Benchmark by myCPE
Benchmark by myCPE is a firm-level analytics tool that lets accounting firms compare their CPE compliance performance, learning activity, and training investment against industry peers. I designed the complete product experience — from the benchmarking input flows to comparison dashboards, graphical insights, and strategic recommendation reports — creating a data-dense but scannable interface built for firm principals and HR managers.
Context & Challenge
As myCPE's B2B business grew, accounting firms began asking a natural question: how does our firm's CPE compliance compare to others in our size and region? Benchmark was conceived to answer that question with data. The design challenge was significant: the product needed to handle complex multi-dimensional comparisons (firm size, geography, specialty, CPE category) while remaining legible to an audience of senior professionals who are data-literate but not data scientists. Every chart, table, and insight had to be immediately interpretable — there was no room for ambiguous visualizations.
My Role & Scope
I owned the complete UX design for Benchmark — from the initial benchmarking input wizard that collects firm data, to the comparison dashboard, to the exportable PDF report layout. I worked closely with the data engineering team to map the data model to the UI requirements: which metrics were available, how aggregations worked, what anonymization rules applied to peer comparisons, and how recommendations were generated. I produced wireframes, high-fidelity prototypes, and a comprehensive data visualization style guide that the development team used to build the charts.
Process & Approach
I started by interviewing firm principals and HR managers at accounting firms to understand what performance data they currently track and what decisions they would make with better benchmarking data. This research revealed that the most critical metrics were CPE completion rates, credit hours per employee, compliance deadline proximity, and training spend per head. I then designed a benchmarking wizard that collects just enough firm data — size, specialty, region, fiscal year — to generate meaningful comparisons. The comparison dashboard was designed in three progressive layers: a summary scorecard at the top, detailed category breakdowns in the middle, and peer distribution charts at the bottom.
Design Showcase
The Benchmark interface centers on a three-panel dashboard: a firm scorecard comparing your metrics to the industry median, category-by-category breakdowns with horizontal bar charts showing peer distribution, and a recommendation panel that surfaces actionable insights. The benchmarking input wizard uses a clean step-by-step pattern to reduce cognitive load. Charts were designed to be immediately readable at a glance — I avoided pie charts entirely in favor of bar comparisons and dot plots that make distribution visible.
Business Analysis Artifacts
The business analysis work for Benchmark was some of the most complex I've done — because the product sits at the intersection of design, data science, and business logic. I wrote detailed specifications for each metric calculation: how CPE completion rate is computed, what counts as a completed credit, how peer cohorts are defined, and how the anonymization model works to protect individual firm data. The recommendation engine requirements were written as decision trees with clear if/then logic that the data team could implement directly. I also produced a data dictionary mapping every field in the UI to its source in the data warehouse.
Outcomes & Impact
Benchmark became a key differentiator in myCPE's enterprise sales process — giving the business development team a concrete, unique product to demonstrate to firm decision-makers. The ability to benchmark CPE performance against industry peers proved to be a compelling retention driver for existing firm accounts.
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Key Metrics Tracked
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Dashboard Layers (Score → Detail → Peers)
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Benchmarking Dimensions
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Data-Driven Recommendations
Key Learnings
Benchmark taught me that designing for data-heavy products requires a fundamentally different mindset than designing consumer products. The user's goal is insight, not delight — so every design decision has to serve clarity and trust. I learned to ruthlessly cut visual complexity: if a chart requires a legend to interpret, it probably needs to be redesigned. I also gained deep appreciation for the BA role in data products — without precise specifications for how metrics are calculated, the gap between the design and the engineering implementation would have been enormous.