What Is Commodity Content?

What commodity content actually is

Most teams recognise bland content when they see it, but they struggle to define it. In practice, commodity content is material that any reasonable competitor could publish without changing a word. It might rank for a while, yet it never becomes a piece people remember, quote, or link to willingly.

The key issue is that this type of content carries nothing that only your brand could know or say. It repeats public information, rearranges well known tips, and mirrors the same structure as every other search result. The tone might feel polished, yet the ideas remain generic.

Think about a blog post titled "What is email marketing?" that lists the same benefits and best practices as ten other pages on page one. If you swapped the logo for a rival’s, nobody would notice. That is classic commodity content, and it quietly weakens your positioning.

To draw a clear line, you can treat commodity articles as content that fails at least one of these tests:

• It does not include unique information that comes from your data, customers, or product.
• It does not show real first-hand experience, such as failures, edge cases, or tradeoffs.
• It does not express a clear stance that some experts might disagree with.
• It does not offer a practical asset, such as a template, calculator, or workflow.
• It does not name real examples from your market that readers can investigate.

Google’s people-first content guidance asks whether a page provides original information, reporting, research, or analysis, and whether it offers substantial value compared with other pages in search results. That is almost a word-for-word description of non-commodity content. If your page fails that bar, you probably have a commodity piece, even if the writing looks clean.

The test: could a competitor publish this unchanged?

There is a simple stress test you can run on any draft. Remove your logo, strip brand mentions, and ask an honest question: could a direct competitor publish this article unchanged and stay consistent with their positioning. If the answer is yes, you have written interchangeable material. When a piece passes the test, something inside it would feel wrong in a rival’s voice, because it relies on your data, your methods, or your lived experience.

Why teams keep writing it anyway

Teams rarely set out to write forgettable material. They slide into commodity content because it feels safe, fast, and easy to approve. Stakeholders ask for coverage of every keyword on a list, so writers patch together what already ranks. Legal teams push for neutral claims, which strips out bold opinions. Agencies chase predictable deliverables, not distinct ideas. Over time, the process rewards volume and compliance rather than originality, so the library fills with content that nobody inside the business would defend as special.

Why AI made commodity content the default

Commodity content existed long before large language models, yet generative tools changed the economics. When you can produce a passable draft in minutes, the temptation to flood every keyword grows strong. Many teams now treat AI as a vending machine for articles instead of a partner that shapes stronger thinking. The result is a wave of pieces that sound fluent yet carry no fresh insight.

Google’s AI optimization guidance explicitly tells publishers to create non-commodity content. It defines valuable material as pages that carry unique information, first-hand experience, or original analysis, rather than a restatement of what is already available elsewhere. That guidance does not attack AI itself. It attacks the habit of using any tool to remix the same surface level points.

Google also states that its focus is on the quality of content rather than how teams produce it. You can use ChatGPT, in-house writers, or a mix of both. What matters is whether the final piece helps searchers solve a problem in a way no other page already covers. Google’s spam policies even define scaled content abuse as producing many pages mainly to manipulate rankings rather than to help users, regardless of whether automation, humans, or a combination produced them.

To see how AI nudges teams toward sameness, look at a typical workflow:

• Someone pastes a keyword and prompt into a model and accepts the first outline.
• The model pulls from common web patterns, so headings mirror current search results.
• The draft repeats definitions, pros, cons, and steps that other pages already list.
• Editors focus on tone and length instead of injecting original material.

This cycle does not break because the output looks obviously wrong. It persists because the content sounds fine on a quick skim. Without a deliberate step that adds proprietary data, lived experience, or a sharp point of view, AI will naturally produce something that blends into the existing digital noise.

The cost of a passable draft fell to almost nothing

Before generative models, teams had to budget serious time and money for every article. That friction forced prioritisation. Once AI could produce a decent first draft from a short prompt, the marginal cost of another page dropped close to zero. Many marketers responded by scaling output instead of raising the bar. Cheap drafts are useful, yet they also make it dangerously easy to ship ten shallow pieces instead of one deep, strategic asset.

