Understanding RAIDA Buy
RAIDA Buy is an independent platform to exchange USD for CloudCoins.
To understand the platform’s challenges, I held a stakeholder meeting with the founder, PM, and designers to gather context and requirements.
I asked targeted questions to define the core problem, understand user groups, and identify constraints. From this, I discovered that many first-time users did not complete purchases through RAIDA Buy, resulting in a low return on investment.
Another key insight was that users experienced friction across the end-to-end journey, from purchase to post-purchase, caused by unclear features and an overly complex interface.
Initial research
To validate initial concerns, I conducted a Heuristic Evaluation using Don Norman’s usability heuristics. Collaborating with other designers, I was able to confirm several usability issues. See key takeaways in the image below
Discovery & insight gathering
To understand the user perspective, I conducted sessions with four user groups — a mix of existing and potential users. I mapped their journeys to uncover current behaviors, identify opportunities, and create a customer journey map as a shared reference for the team.
During the exercise, I focused on:
Identifying and eliminating friction points in the buy flow
Understanding the causes of poor customer retention
Defining what a seamless information experience looks like for users on the website
Based on these findings, I was able to clarify both the business goal and the user need:
Business Goal: Improve ease of use in the buy flow and increase customer retention
User Need: Clearly communicate expectations and next steps after purchasing CloudCoins
UX challenges in the Buy Flow
A complex and cluttered interface confused users
Pricing in CloudCoin vs. USD was unclear
Users could only purchase in fixed multiples (i.e. 100s), discouraging smaller transactions and leading to drop-off
New users didn’t know what to do with their CloudCoins after purchase
Anonymous, pay-first transactions reduced user trust
Users were unaware of other platforms that accepted CloudCoin
Designing a better Buy Flow
Based on our early findings, we prioritized the following design improvements to reduce friction, build trust, and guide users through a more intuitive buying experience:
Streamlined user flows to support easy decision-making
Clear and simple information at every step
Fintech-based best practices for credibility and usability
Routing users to partner platforms to expand utility
Encouraging the Skywallet as the go-to solution for storing purchased coins
Low-Fi Design
Insights from the customer journey map informed the creation of multiple user flow variations for the interface design.
Research Methods
Given the stage of development and availability of wireframes, I selected a qualitative approach using one-on-one usability interviews. This allowed me to observe and probe users’ experiences with the new wireframes in real time.
To guide the sessions, I developed a research plan that included:
Objectives
Hypotheses
Research questions
Interview script
Participant Criteria
I identified three key user groups:
Existing CloudCoin users
Cryptocurrency enthusiasts
Libertarians – individuals who value autonomy and political freedom. I included this group to better understand ideological motivations for adopting cryptocurrency.
Recruitment Strategy
I recruited participants via LinkedIn, Facebook, Twitter, and Instagram, targeting online communities such as:
Foreign Exchange & Cryptocurrency Trader Network
CryptoBuzz Premiere
Ewallet, Bitcoin, Digital Wallet, Cryptocurrency
Libertarian Party of America
To manage outreach and tracking, I created an Excel sheet to monitor recruitment progress and engagement.
Validating with users
I conducted the first round of online usability testing with 12 participants. Each week, I created presentation slides to share key findings, user observations, and proposed solutions with stakeholders — including the founder, project manager, and product designers.
As I continued testing, feedback was actively implemented into the low-fidelity designs, allowing for iterative improvements based on real user input.
