Research Methods
I employed a multi-faceted research approach to gain a comprehensive understanding of user needs and behaviors:
• Contextual Inquiry
I observed multiple teams within the company using the software in their natural work environment. This revealed that usage was minimal, with most work being done in spreadsheets instead.
• User Interviews
I conducted in-depth interviews with 12 Amazon sellers of varying experience levels to understand their data management workflows, pain points, and needs.
• Competitive Analysis
I analyzed multiple platforms in the same field, examining their approaches to data visualization, workflow design, and user engagement.
Complex Data Visualization
Users struggled to interpret data presented in the interface, finding it overwhelming and difficult to extract actionable insights.
Inefficient Workflow
The software required too many clicks to complete common tasks, creating friction in the user experience.
Limited Customization
Users couldn't tailor the interface to their specific business needs or data priorities.
Poor Information Architecture
Critical features were buried in unintuitive locations, making them difficult to discover.
Lack of Integration
The software didn't connect seamlessly with other tools in the users' workflow.
• Increase feature adoption rate by 50%
• Reduce time spent on common tasks by 40%
• Improve user satisfaction scores to above 4.5/5
• Create an intuitive, scalable design system for future development
• Need to maintain compatibility with existing backend systems
• Requirement to support both novice and power users
• Limited development resources for implementation
Workflow Disruption
Users were forced to switch between multiple tools because Clove didn't support their end-to-end workflow.
Data Trust Issues
Users were uncertain about the accuracy and timeliness of data presented in the interface, leading them to double-check in other systems.
Mental Model Mismatch
The software's organization didn't align with how Amazon sellers conceptualized their business operations.
Visualization Preferences
Users strongly preferred visual representations of trends and patterns over tabular data.
Design Thinking Approach
I approached this redesign using a design thinking methodology, emphasizing empathy, ideation, and iteration. The process began with an in-depth exploration of our target users' behaviors, needs, and challenges through interviews, surveys, and observation.
Information Architecture Redesign
Based on research insights, I completely restructured the information architecture to align with users' mental models. This involved:
• Card Sorting Sessions
I conducted card sorting exercises with users to understand how they naturally categorize and prioritize different types of data and features.
• Site Mapping
I created comprehensive site maps to visualize the new structure and ensure logical grouping and hierarchy.
Wireframing & Low-Fidelity Prototyping
I created detailed wireframes to establish the basic structure and functionality of each screen. This phase focused on:
• Layout Optimization
Designing layouts that prioritized the most important information and actions.
• Workflow Mapping
Ensuring that common user tasks could be completed with minimal friction.
• Component Definition
Establishing the core UI components needed across the application.
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