Benchmarks are calculated with explicit rules for observed versus modeled coverage, normalization, confidence, and freshness so users are not comparing unlike rows or over-reading directional context.
Observed rows are the direct benchmark anchor wherever stable cohort medians exist. Modeled rows are used to extend coverage carefully, especially on geo pages, but they remain directional unless confidence is high enough to support harder planning use.
| Point | Detail |
|---|---|
| Observed versus modeled rows | Observed plus high confidence is framed as a primary benchmark |
| Observed versus modeled rows | Modeled plus medium confidence is framed as a directional benchmark |
| Observed versus modeled rows | Low-confidence rows are context only and should not be treated as hard targets |
We normalize time windows, naming, metric definitions, and benchmark groupings so rows can be compared more safely across markets and page types. We also surface `sampleDepthLabel` and `lastUpdated` so users can judge whether a benchmark is deep, recent, and stable enough for planning.
| Point | Detail |
|---|---|
| Normalization, sample depth, and freshness | Currency normalization |
| Normalization, sample depth, and freshness | Metric-definition mapping |
| Normalization, sample depth, and freshness | Date-range consistency |
| Normalization, sample depth, and freshness | Taxonomy rollups for channels, industries, and conversion types |
| Normalization, sample depth, and freshness | Freshness labels and sample-depth framing on trust-aware pages |
Payment maturity, localization complexity, and fulfillment complexity are qualitative context signals. They help explain why a market may be attractive or operationally demanding, but they are not performance targets and should never be treated like attributed channel metrics.
| Point | Detail |
|---|---|
| Qualitative context cards | Payment maturity, localization complexity, and fulfillment complexity are qualitative context signals. They help explain why a market may be attractive or operationally demanding, but they are not performance targets and should never be treated like attributed channel metrics. |
How Benchmarketing labels observed versus modeled rows, sets primary versus directional benchmarks, and carries confidence, sample-depth, freshness, and context signals into public pages.
Support pages strengthen benchmark credibility and give users a trustworthy explanation of the data model.
These pages should connect core benchmark hubs, definitions, and comparison themes so no important page becomes orphaned.
They extend coverage where observed market-level rows are incomplete, but they stay explicitly labeled so users know they are directional rather than primary benchmarks.
They summarize benchmark coverage qualitatively. A deeper label suggests broader and more stable cohort support, while a directional label signals more caution.