When people talk about GEO, the conversation quickly shifts to format: add a short answer under the heading, put together an FAQ, mention entities more often. That is useful, but secondary. First, a search engine or language model has to determine whether it can rely on a number it finds on a page at all. If a metric has no source, date, or explanation of the method, clean formatting does not make it any more reliable.
The problem is especially clear in topics where the same value changes depending on the product version, provider, or platform. Take RTP, the theoretical percentage of money returned to players in a gambling game, for example. It is not enough to write “RTP is 96%”: a game may have several certified configurations, and the operator chooses a specific one. For an SEO specialist, this case is interesting not because of the gambling angle, but because of how a verifiable answer is constructed.
Why one accurate number is not enough
People often judge a text by how confident the author sounds. An algorithm has to look for more formal points of reference: who published the information, where it came from, what it refers to, and when it was verified. The phrase “the market average is 96%” sounds complete, but leaves several questions unanswered.
- What exactly was averaged: games, operators, studios, or individual configurations?
- What period was covered by the sample?
- Were duplicates and incomplete records excluded?
- Can the primary source be opened, or at least the data collection procedure be described?
- Does the figure refer to the current version of the product?
Without these clarifications, a number is fine for illustration, but poorly suited to citation. A model can repeat it, but it has no reason to prefer this page over another. In competitive search results, the winner is not necessarily the longest piece, but the one where a claim can be checked quickly.
Five details that turn a number into a verifiable fact
1. Object of observation
A metric needs a clearly identified subject: not just “game,” but its name, provider, and, if relevant, specific configuration. For a rate, this might be the region and period it applies to; for a study, the sample; for a software product, the build version. The less the reader has to infer, the lower the risk of an incorrect generalization.
2. Primary or closest available source
The best option is an official document, an operator interface, a technical specification, or an in-house measurement with a described procedure. A link to the tenth article in a chain of articles repeating one another does not create an evidentiary trail. If the primary source is inaccessible, it is worth stating honestly where the value was read and what the limitations of that verification are.
3. Verification date
The article’s publication date and the date the data was checked are not the same thing. An article may have been updated today while its table is two years old. For metrics that change, it is useful to have a separate “verified” line next to the data. This also helps the editor: a few months later, it is clear which section needs to be checked again.
4. Method
The methodology does not have to be an academic paper. It is enough to explain where the records come from, how discrepancies are resolved, and what counts as a confirmed value. RTP Index has a separate page for this: the RTP Index methodology lists the sources, confidence levels, and procedure for handling conflicting data. This section is useful not only to users. It gives search engines context, without which an individual table looks like a collection of numbers from who knows where.
5. Scope
A good answer explains not only what is known, but also what cannot be concluded from the data. Theoretical RTP does not promise a particular outcome over a short gaming session. An average across a sample does not describe every item in it. A correlation between two metrics does not prove a causal relationship. These caveats do not weaken the text—they show that the author understands the limits of their own conclusions.
What a citable passage looks like
For GEO, what matters is not just the length of an article, but the self-contained nature of its sections. A paragraph should make sense even if an algorithm extracts it without the three screens that came before it. A practical formula looks like this:
Answer → evidence → date → limitation. Start with a direct conclusion, then name the source or method, specify when it was checked, and state in one sentence where the conclusion no longer applies.
For example: “The stated RTP value for the same game may differ across operators because the provider releases several certified configurations. The comparison was based on values disclosed in operator interfaces as of the verification date. This is a theoretical long-term metric and does not predict the outcome of an individual session.”
This paragraph contains no promotional call to action, but it does provide a complete answer. It can be quoted without losing its meaning. If a table of specific examples is placed nearby, the page becomes even easier for machines to extract information from.
Discrepancies are more useful than a perfectly uniform table
Editors often want to standardize the data: keep one value, round it, and remove disputed entries. For GEO, that is not always the best solution. A discrepancy may itself be the key fact. It shows that the author compared sources rather than copying a reference guide.
On the page about comparing the same game across different casinos RTP Index does not hide differences between operators, but presents them in a separate analytical breakdown. It gives the maximum and median differences, the size of the dataset, and the date of the last check. Readers can immediately see what was compared and why a single value for the game could be misleading.
This approach can be applied to almost any industry. A price comparison can show variation by region. A legal article can show differences between the general rule and the practice of a specific agency. A SaaS test can show differences between plans and actual limits. Hiding variation is easier, but explaining it is more useful.
What to put on a page to make it easier to trust
There is no need to turn every piece into a twenty-page report. For most informational publications, a few standard elements are enough.
| Element | What it explains | Where to place it |
|---|---|---|
| Verification date | How current the information is | Above the table or next to the conclusion |
| Source | Where the claim came from | In the same paragraph or a footnote |
| Brief methodology | How the result was obtained | Before the data or on a separate page |
| Sample size | How broad a conclusion can be drawn | In the table heading or study summary |
| Limitation | What the data does not prove | Immediately after the conclusion |
If the methodology is repeated across many pieces, it makes more sense to create one detailed document and link to it from individual listings. But the date and object of observation still need to remain on the page itself: users should not have to navigate around the site to understand the basic conditions.
How to check an article before publication
Before submitting a page for indexing, it is useful to look at the article not as its author, but as someone who has just come across one random paragraph from it.
- Find all the numbers. For each one, ask: where did it come from, and what date does it refer to?
- Check the names of entities.The company, product, version, and region should be named consistently throughout the text.
- Define the term the first time it appears. After that, you can use the abbreviation.
- Separate fact from interpretation. First, the observation; then, the author’s conclusion.
- Include one limitation. If there isn’t one, the text probably claims more than the data supports.
- Open the links. They should lead to a specific methodology, document, or dataset, not to a homepage added “for authority.”
- Read the image captions. A screenshot rarely adds meaning without an explanation.
What does not work
A link to “research data” without naming the study. The source is technically mentioned, but the claim cannot actually be checked.
A template’s update date instead of the data date. An automatic “updated today” label creates a false impression of freshness if the figures were not reviewed.
A large table with no sample description. A hundred rows may look convincing, but it is unclear why those particular entries made it into the dataset.
The same conclusion for different conditions. If a metric depends on configuration, plan, or region, any average should be explicitly identified as an average.
A screenshot instead of accessible text. An image confirms that the author saw the interface, but the key conclusion should also be given in text. That way, both search engine crawlers and screen reader users can access it.
Conclusion
GEO starts before FAQ markup and the choice of wording. First, make the claim verifiable: identify the subject, show the source, specify the date, explain the method, and honestly limit the conclusion. Then an individual paragraph works as a self-contained unit of knowledge, rather than as a promotional phrase taken out of a long text.
The RTP case is illuminating precisely because there is no simple answer. The same game may exist in several configurations, the data changes, and the theoretical metric is easy to misinterpret. A site that documents these complexities instead of hiding them gives search engines more reason to quote it accurately.