What Google says it is looking for instead

Google’s guidance on generative AI content and helpful content points in the same direction. The company wants pages that show real experience, original research, or distinctive analysis. Its AI optimisation guidance calls out non-commodity content as the goal, and its people-first criteria ask whether a page offers substantial value compared with other results. Google also notes that there is no special markup or separate optimisation channel for AI features such as AI Overviews and AI Mode, so the same quality bar applies everywhere.

Five sources of material nobody can copy

Escaping commodity content does not require genius level creativity. It requires building each major piece around material that competitors cannot easily reproduce. Every business already owns more of that than it realises. The challenge lies in surfacing it and weaving it into articles with intent.

In practice, most standout pieces draw from at least one of five sources. Stronger articles often combine several. Each source changes the nature of the page, shifting it from generic explanation to grounded, specific guidance. Readers feel that difference quickly, even if they cannot name why.

Here are the five most reliable wells of non-commodity material:

• Proprietary data from your product, surveys, or operations.
• First-hand experience from real projects and experiments.
• A clear point of view that you are willing to defend.
• A practical asset such as a tool, template, or calculator.
• Named, real-world examples instead of vague hypotheticals.

Consider a SaaS company writing about churn reduction. A commodity piece lists generic tactics like better onboarding and lifecycle emails. A non-commodity content approach might share anonymised churn curves from actual customers, describe a failed experiment, argue against a popular tactic, include a retention dashboard template, and reference specific brands that turned churn around. Google’s people-first guidance asks whether a page offers original research or analysis. These five sources give you concrete ways to meet that expectation.

Proprietary data you already hold

Most companies sit on a goldmine of data from their product, support tickets, or campaigns. That data, even when anonymised and aggregated, can power insights no competitor can match. You might analyse when trial users usually drop, which features correlate with retention, or what time of day support volume spikes. Turning those patterns into charts, benchmarks, or rules of thumb instantly lifts an article out of the commodity bucket. Readers cannot get those numbers from public search alone, so they treat your piece as a reference rather than just another explainer.

First-hand experience from real projects

Stories from real work carry weight that theory cannot match. When you describe a failed migration, a messy implementation, or a campaign that underperformed, you give readers something rare in digital marketing content: honest context. You can explain what you tried, what surprised you, and what you would change next time. That level of detail signals genuine experience, which aligns with Google’s emphasis on first-hand expertise in its quality frameworks. It also makes your advice more believable, because it comes attached to consequences, not just abstract claims.

A point of view you are willing to defend

Most commodity content avoids strong opinions, which makes it feel safe yet forgettable. A distinctive point of view, by contrast, draws a clear line in the sand. You might argue that brands should publish fewer, deeper guides, or that certain vanity metrics waste budget. When you explain why, backed by your data and experience, you create a piece that people discuss and share. Some readers will disagree, and that is healthy. What matters is that your stance reflects your strategy and helps the right audience self select.

A tool, a template or a calculator

Readers often want something they can use today, not just ideas to think about. Offering a spreadsheet template, checklist, or simple calculator inside an article transforms the page into a working resource. For a content lead, that might mean a briefing template that bakes in non-commodity elements, or a calculator that estimates the cost of rewriting thin pages. These assets require effort to design, yet competitors cannot easily clone them without looking derivative. They also give your sales or customer success teams something concrete to share in conversations.

Named examples instead of hypotheticals

Named examples turn abstract advice into vivid guidance. Instead of saying "a B2B SaaS company might segment its emails", you could reference how a known brand structures its lifecycle flows, using only public information. You can also highlight your own customers, with permission, and explain what worked for them. Citing real campaigns, landing pages, or site structures shows that you study the field closely. It also helps readers search for those examples and learn further, which deepens trust in your content as a reliable starting point.

The originality matrix: what to add and when it pays

Content leads often accept commodity content because they lack a simple way to judge how much originality a given piece deserves. Not every page can support a full research project. You need a framework that matches investment to potential impact. That is where an originality matrix helps. It maps sources of unique material against their cost and likely upside, so you can make explicit tradeoffs.

