Trusted Profit Insights for 3,000+ QSR Locations

Our Approach

The head of financial data systems made a deliberate choice: he didn’t want to ask “How do we fix this?” He wanted to ask, “How do we make this excellent for our operators and team members?” Analytic Vizion came alongside as a guide, helping to turn a mandatory change into a chance to rethink both the data and the experience.

Because the client had close relationships with operators, they had already collected years of feedback—what profit reporting would look like if they could “wave a magic wand.” That list included clearer peer comparisons, more detail in certain cost categories, and less time spent questioning whether numbers were right.

On the technology side, the client was in the middle of a major shift in the tools and platforms they supported. Rather than bolt the new solution to old patterns, the team aligned this project with that broader technology direction. The goal was to create a model profit solution on the new stack, not an exception.

On the analytics experience side, Analytic Vizion emphasized people‑first design. They used Figma to prototype the dashboard so operators could see, touch, and react to the “art of the possible” before any heavy implementation. This allowed the team to validate whether the planned views, comparisons, and interactions would actually help operators make quick decisions that move the business forward.

The How

The team framed the work around one idea: recapturable profit, the margin operators could regain by performing like their peers.

Analytic Vizion and the client defined it practically:

• Profit remained calculated per store, but dashboards introduced precise peer comparisons.
• Operators could select custom comparison groups and percentiles, rather than default views.
• General costs gained deeper transparency through sub‑ledger‑style categories like repairs and maintenance, with side‑by‑side peer benchmarks.

With that target, the teams built a modern data pipeline. A new dbt job automatically flags missed labor reporting, the long‑standing failure mode that once triggered two weeks of manual cleanup.

Meanwhile, Figma prototypes evolved into a live dashboard showing.

• Current store‑level margin.
• Potential margin if matching peers on cost performance.
• Recapturable profit across food, labor, and general costs, including detailed trends like unusually high repairs.

Throughout the build, Analytic Vizion anchored every dashboard decision to clarity and trust. The team worked with key stakeholders to explain all calculation differences from the legacy PDFs, pausing timelines when necessary to ensure full confidence at launch. From the beginning, the team organized the work around a core idea: recapturable profit. The existing profit reporting already surfaced components like food cost, but it didn’t give operators a granular, fair way to understand what was realistically on the table.

Tools & Technology

• Enterprise Data Warehouse (EDW): Legacy warehouse feeding semi‑manual reporting; deprecation forced reinvention.
• dbt job: Automated detection of missed labor reporting, replacing roughly two weeks of manual estimation.
• PDF reports: Static legacy profit reports, replaced by dynamic dashboards.
• Modern profit dashboard: Interactive view of margin and recapturable profit with custom peer groups and detailed cost structures.
• Figma: Prototype tool enabling early operator feedback and reducing rework.

The Results

• The client replaced a fragile, manual pipeline with an automated platform that delivers trusted, real‑time profit insights.
• Known failure modes, like missed labor reporting, are flagged instantly instead of discovered weeks later.
• Operators see a fair, transparent view of their performance and how to close gaps on food, labor, and general costs.
• The refined dashboard—described by leaders as “swanky”—turns data into confident decisions.
  
  What began as an EDW sunset became a catalyst for sustained excellence: faster insight for operators, less firefighting for data teams, and a framework for future financial analytics built on rigor, clarity, and trust.