The 20% rule: Benchmarking organic revenue for Shopify stores

One of the most common questions we get during forecasting or agency audits is simple: "What percentage of my revenue should actually be coming from organic search?"

If you ask the broader industry, the answer is almost always: "It depends."

And if you are a generalist agency working with everyone from local dentists to global SaaS platforms, that is probably true. The data is too noisy to find a pattern.

However, at Blink, we have spent years looking exclusively at the P&Ls of a very specific type of business: high-complexity, large-catalogue Shopify stores.

When you narrow the field that drastically, the "it depends" falls away. You stop seeing random variables and start seeing a reliable pattern. We know exactly what "good" looks like, and we know that if you fit this specific profile, there is a clear benchmark you should be hitting.

The physics of the business

This concept of "business physics" is something we talk about a lot, and it needs a little explaining.

Most people approach benchmarking the wrong way. They look at the top-line category - "eCommerce" - and try to average out the performance of every online store in existence. Or they narrow it slightly to "Shopify stores," but still lump a single-product drop-shipper in with a multinational retailer.

That data is too broad to be useful.

The reason we drill down so deep - specifically looking at platform + catalogue size - is because SEO is ultimately an execution channel. You have to get stuck into the architecture of the website. Running a campaign on a headless Magento build is a completely different engineering challenge to running one on Shopify.

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Number of SKUs

Our specific sweet spot happens to be large-catalogue stores. And what we found is that when you filter for that level of granularity - high SKU count, on Shopify - the noise disappears. You stop guessing, and accurate benchmarks actually start to emerge.

To illustrate this, look at two completely different types of eCommerce business:

• The "Instagram" brand (e.g. Wild or Surreal)

These brands have low complexity (perhaps 10-20 SKUs). People don't typically go to Google and search for "refillable aluminium deodorant case in purple"; they buy the brand because they saw a great ad on social media. For these businesses, organic revenue might sit naturally at 5-10%, and that is perfectly healthy.

• The "digital department store" (e.g. The Conran Shop)

This represents high complexity (thousands of SKUs). If you sell huge inventories of furniture, lighting, or homeware, you are playing a different game. You aren't just relying on brand storytelling; you are relying on capturing existing demand for products like "Knoll tulip table" or "Anglepoise lamp".

If your business looks more like Conran than Wild - meaning you have a large catalogue and complex inventory - the rules are different.

Revenue mix benchmark

For a mature, large-catalogue Shopify store, our data shows the north star metric is consistently 20% to 25% of total revenue generated via organic search.

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• The lottery ticket effect

A store with 5,000 products has 5,000 potential entry points. If your site structure is built correctly, you aren't just ranking for broad terms like "lighting". You are ranking for thousands of specific, high-intent searches like "black industrial pendant light".

• Brand maturity

At the scale required to hold a large inventory, your branded search volume usually stabilizes the baseline traffic.

• Organic shopping

For large inventories, Google’s free organic shopping listings often contribute significantly to this pie. This is a silent revenue driver that is easily overlooked.

The data caveat

There is a significant catch here. You might be looking at your analytics dashboard right now, seeing 12%, and worrying that you are failing.

In reality, you might actually be hitting 22%, but your attribution is obscured.

In a privacy-first environment, "clean data" is rare. We covered this in detail in our article on the problem with tracking, but the short version is that if a customer rejects cookies on your consent banner, GA4 often cannot track where they came from. When they eventually buy, that sale is frequently dumped into the "Direct" bucket, stripping the credit away from organic.

We have seen instances where upgrading the data infrastructure - typically a mix of server-side tracking and better identity resolution - caused the organic revenue share to jump overnight. The SEO didn't get better; the data just got clearer.

Diagnosing the numbers

Assuming your data is clean, you can use the 20-25% range as a diagnostic tool:

• If you are under 15%

This often implies a structure problem. You are likely relying on generic top-level collections (e.g. just "sofas") and haven't built out the granular sub-categories required to capture the specific searches (e.g. "green velvet corner sofa").

• If you are over 40%

This often signals a scale problem. While it looks great for profitability on paper, it usually implies you are under-investing in paid media. You are highly efficient, but you are likely leaving growth on the table by not acquiring enough new customers at the top of the funnel.

For complex, high-SKU retailers, 20-25% indicates a healthy ecosystem where paid channels drive scale and organic protects your profit margin.

But before you fire your agency or double your budget, audit your tracking. You need to be sure you are solving a traffic problem, rather than just a data problem.