We can stop guessing which words Google wants to see. Instead, we can use what language models already understand to build pages that rank. In this guide we explain a better way to write and structure content. We walk through why scraping the top 20 results and chasing word counts often leads to over-optimized pages. Then we show how to map related terms with a distance graph, pick reliable algorithm trigger words, and build internal linking that reflects how models group ideas. We use a real example to make this easy to follow.
Table of Contents
Why average-based tools can lead us astray
Many tools work by scraping the top 20 pages for a keyword. They average the words, counts, and phrases those pages use, then tell us to copy the average and add more. That sounds logical but it has a hidden problem.
Most of the pages that rank can be over-optimized. If we copy their averages, we end up over-optimizing too. We build pages that are bloated or stuffed with phrases that do not help the reader. We waste time on word count instead of structure and meaning.
We used to fix low performance by adding words or building more links. That still helps sometimes. But it should not be the first move. A better first step is to know which terms the language models treat as linked to our topic, and then structure our pages to match that map of relations.
What Content Maxima does for us
Content Maxima is not just a keyword tool. It is a marketing tool that uses many language models to show how terms connect. It finds which words are directly linked to a seed term and which are two hops away. It then shows how often those links appear across 63 major language models.
We get two main views in the tool. The matrix view shows the top related queries that are one hop away from the seed term. The analysis view builds a node and edge map. We can choose how many primary nodes and how many secondary nodes to pull back. That gives us a small network we can use to shape content and links.
Seed term, tier 1, and tier 2 explained
A seed term is the main topic we start with. For example, “tree service” is a seed term. Tier 1 nodes are the terms that language models show as directly adjacent to the seed term. Tier 2 nodes are terms that sit one step farther away. They are related to a tier 1 term rather than directly to the seed term.
Think of it like a family tree. The seed term is the parent. Tier 1 are the children. Tier 2 are the grandchildren. This map tells us which phrases should appear on the page and where links should point to mirror how models see the topic.
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How the matrix helps pick algorithm trigger words
The matrix shows the frequency with which a related term appears across the 63 models. If many models show the same related term near our seed term, that term is an algorithm trigger word. It is a strong signal for the topic.
The tool makers suggest a rule of thumb. If a related term appears in more than 10 out of 63 models, we should treat it as a trigger word. We can then use those trigger words across our headings, subheadings, and links, but always in a natural way. We do not stuff them or force them into content that does not flow.
Example: mapping the Tree Service category
We ran “tree service” as a seed term. The matrix returned the top 25 related search terms. Out of 63 models, 62 listed tree trimming and tree removal as immediately adjacent to tree service. Other terms like stump grinding, tree care, tree planting and tree risk assessment showed up frequently too.
Using this map, we see that tree removal, trimming, and stump grinding are not separate silos. They are services that live under the tree service category. That means we can link between these service pages freely. We should not treat each one as a fully separate silo that cannot link out. The language models show these are tied together, so our internal linking should match.
Why internal linking matters more than word count
We find that structure and linking matter more than the number of words on a page. We have ranked location pages with only headings and a few short lines of content. Those pages still outrank thin pages with 1500 words if the internal linking and page structure mirror the language model map.
Wikipedia climbed in search because of smart internal linking. It creates a network of pages that reinforce each other. We should aim for the same kind of clear network inside our site. Use a main service category page and link to supporting service pages. Each supporting page should link back and to related services. That is how we create context and association.
When to use Content Maxima and when other tools are fine
For location pages we rarely need to use complex NLP tools. Location pages often need simple structure and correct local signals. For product and service pages, and for content meant to persuade a buyer, Content Maxima shines.
Content Maxima does more than pick keywords. It helps us build marketing messages. The tool turns entity research into specific copy suggestions for buyer personas at different stages in the sales cycle. That is what makes it worth the time for product pages and services that require persuasive content.
How we run a quick Content Maxima workflow
- Pick the seed term you want to rank for.
- Decide how many tier 1 nodes you want to pull. We typically pick 15.
