Ten shops on the same street sell the same product. Nine of them write blog posts explaining what the product is. Anyone can write that post; the tenth shop writes it too, and so does an AI in four seconds.
The tenth shop also publishes, every quarter, the actual average price paid on that street, because it is the only one that tracks it. When anyone — a customer, a journalist, a machine — wants that figure, there is exactly one place to get it. They must name the source, because there is no alternative source.
That is not content marketing. That is a monopoly on a fact.
Why this works mechanically
Return to Chapter 1. The engine retrieves passages and prefers ones that answer a sub-query with specific, corroborated information. Now consider what happens with a genuinely proprietary figure:
- It cannot be sourced from a competitor, because they do not have it.
- It cannot be synthesised from the model's training, because it did not exist at training time.
- If the engine wants to use it, it has to cite you — or omit the point entirely.
Every other tactic in this book makes you more likely to be chosen among substitutes. This one removes the substitutes. It is the difference between being a good option and being the only option.
Commodity content competes. Proprietary data compels.
You already have the data
Most businesses believe they have no data worth publishing. Almost all of them are wrong. You are sitting on operational records that nobody outside your company has ever seen, and which answer questions people genuinely ask. Some patterns:
| If you are a… | You can publish… |
|---|---|
| Logistics / freight business | Average clearance times by port and month. Detention and demurrage rates by shipping line. Seasonal volume patterns. Typical cost breakdown for a standard shipment. |
| Professional services firm | How long a typical engagement takes. The three problems that account for most enquiries. Anonymised outcome benchmarks across your client base. |
| Local trade or contractor | Actual local price ranges for common jobs. Which faults you see most often in this region. Seasonal demand patterns. |
| Agency or consultancy | Aggregated, anonymised client results. Before-and-after benchmarks. Time-to-result distributions. |
| Retailer or e-commerce | What actually sells together. Return-rate patterns by category. Regional demand differences. |
How to publish it so it actually gets used
"Based on 3,928 import declarations, covering 10,400 containers, filed at Port Klang between January and December 2025." One sentence. It is what makes the number credible rather than a marketing claim, and it is what a careful writer needs before they will cite you.
Not in a chart image. Not in a downloadable PDF behind a form. A retriever cannot read either. Chart it as well, by all means — but the number must exist as text.
"Port Klang Customs Clearance Benchmark 2026". Something a person could cite in a sentence. Update the same URL each period rather than creating a new one — you compound authority instead of splitting it.
Quarterly or annually. A one-off is a curiosity; a series becomes the reference. The second edition is worth more than the first, and the fourth is worth more than the first three combined.
Trade press, LinkedIn, your association, the journalists covering your sector. Data with no distribution is a diary. This is also how Lever One gets fed — original data is the most reliable way to earn genuine, unpaid mentions.
- Publishing something nobody asked about. Interesting to you is not the test. Start from a question you are actually asked, then find the data that answers it.
- Locking it behind a form. Gated data is invisible data. If lead capture matters, publish the findings openly and gate the full dataset or the detailed methodology.
- Breaching confidence. Aggregate, anonymise, and check your contracts before publishing anything derived from client work. This is the one part of this chapter with legal exposure.
You will find vendor claims that original research achieves citation rates of 38–65% against 6–15% for standard blog posts. TIER C I cannot trace those figures to a published method and I would not repeat them. The mechanical argument above stands on its own without them, which is exactly why I have made it that way.
Ask yourself one question about any page you are about to publish: could a competent competitor write this page tomorrow? If yes, it is commodity content — publish it if it serves customers, but do not expect it to build a position. If no, and specifically if the reason is that you hold data they do not, you are building something that lasts.
Find one question you are asked constantly, pull the answer out of your own operational records, and publish it with the method stated. One number, honestly derived, is worth more than a year of blog posts.