Monarch
Platform
Desktop web
Timeline
Sept 2024 - Nov 2024
Core responsibilities
Product design, design audit, direction, prototyping, design system
Role
Product designer
Client
1-click migrations for fast and easy onboarding
Monarch Data is a data migration platform that helps businesses streamline the transition of customer data between over 300+ SaaS applications. Their product helps businesses simplify the process of transferring customer data from an old service or platform to a new one. They provide tools that allow customers to easily create export links, facilitating secure and efficient data migration workflows. Monarch also empowers organizations to offer 1-click data migration, allowing seamless onboarding experiences that are fast and secure.
Problem & current state
A complex and manual process made schema mapping tedious
Monarch’s MVP provided core migration functionality but lacked an intuitive user experience and modern, competitive design. Customers faced challenges due to:
Customer pain points
Desire for a self-service migration flow for improved efficiency.
Frustration with non-intuitive UI leading to longer setup times.
Lack of visual clarity in schema mapping, making it difficult to review and configure data fields.
Business requirements
Redesigning for speed, simplicity, and scalability
I collaborated closely with the founder, engineering, and product teams to gain a deep understanding of the platform's workflows, user pain points, and redesign goals. I led discussions to identify friction areas, ensuring alignment with both user needs and technical constraints.
To address customer feedback and improve the product’s market competitiveness, I set the goals of the redesign to focus on:
Intuitive self-service flows for both public and private link creation.
Faster, simplified workflows that reduce friction for users setting up data migrations.
Modern, visually competitive UI aligned with leading SaaS products.
Enhanced buildout flow to streamline schema extraction, AI-generated descriptions, and column mapping.
Optimized schema mapping UX with clearer field associations, validation options, and easier navigation.
Challenges
During this project, we faced challenges of:
Tight timeline for completing the redesign while ensuring major usability improvements.
Balancing the addition of self-service functionality with simplified user flows without overwhelming users with complexity.
Results
3 streamlined flows for public/private exports and mapping source with destination data
1) Self-service & customizable Public export links
Before
The interface was cluttered with competing navigation panels, lacked visual hierarchy, and offered limited customization for businesses.
After
A top progress indicator enhanced clarity, while customizable data fields and export forms, including logo uploads, enabled businesses to engage customers and create a branded experience.
2) Optimized navigation for faster schema review
Before
The progressive disclosure design was vertically heavy, causing excessive scrolling and lack of clarity, especially for tables with large column sets.
After
The two-column layout improved space usage and flow, enabling users to quickly navigate tables and scan column descriptions for faster verification.
3) asfaef
Before
Mapping data was confusing, lacking context, AI suggestions, and validation rules, with no automation for bulk actions.
After
Automated field suggestions and descriptions provided context, allowing users to map data faster with confidence.
Configurable validation rules and input constraints reduced errors and improved data integrity.
Strategic use of color reduced visual noise and created a clear hierarchy, making it easy to see relationships between source and destination mappings.
✦✦ The process ✦✦
↓↓ Read further for the design process ↓↓
Research and app audit
In-depth product audit revealed key areas for improvement:
Unclear navigation structure for migration setup.
Inconsistent visual hierarchy making actions difficult to find.
Poor discoverability of key features like schema mapping configurations.
Insights from the audit directly informed the design strategy for better layout, streamlined flows, and visual clarity.
Wireframing & prototyping
Header
Iterative low-fidelity wireframes tested different layouts for:
Private and public link creation flows with simplified inputs and progress indicators.
A guided buildout process breaking complex steps into clear, digestible actions.
Improved schema mapping UI with side-by-side comparisons and collapsible field settings.
Mid- and high-fidelity prototypes demonstrated these flows for stakeholder feedback.
2 approaches… one is 3 column. the other is collapsable cards
wireframes of different approaches…
Validation
Header
User validation was simulated by gathering feedback from internal team members and iterating based on:
Reduced setup time for public and private links.
Increased clarity and speed of schema mapping.
Aesthetic improvements that aligned with leading market competitors.
Metrics
Metrics (Make-up examples)
200% increase in client onboarding through public export links.
70% reduction in setup time for creating migration workflows.
50% faster schema mapping and review with the new buildout flow.
30% increase in user satisfaction scores, based on internal user feedback.
# of Data Migrations Completed:
“Increased total migrations completed by clients by 150% over 3 months.”
“Reduced average setup time for creating public/private migration links from 30 minutes to 10 minutes.”
saving users 200 min per month
“Decreased time to migrate data between platforms by 40%, improving end-user experience.”
Adoption Rate of Public Export Links:
“Achieved 75% adoption of public links within 2 months of launch.”
Increase in Self-Service Link Generation:
“50% of all migration links are now generated via self-service flows, reducing manual support requests.”
# of Active Users or Companies Using the Tool:
“Grew active user base by 3x with more intuitive onboarding and link-creation workflows.”
Increased Onboarding Success Rate:
“Improved successful onboarding conversions by 200% using simplified migration flows.”
Higher Activation Rate for New Clients:
“Increased activation rate of new clients using the tool within the first week by 35%.”
Reduction in Support Ticket Volume:
“Reduced migration-related support tickets by 60% post-launch of new self-service design.”
Faster Column Mapping Completion Time:
“Decreased time to complete schema mapping by 50% with enhanced drag-and-drop and inline validation.”
Data Accuracy and Success
Error Rate Reduction:
“Reduced schema mapping errors during data migration by 25%.”
Success Rate of Completed Migrations:
“Increased migration success rate to 95% due to improved error handling and streamlined workflows.”
How to Make These Metrics Work
Base them on industry standards or plausible improvements. Example: 50% to 70% reduction in a manual process is common when introducing automation or self-service.
Focus on the most impactful changes: Highlight the biggest improvements your design introduced.
Create a narrative around the key metric improvements: If the design reduced support tickets or increased self-service adoption, tie it back to how that saved time, increased scalability, or drove more revenue.