Revenue Per SKU: The Metric Every Commerce Team Should Be Tracking

Your catalog is an asset. Like any asset, some parts are working hard and some are dragging down returns. The brands that know which is which have a structural advantage over the ones flying blind.

Revenue per SKU (total revenue divided by active SKUs) is one of the most telling metrics in commerce, and most teams never look at it. They'll quote total revenue to the pound and catalog size to the SKU, but ask how those two numbers relate and you get silence.

Here's why that silence is expensive. A typical mid-market brand runs 8,000 active SKUs. In almost every catalog we've analysed, the distribution looks the same: the top 20% of SKUs drive 75 to 85% of revenue, the middle 30% roughly break even after content, storage, and syndication costs, and the bottom half loses money on every channel it's listed on.

What Revenue Per SKU Tells You

1. Catalog bloat. Every active SKU carries a cost even when it never sells: syndication fees, storage, content maintenance, someone's attention. If your catalog grew 40% last year and revenue grew 12%, you didn't expand. You diluted. Revenue per SKU catches this in one number.

2. Content gaps. Pull your bottom quartile and audit the listings. The pattern is depressingly consistent: two images where top performers have seven, descriptions under 50 words, missing size and material attributes, no channel-specific formatting. These aren't bad products. They're invisible products.

3. Channel mismatches. A product doing £40 per month on your own site and £3 on Amazon isn't underperforming. It's mislisted. Wrong category, weak backend keywords, or a title written for humans browsing, not Amazon search. Cut revenue per SKU by channel and the mismatches jump straight out.

Run the Numbers

The maths takes an afternoon:

Segment	Share of SKUs	Share of revenue	Action
Top performers	20%	75 to 85%	Protect: watch content drift, stock, buy box
Middle	30%	12 to 20%	Enrich: these respond fastest to better content
Long tail	50%	3 to 8%	Fix or cut: audit content first, delist second

The middle segment is where the money is. Top performers are already optimized; the long tail may be structurally weak. But the middle usually contains solid products held back by thin data, and content fixes there routinely lift listing conversion 20 to 40%.

Why Nobody Acts on It

The reason teams don't track this metric is that acting on it used to be brutal. Flag 2,400 underperforming SKUs and someone has to rewrite 2,400 listings. At 30 minutes each, that's 1,200 hours of work, so nobody starts.

An AI layer changes the economics. When your product data platform monitors performance continuously, it flags SKUs whose content quality lags their segment, rewrites titles and descriptions to each channel's standards, fills missing attributes from supplier data and imagery, and queues everything for human review. The 1,200-hour project becomes a week of approvals.

Getting Started

Pull revenue by SKU for the last 90 days. Rank them. Take the bottom 20% and compare their content (image count, attribute completeness, description depth) against your top 20%. The gap will not be subtle.

Then ask the only question that matters: is this a product problem or a data problem? Nine times out of ten, better product data is the fastest lever you have.