At a high level, you can think in three dimensions. First, how important is the topic to revenue or positioning. Second, how competitive is the search landscape. Third, how reusable the material will be across formats such as sales decks, webinars, and customer training. High scores on these axes justify heavier lifts, while low scores might only need a light touch of distinctiveness.

The following table sketches how the five sources of non-commodity material compare in typical situations.

This matrix is deliberately simple. Its value lies in forcing a conscious choice. For a high-intent keyword that maps directly to your product, you might combine proprietary data, a tool, and a firm stance. For a supporting definition page, you might rely on first-hand experience and one or two named examples. Over time, that discipline builds a library where your most strategic pages carry the densest concentration of non-commodity material, and even lighter pieces still avoid pure repetition.

Building non-commodity material into the brief

The easiest place to kill commodity content is not in editing. It is in the brief. Once a writer or AI model starts from a generic outline, they will usually produce a generic draft. To change the outcome, you must encode originality into the assignment itself. That means defining the unique contribution, specifying sources, and giving access to raw material before anyone writes a single line.

Many content teams treat briefs as keyword checklists and structural notes. That keeps production moving, yet it does nothing to raise the ceiling on quality. A stronger brief reads more like a research plan. It tells the writer which internal dashboards to pull, which customers to interview, and which internal experts to quote. It also flags where the piece should take a stance, and where it must stay neutral for legal or brand reasons.

To make this practical, you can add three mandatory elements to every major brief:

• A one line statement of the article’s unique contribution.
• A list of internal sources, such as data, people, or documents.
• A clear rule for when to cancel the piece if those sources prove thin.

When you run briefs through that filter, you discover which topics matter enough to warrant deep treatment. You also expose gaps where the business lacks experience or data, which can inform future projects. Over time, this habit shifts your library toward non-commodity content by design, not by accident.

Name the unique contribution before drafting

Every significant article should answer one sharp question: what will exist in this piece that no other page on the topic currently offers. You might decide to share a new framework, fresh benchmarks, or a contrarian take. Write that promise in a single sentence at the top of the brief. If you cannot, you are not ready to commission the work. This constraint feels strict, yet it prevents wasted cycles on topics where you have nothing distinct to say.

Give the model raw material, not just a topic

When you use AI to assist with drafting, the inputs matter more than the prompt wording. Feed the model outlines built from your proprietary data, customer quotes, and internal notes. Paste in anonymised support transcripts or research summaries, then ask it to structure and polish. In that role, the model acts like a tireless editor rather than a source of ideas. You still control the unique substance, which keeps the final article from collapsing into commodity content that mirrors the public web.

Kill the article when you have nothing to add

One of the most powerful moves a content lead can make is to cancel a piece that lacks a clear edge. If your research shows that ten strong guides already cover a topic, and you have no new data or experience, walk away. Redirect that effort toward pages where you can truly help readers. Saying no protects your brand from swelling with filler, and it signals to the team that originality matters more than hitting arbitrary publishing quotas.

Frequently Asked Questions

What is commodity content in simple terms?

Commodity content is material that any competitor could publish unchanged because it adds nothing unique. It usually rephrases existing search results. Readers learn nothing they could not find elsewhere in minutes.

How do I tell whether my article is commodity content?

Ask whether the piece includes information, experience, or tools that only your brand could provide. If a rival could swap in their logo without edits, you have a commodity article. Also check Google’s people-first questions about originality and substantial value.

Does commodity content ever make sense to publish?

Commodity content sometimes makes sense for low-stakes, purely informational pages, such as basic definitions for internal linking. Even then, you should keep it lean and avoid heavy promotion. For strategic topics tied to revenue, it rarely justifies the opportunity cost.

How much original material does one article need?

Every important article needs at least one strong source of originality, such as proprietary data, first-hand experience, or a practical template. High value pieces usually combine several. The higher the business impact and search competition, the more non-commodity material you should invest.

Can AI produce non-commodity content on its own?

AI on its own usually produces commodity content because it learns from existing public patterns. It can help structure, edit, and clarify, yet it cannot invent your proprietary data or lived experience. You get the best results when you feed models rich, brand-specific inputs and use them as drafting partners.