How we collect, anonymize, aggregate, and publish cross-brand franchise benchmark data — with full transparency on sample size, privacy protections, and statistical methods.
No individual brand's raw data is ever published. Only aggregate statistics (medians, means, quartiles) leave the system.
We require at least 5 brands in any segment before publishing non-beta benchmarks. This prevents de-anonymization.
Brands must explicitly opt in. Participation is voluntary and can be withdrawn at any time.
We publish exactly how percentiles are computed, what metrics are tracked, and how segments are defined.
Participating brands opt in through their platform settings. When a brand opts in, they choose which data categories to share (development, openings, operations, training, financial). The platform then reads the brand's existing operational data — leads, franchisees, audits, training assignments, and royalty reports — and computes per-brand metric values.
What we track across 5 categories:
Benchmarks are computed for each metric across multiple segment dimensions, so brands can compare against peers that are most similar to them.
Raw data never leaves the brand's own records. Only computed metric values are aggregated.
No brand name, identity, or contact information is attached to any benchmark value.
The minimum participant floor prevents reverse-engineering individual brand performance from small samples.
Brands can opt out at any time. Their data is excluded from future benchmark computations.
Financial metrics (average unit revenue) are only included if the brand explicitly shares them.
All benchmark computation runs server-side with service-role access — no user can see another brand's raw data.
Small sample sizes in early stages mean benchmarks are marked "Beta" and should be interpreted with caution.
Industry and region segments may have fewer participants than the overall benchmark, limiting granularity.
Metrics depend on the quality and completeness of each brand's data. Brands with incomplete data may skew results.
The benchmark is a point-in-time snapshot. Trends over time will be available once multiple reporting periods are collected.
Self-selection bias: brands that opt in may be more data-driven than the average franchise, potentially skewing benchmarks upward.
Initial methodology. Defines 5 categories, 16 metrics, 3 segment dimensions, minimum participant floor of 5, and percentile-based aggregation.
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