducted highlighter A/B copy tests to pinpoint confusing or off-putting language
- Reframed intimidating terms (e.g., “assessment” → “quiz”) and clarified feature naming to match user expectations
- Added contextual tone shifts and guided tooltips to improve editor usability
- Content Designer
- Product Designer
- UX Researcher
- Airtable
- Figma
- Google Suite
See the detailed research report
Read the full style guide
See the detailed testing report
Explore the final prototype
10 weeks
Results
44%
of SWAY’s target user group had privacy concerns about AI, directly informing SWAY’s privacy-first content approach
30+
screens created and tested
3
flows designed
3
distinct user archetypes identified
Overview
SWAY helps people develop emotional intelligence through AI-powered writing analysis and skill-building. But in a market saturated with AI hype, potential users had concerns about their privacy. I used research to understand those concerns, then designed content that addresses them directly to make SWAY feel approachable.
Process
Researching first so the content would click
Before designing anything, I needed to understand what people actually wanted from emotional intelligence tools. Through competitor analysis, conversation mining, and a survey, I uncovered SWAY’s niche.
Key insights
- There is a market gap for a tool that offers both writing analysis and EI training in one place
- Users need to be both informed and convinced before trying a tool like SWAY
- Privacy and ethical AI were top concerns and must be central to the experience
This research shaped every content and feature decision that followed.
Building a mini style guide to simplify and scale
Raw data is great, but not exactly writer-friendly. I turned key insights into a mini style guide: a quick, reliable reference to keep SWAY’s voice focused, clear, and aligned with what users needed.
What’s inside
- A clear content mission
- Three core voice traits: encouraging, expert, and transparent
- Dos and Don’ts with copy examples
Creating a user journey to tackle AI skepticism
Research showed users worried about AI, especially privacy. Enter Evelyn—one of my personas: a 45‑year‑old freelance journalist who's great with words but highly skeptical about AI’s trustworthiness. Mapping her journey revealed key moments to ease concerns with clear, targeted content. This blueprint guided wireframes and kept every screen aligned with both user needs and business goals.
Solution
Using research insights and the user journey, I designed SWAY’s main experience:
- An external flow that educates users about SWAY while addressing their concerns
- An internal flow where users discover their emotional strengths and build skills
- An editor tool that gives feedback on the emotional intelligence of users’ writing
A/B testing copy with highlighters to cut friction
My wireframes looked solid on paper, but I needed to hear from potential users. I printed two versions of each key screen and ran highlighter tests with four participants, having them mark what worked, what didn't, and why. Their feedback was crucial in guiding SWAY's latest iteration.
Reframing emotional intelligence to feel approachable
Test participants reacted negatively to words like intelligence, score, and assessment—they felt judged and tested. Swapping in gentler terms like profile, strengths, and quiz made the experience feel more approachable and shifted the tone from intimidating to inviting.
Removing judgment and clarifying meaning with one word swap
Test participants were confused by writing feedback—they expected red marks or criticism, which wasn’t the message I wanted to send. Swapping in writing analysis clarified the feature’s purpose instantly, making it feel more neutral and data-driven. This simple change cuts confusion and better matches user expectations.
Adapting tone to match user mindsets in different contexts
Test participants preferred different voices depending on the situation: conversational and warm when learning about SWAY, but clear and concise when completing tasks. I tailored the content accordingly—friendly and descriptive for onboarding, short and direct for repetitive actions. This context-aware tone made SWAY feel intuitive and easy, not demanding.
Redesigning onboarding in the SWAY Editor to clear confusion
Test participants were lost navigating SWAY's editor modes, even after the tour. Static explanations weren't working, so I added automated tooltips that guide users step-by-step as they enter. I also rewrote mode descriptions to make their functions clearer and easier to understand.
Results
SWAY is positioned to earn user trust
I designed an experience that addresses concerns upfront, feels approachable and inviting, and adapts smoothly to different contexts. By reworking off-putting language and using privacy-first messaging, SWAY now speaks to users who want to grow their emotional intelligence—capturing market share missed by current tools that feel judgmental or invasive.
The research framework and style guide I created offer a repeatable model for designing in AI-skeptical markets.
Takeaways
This project pushed me to solve one of content design's tougher challenges: earning trust in a skeptical market. I learned how to turn research into strategic content, navigate complex emotions around AI, and design experiences that feel supportive and human. Working end-to-end, from discovery through usability testing, reinforced how crucial research and validation are to building tools people truly want to use.
Next steps
Test the updated prototype with users to confirm the content addresses their concerns, then iterate based on feedback.