Benchmark Methodology
Our industry benchmarks are the backbone of every benchmark page and the Profit Reality Score. This page explains where the numbers come from, how they are calculated, what the ranges mean, and where the limits are. The goal is simple: you should be able to trust a figure enough to act on it, and know exactly when to adjust for your own store.
In one sentence
Each metric is published as a range (low – high) with an average, compiled as editorial estimates from public industry research, company filings, and practitioner experience — not a single store’s data point, and not a formal citation. Ranges exist because profitability varies widely within every vertical.
Public industry research (orientation, not source)
We follow reporting from NRF, eMarketer, Statista, and vertical trade press to stay oriented on where margins, refund rates, and ad costs typically land. But our numbers are editorial estimates — they are not pulled from any of these reports, and no figure here is a citation to a specific published study.
Public company filings (reference, not source)
We reference net margin, COGS, and ad-spend ranges disclosed in public retail SEC filings and investor reports to sanity-check our estimates. These filings describe specific public companies, not the broader vertical, so we use them as a compass rather than a data source.
Practitioner experience
Years of working with Shopify merchants inform the typical ranges for refund rates, shipping costs, and acquisition spend by vertical. This is editorial judgment from experience, not a formal survey with a sample size.
No user data, ever
Every benchmark range is an editorial estimate. We do not aggregate user CSV data — uploads are parsed in your browser only and never reach a server (see our privacy boundary, ADR-002). Our ranges cannot and do not claim to be derived from merchant data.
How each metric is defined
Every industry page reports the same ten metrics. Here is exactly what each one measures.
| Metric | Unit | Definition |
|---|---|---|
| Gross margin | % of revenue | (Revenue − Cost of goods sold) / Revenue. The margin left after the direct cost of the product itself. |
| Net margin | % of revenue | Profit after all operating costs (COGS, ads, shipping, payment fees, refunds) as a share of revenue. The number you actually keep. |
| COGS | % of revenue | Cost of goods sold as a share of revenue. Product, materials, and inbound fulfillment cost. |
| Ad cost | % of revenue | Paid advertising spend as a share of revenue. A proxy for how acquisition-heavy a vertical is. |
| Shipping | % of revenue | Outbound shipping and fulfillment cost as a share of revenue. |
| Refund rate | % of orders | Refunded orders divided by total orders. A leading indicator of sizing, quality, and expectation gaps. |
| CAC | USD | Customer acquisition cost — blended marketing spend divided by new customers acquired. |
| AOV | USD | Average order value — total revenue divided by number of orders. |
| ROAS | ratio (x) | Return on ad spend — revenue attributed to ads divided by ad spend. |
| CLV | USD | Customer lifetime value — projected net revenue from a customer over their relationship with the store. |
Why ranges, not single numbers
A jewelry store running on thin ad margins and a vertically integrated brand with a loyal audience can sit 20 percentage points apart on net margin. Publishing one “average” hides that spread. So every figure is shown as low – high with the average marked alongside. Treat the average as the center of mass of the vertical, and the range as the realistic band your store can land in depending on execution.
Refresh cadence
Benchmarks are reviewed quarterly and re-estimated when public reporting shows materially different ranges. Structural shifts (e.g. ad-cost spikes in a vertical) trigger an off-cycle update.
The roadmap to measured data
Today these are curated ranges compiled from public industry research, company filings, and Shopify’s published merchant benchmarks. As public data improves, we update the ranges — without ever collecting or storing raw order data (ADR-002 permanent boundary).
Limitations & how to use these numbers
- Ranges are indicative, not audited. Use them to set expectations and spot outliers, not as a guarantee.
- Your store’s result depends on business model, geography, pricing power, and scale — compare to the band, not just the average.
- Profit Reality Score compares your CSV against these ranges; a score reflects relative position, not an absolute grade.
- Verticals with thin public data carry wider ranges and should be read with more caution.
Questions about a specific figure or vertical? Get in touch and we’ll walk through the sources behind it.