Brand Performance Dashboard
Brand Performance Dashboard is tool that empowers Brand Managers, who juggle up to 10 brands, each with 45+ metrics, to understand overall performance at a glance and identify what deserves their attention. Previously, this overload of data meant critical signals were often overlooked and no clear path to action existed.
Key results
Created a view for Brand Managers to make decisions
Product launched after being stalled for 2 years
Created a open communication with Brand Managers
Role & Timeline
Product Designer & Researcher
Feb 2022 - Mar 2023
Methods
User Research
Qualitative and Quantitive tests
High Fidelity Prototyping
TL:DR
After being stalled for over two years, The Brand Performance Dashboard project was revived and turned into one of the most impactful tools ever delivered by the Product team—praised by the Chief Brand Officer as “one of the best tools Product has produced.”
The dashboard gave Brand Managers a clear hierarchy of metrics, showing at a glance where a brand was excelling, where it needed attention, and how performance was trending over time. Instead of digging through multiple reports, they could now make faster, more confident decisions, compare results across brands, and take clear actions with their clients.
Most importantly, the project didn’t just deliver a product, it created a new level of collaboration between the Brand Management and Product teams, ensuring that the solution truly reflected user needs and set a precedent for future cross-team work.
Final Design
The Challenge
Pattern’s Brand Managers oversee up to 10 brands, each with more than 45 performance metrics. With so much data to parse, it was difficult to know which metrics mattered most and in what order to address them. As a result, critical insights were often buried, and Brand Managers spent hours digging through reports—slowing down client recommendations and reducing their impact.
The goal of The Brand Performance Dashboard was to change that: to provide a clear, high-level snapshot of how a brand was performing, while making it easy to drill down into the metrics that needed attention.
Defining the Problem
What Metrics Should We Track
We worked closely with Brand Managers to determine which metrics mattered most, how they should be prioritized, and the best way to calculate them. This collaboration not only clarified the order in which issues needed to be addressed and their connection to high-level performance goals, but also revealed gaps, showing us which metrics we already had reliable data for and where we needed to dig deeper to uncover additional insights.
List Of All 45+ Metrics
How Do You Display The Metrics
With over 45 metrics in play, the challenge was figuring out how to present information in a way that gave Brand Managers a quick, high-level overview while still allowing them to drill down into granular details to uncover the root of any issues. To solve this, we established a clear hierarchy for the page, surfacing the most critical signals first, then progressively revealing supporting metrics.
We worked closely with Brand Managers throughout this process, ensuring the layout reflected their priorities and decision-making flow. By grounding the design in their real-world needs, we created a structure that felt both intuitive and actionable.
User Interviews and Testing
Understanding the user was key to this project, there were many stake holders for this project and making sure everyone was heard and their ideas were understood was important for this product to be successful.
We found that for quantitative feedback Maze surveys would be the best and for qualitative feedback In Person zoom calls were the best way to collect data from our users.
Example Maze Test
The MVP
With the foundations of research, insights, and a clear hierarchy of metrics in place, I began designing the interface for Pattern’s performance dashboard. We prioritized surfacing the most critical metrics first, such as in-stock and availability, since Brand Managers consistently identified these as the top drivers of decision-making. This approach ensured they could spot urgent issues quickly, then drill down into supporting metrics for deeper context.
The design aimed to balance clarity and depth: giving Brand Managers confidence in their decisions without overwhelming them with unnecessary noise. By focusing on intuitive navigation, logical groupings, and actionable insights, the interface helped transform the project into a tool that empowered faster, more informed decisions.
V1.0
V2.0
Organizing The Data
Once we knew which metrics mattered most, the next challenge was figuring out how Brand Managers should see those metrics over time. There were two big design and data problems we needed to solve:
High-Level Metrics (Header KPIs)
At the very top of the dashboard, we displayed the most important KPIs that summarized a brand’s overall performance (for example, In-Stock or Buy Box). The challenge was deciding how to trend these metrics over time—such as showing the last 30 days—and whether it was more useful for Brand Managers to compare them side-by-side (e.g., In-Stock vs Buy Box) or to view each one individually as a standalone measure.Aggregated Metrics in the Table View
Below the header, Brand Managers needed the ability to select a time range (like the last quarter) and see a reliable summation of performance across that period. The design challenge was twofold:Data roll-up: how to calculate and display an aggregate number that accurately reflected the selected time range.
Clarity of organization: how to show those roll-ups in a way that clearly tied them back to the higher-level KPIs, so managers understood exactly what was driving overall performance.
By addressing both challenges, we created a dashboard that gave managers a clear, trustworthy view of performance over time while still allowing them to drill down and connect daily metrics back to strategic goals.
The Final Product
After sitting stalled for two years, Performance became a tool that transformed how Brand Managers worked. Instead of digging through countless reports, they could now see at a glance where a brand was excelling, where it needed attention, and how performance was trending over time. This clarity empowered them to make faster recommendations to clients, compare results across brands, and track whether their efforts were driving real improvement. Beyond the product itself, the project also broke down silos—bringing the Brand Management and Product teams together in a way that hadn’t existed before, and setting a new standard for collaboration.
Final results
Impact
Before Performance, Brand Managers spent hours combing through multiple reports to piece together insights. This slowed down decision-making, delayed client recommendations, and reduced confidence in the actions being taken.
With Performance, that process transformed:
Time savings: Reduced the time to identify key issues from hours of manual reporting to just minutes in the dashboard.
Decision clarity: Surfaced a clear hierarchy of metrics, giving managers confidence in what needed immediate attention versus what could wait.
Efficiency at scale: Allowed managers to quickly compare results across multiple brands, applying successful strategies from one portfolio to another.
Tracking improvement over time: Made historical data easily accessible, enabling side-by-side comparisons of current vs. past performance (e.g., quarter-over-quarter, year-over-year).
Collaboration: Strengthened alignment between the Brand Management and Product teams, breaking down silos and setting a new precedent for future projects.
In short, Performance turned an overwhelming sea of data into actionable insights, empowering Brand Managers to move faster, make smarter recommendations, and deliver greater impact for their clients.
Next Steps
Following launch, feedback highlighted a desire for more dynamic functionality. Brand Managers wanted the ability to simulate outcomes—for example, projecting the monetary impact of improving specific metrics. This would enable them to go back to their brands with clear, data-driven recommendations: “If we improve this metric by X, it will increase revenue by Y.”
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