Metric Hive resources focus on the data contracts behind marketing and commerce reporting: canonical entities, metric formulas, grain, date basis, currency behavior, and aggregation rules. Start with the glossaries, then use the diagnostic tools and source-backed comparisons to explain real reporting differences.
These glossaries define common marketing and ecommerce profit metrics with formulas, grain notes, and pitfalls. They are the most direct references for teams standardizing reports across ad platforms, storefronts, warehouses, and BI tools.
Definitions, formulas, and aggregation notes for paid media, web analytics, funnel, revenue, profit, and customer metrics.
Definitions, formulas, grains, and caveats for gross margin, contribution profit, returns, shipping, payment fees, inventory costs, CAC, and LTV.
Guides explain how governed metric definitions become reusable datasets, exports, dashboards, and operating views. The linked guide pages are part of the current resources buildout.
How a marketing semantic layer keeps entities, dimensions, metrics, filters, and aggregation rules consistent across tools.
How ecommerce teams define contribution margin from revenue, discounts, refunds, COGS, shipping, fulfillment, payment fees, and ad spend.
Planned. A practical guide to date basis, attribution windows, source identity, and when attribution outputs should be separated from finance-grade reporting.
These guides start with identifiers, definitions, time, currency, attribution, and report grain. They do not force unlike source facts into one total.
Use the interactive decision tree to generate a discrepancy checklist for revenue, orders, ROAS, and aggregation problems.
Match order and transaction IDs, then reconcile coverage, revenue components, dates, refunds, scope, and currency.
Separate platform-attributed purchase events from observed store orders and inspect event deduplication.
Use the calculator to compare attribution-dependent channel efficiency with observed business revenue over paid-media spend.
See why additive bases, ratios, reach, distinct counts, snapshots, and modeled metrics require different rollups.
Build a cost waterfall from sales through refunds, COGS, fulfillment, payment fees, and acquisition spend.
Define the Shopify order, refund, cost, fee, and marketing inputs needed for a repeatable margin model.
Metric comparisons explain different definitions and denominators. Vendor comparisons disclose Metric Hive's authorship, cite current primary sources, and state when the competitor is the better fit.
Compare channel-attributed efficiency with blended marketing efficiency, including denominator choices and aggregation caveats.
Compare Funnel's established marketing data hub model with Metric Hive's inspectable semantic contracts and usage-based pricing.
Compare Supermetrics connector automation with Metric Hive's governed metrics, semantic exports, and no feature-gated plans.
Compare Fivetran's pipeline-first ELT model with Metric Hive's marketing and commerce semantic datasets.
Compare turnkey ecommerce attribution and intelligence with inspectable source-to-canonical contracts.
Compare mature omnichannel consumer-brand analytics with an earlier semantic-contract platform.
Compare a focused commerce operating model with public report, grain, mapping, and aggregation contracts.
Compare two semantic-first approaches with different production maturity and infrastructure.
Choose by operating model, inspect the editorial method, and open the page closest to the buying decision.
The available calculators keep inputs in the browser, expose their formulas, and explain what the result cannot prove.
Calculate each ratio from an explicit revenue basis and spend denominator without averaging row-level ROAS.
Estimate a period or order cost waterfall from sales, adjustments, variable costs, and acquisition spend.
Model acquisition payback from explicit CAC, contribution profit, retention, and cohort timing assumptions.
Templates will focus on repeatable semantic contracts rather than one-off spreadsheets: field inventories, metric definitions, source mappings, and export readiness checks.
Planned. A structure for documenting formula, grain, date basis, owner, valid filters, and known exclusions.
Planned. A checklist for revenue, refund, cost, shipping, fulfillment, payment fee, and inventory inputs.
Planned. A review format for deciding whether a dataset is safe to expose to BI, spreadsheet, or downstream reporting tools.
These pages connect resource concepts to Metric Hive platform and solution surfaces that are being built alongside the public resource hub.
Inspect code-backed connector contracts for base entity, row grain, time key, canonical field mapping, aggregation, and export visibility.
Platform overview for governed entities, metrics, dimensions, filters, and semantic query behavior.
Platform overview for ingestion workflows that preserve canonical marketing and commerce data contracts.
Solution overview for commerce profit analysis built from modeled revenue, cost, order, product, and customer datasets.
Solution overview for governed marketing datasets that can support analytics, exports, and BI workflows.
Before a report, export, or dashboard becomes a shared business surface, its metric definitions should state the grain, date semantics, currency behavior, source assumptions, and aggregation behavior.