Shopify analytics for brands that measure profit, not just revenue

Native Shopify analytics are built around the order event. When an order lands, revenue is recognized, and the report is updated. That is fine for a topline view, but it hides the reality that a meaningful percentage of that revenue is going to come back through the returns portal over the following weeks. For a brand with a 20% return rate, the difference between reported revenue and settled revenue can be enormous.

Reversify solves this by treating the return as a first-class event. Every revenue and margin number in the platform is computed net of returns for the relevant cohort. When you look at a channel's performance in Reversify, you are looking at the version of that number your CFO would recognize.

Revenue rankings are the most common way brands look at their catalog, and one of the most misleading. A SKU that generates a million dollars a year in revenue but returns at 35% and carries a 40% gross margin is not a hero product; it is a working-capital problem. Reversify replaces the raw revenue ranking with a health score that combines velocity, return rate, margin after returns, and days on hand into a single view.

The result is a catalog view that actually reflects operating reality. Top-of-list SKUs are the ones you want to double down on; bottom-of-list SKUs are the ones your team should discuss on the next merchandising call.

Marketing teams typically evaluate acquisition cohorts on gross revenue in a given window. That works only if return behavior is uniform across cohorts, which it almost never is. Discount-code cohorts, paid-social cohorts, and specific creative cohorts frequently return at multiples of the site average, which can flip a "profitable" campaign into a loss once refunds settle.

Reversify computes cohort contribution net of returns, refund fees, and re-marketing spend, so the campaigns that look profitable in Shopify but lose money in reality are visible immediately.

Reversify is designed to be used by a merchandiser, an operator, or a founder — not only by a data team. Every metric has a plain-English description, every trend links back to the underlying orders, and the weekly written briefing summarizes what changed without requiring anyone to open a dashboard.

For teams with a data function, Reversify also exposes clean, normalized tables you can pipe into your warehouse. The point is not to lock the data behind a UI; it is to give every part of the organization the same, correct view of Shopify performance.