Clarity in a complex, AI-powered ecosystem for Bloomreach.
Brief
Merchandisers rely on Bloomreach’s dashboard to stay ahead of trends and optimize digital storefronts with AI-driven tools. I helped redesign the dashboard to simplify access to key insights, making tools like analytics and Loomi AI easier to use without overwhelming users. The result was a clean, action-focused interface that empowers merchandisers to make smarter, faster decisions.
TEAM
Employer
Bloomreach
Timeline
Mar 2022 — Jun 2022
MY ROLES
The Challenge
The goal was to create a coherent, intuitive interface that allowed merchandisers to efficiently manage complex merchandising tasks while balancing automated, AI-driven recommendations with hands-on control. Merchandisers needed to easily switch between tools, such as search and category merchandising, and quickly surface the most relevant insights to support business goals like seasonal campaigns or brand promotions. A key challenge was enabling users to take action on these insights in real-time without getting lost in the complexity of overlapping rules, data overload, or inconsistent UI patterns. The interface had to support both detailed manual adjustments and more high-level, automated workflows—catering to a wide range of user expertise—while ensuring that every action felt seamless, purposeful, and aligned with their merchandising strategy.
Approach
Overwhelming Data
Loomi’s AI-driven insights generated a large volume of actionable data, but users struggled to surface the most critical information quickly.
Difficult Navigation
As the number of features expanded, it became harder for users to locate relevant tools and insights.
Fragmented UI
Inconsistent design patterns across the dashboard, added over time, created a fragmented experience that detracted from user engagement and productivity.
Surface Relevant Data
Users wanted Loomi’s AI to focus on surfacing the most actionable data, like daily sales trends, product performance, and real-time recommendations. The data needed to be immediately visible without requiring users to dig for it.
Customization Needs
Merchandisers also wanted the ability to customize which AI insights were prioritized on their dashboards, allowing them to tailor their experience to their unique business goals.
Simplified Data Views
While interactive data visualizations were planned, we initially scoped them down to easy-to-read charts and tables, which conveyed key information such as top-performing products and regional performance trends.
Conclusion
Empowering merchandisers with AI-driven insights in an intuitive, action-oriented dashboard.
While the redesigned dashboard has been well received, there are additional improvements we aim to make. We’re planning to introduce further customization options that allow users to tailor the AI insights to their specific business needs, along with more advanced filtering capabilities for the data. We’re also working on incorporating real-time, in-app notifications based on AI-driven insights to provide an even more seamless experience.
Key Outcomes
• Reduced Drop-Off Rates: With AI-driven insights front and center, users now spend more time interacting with data, resulting in fewer drop-offs, particularly on mobile.
• Enhanced Workflow Efficiency: By providing action-oriented insights, users can now act on recommendations and performance metrics faster, improving their overall workflow.