- Decide how many tier 2 nodes per tier 1 node. We often pick five, which gives 75 tier 2 terms and 15 tier 1 terms plus the seed term.
- Run the matrix to see the top one-hop related terms and how often each appears across the 63 models.
- Use the analysis view to generate the node and edge diagram. This shows the best internal linking plan.
- Choose algorithm trigger words. Aim for terms that appear in at least 10 of the 63 models. Pick the highest frequency terms first.
- Write headings and short sections that use those trigger words naturally. Add links between related pages according to the node map.
How we place trigger words without stuffing
We sprinkle trigger words into headings, lists, and short paragraphs. We do not force them where they do not belong. The goal is to signal the topic clearly while keeping content readable for humans.
For example, a page called “Tree Removal” can naturally include short H2s that mention stump grinding, risk assessment, and tree trimming. Each H2 can link to the relevant service page. The text can be brief but clear. That structure tells language models and search engines that all these services belong to the tree service category.
Common mistakes to avoid
- Relying only on averaged top 20 metrics. That pushes you toward over-optimization.
- Thinking word count equals better results. It can help sometimes, but it is not a substitute for structure and linking.
- Isolating services into separate silos when language models show they belong under one category. This weakens your internal link signals.
- Using trigger words in a mechanical way. Always write for humans first and then apply model-driven terms.
How this helps our marketing work
Content Maxima goes beyond keywords. It helps us map messages to buyers. The marketing side of the tool can turn the research into copy for different personas and for different moments in the sales funnel. That is where the tool pays for itself for product and service pages.
We can write a short top-of-funnel piece that names common problems, a middle-of-funnel piece that compares services, and a bottom-of-funnel page that explains why to call us now. The tool helps us pick terms that match each stage so search engines and buyers see the right message at the right time.
Simple steps to get started today
- Pick one service category you want to improve. Use a clear seed term.
- Run a matrix with 15 tier 1 nodes and five tier 2 nodes per tier 1 node.
- Pick the top trigger words that show up across many models.
- Build a main category page and short supporting pages. Link them according to the node map.
- Write short, clear headings and a few sentences per section that include the trigger words naturally.
- Test and watch rankings. If needed, tweak links and headings rather than piling on words.
Final thoughts
We do not have to chase word counts or copy averages from the current top pages. Instead, we can match how language models group ideas. We pick algorithm trigger words that appear across many models and then we build pages and links that reflect those relationships. For location pages keep things simple. For product and service pages use a tool that maps entities and also helps write messages for buyer personas. This method is faster, cleaner, and it makes the site structure support what search models already understand. That is how we get better results without bloated pages.
FAQ
Should we use Page Optimizer Pro to write copy for content pages?
Page Optimizer Pro can help, but it relies on averaged data from top results. For product or service pages it can be useful. For local location pages it is usually not needed. We prefer a tool that maps entity relationships rather than one that tells us to exceed averages.
What is a seed term and how do we pick one?
A seed term is the main topic or keyword we want to rank for. Choose a clear, common phrase that represents the service or product category. For example, use tree service as the seed term for a business that offers many tree-related services.
What are tier 1 and tier 2 nodes?
Tier 1 nodes are terms that are directly adjacent to the seed term in the language model map. Tier 2 nodes are terms that are one step removed and relate to a tier 1 term. Use both to build headings and links that mirror how models group the topic.
How many models does Content Maxima use to check term frequency?
Content Maxima checks frequency across 63 major language models. It shows how often a related term appears near the seed term in those models. We use that frequency to pick algorithm trigger words.
What is a good rule of thumb for picking trigger words?
A simple rule is to pick related terms that appear in more than 10 out of 63 models. Then prioritize terms that show up most often. Use them naturally in headings, link text, and short copy segments.
Will adding more words always help my page rank better?
No. Adding words can help sometimes, but it is not the main factor for many pages. Structure, internal linking, and using the right related terms often matter more than raw word count, especially for local and location pages.