In early IR, terms could receive unbounded weights before normalization. Modern engines normalize weights (BM25, IDF scaling) for fairness.
Are you confused why your longest, most detailed article doesn’t always rank better than a shorter one? I have seen this happen countless times, and it comes down to a core concept in search engine math.
The problem often lies in Unnormalized Term Weights, which is a technical way of saying “long documents can trick the ranking system.”
Today, I will explain why your page length might be hurting you and give you actionable tips to ensure your key content gets the ranking weight it deserves.
Let us make sure Google reads the right words and gives them the right importance.
What is Unnormalized Term Weights? Length Bias
What is Unnormalized Term Weights? describes the raw importance of words in a document before adjusting for the length of that document.
Without normalization, a long page is automatically favored over a short one because it simply contains a word more times, even if that word is less relevant.
Search engines use normalization techniques (like cosine similarity) to prevent this “length bias” so that a short, highly-focused page can outrank a long, rambling one.
CMS Impact: Controlling the Word Power
The solution to Unnormalized Term Weights is simple: prioritize the clarity and location of your core terms, not just the word count.
We use our CMS features to ensure our most important terms are positioned for maximum impact and proper weighting.
WordPress
WordPress users should use their SEO plugins to check the Term Frequency (TF) of core keywords, but then focus on where they appear.
I make sure my primary keyword is in the Title Tag and the H1 Heading because those locations receive higher weights.
This positional weighting ensures my page focuses the “word power” right at the top.
Shopify
Shopify product pages often have short descriptions, so we must make every word in that limited space count.
I ensure the key differentiating words are in the product title and the first 100 words of the description, which carry heavier weight.
This avoids the trap of low term frequency by giving the few existing terms maximum relevance authority.
Wix
Wix users can easily fall into the length bias by adding lots of low-value, repetitive text to pad out their pages.
I tell my clients to use bolding or bullet points on their most important keywords to draw the attention of the crawling system.
This visual emphasis often corresponds to an algorithmic emphasis on those terms.
Webflow
Webflow’s custom CMS allows me to control Field Lengths for things like meta descriptions and short summaries.
I ensure these short, highly focused fields contain the most critical term for the page’s relevance score.
The short field length means the term frequency in that zone is high, which boosts the term’s overall weight.
Custom CMS
With a custom CMS, I implement Inverse Document Frequency (IDF) checks against my entire content inventory.
IDF helps me find which words are truly unique to a document, and I prioritize using those words in the page’s content.
This ensures my pages are not just long, but are long and rich with rare, highly valuable terms.
Industry Relevance: Avoiding Length Tricks
Every industry must ensure its core terms are properly weighted so that the search algorithm is not fooled by content length.
The key is to create semantically focused content, regardless of the final word count.
Ecommerce
Ecommerce product pages must be optimized to ensure product names and features are highly weighted against long customer reviews.
I make sure the single most relevant term has a high frequency in the product title and short description, before the long customer review section.
This prioritizes the core term over the length of the User Generated Content (UGC).
Local Businesses
Local businesses need to ensure the location term is the highest weighted term on the service page.
I repeat the city or service area name in the first paragraph and in the headings, even if the general service description is very long.
This ensures that the location-specific unnormalized term weight is high enough to pass normalization checks.
SaaS (Software as a Service)
SaaS documentation often suffers from length bias because complex tutorials are very long.
I make sure the problem/solution statement at the top of the article is highly optimized with the key feature terms.
This short, highly focused paragraph receives a higher positional weight than the long, step-by-step instructions that follow.
Blogs
Bloggers must ensure the core topic of the article remains the most important weighted term, regardless of the article’s final word count.
I check that the keyword density of the main topic is highest in the first and last section of the article, where search engines assign extra relevance.
This avoids diluting the term’s power with large blocks of explanatory text in the middle.
FAQ: Unnormalized Term Weights
Q: What is “normalization” in this context?
A: Normalization is a mathematical process search engines use to remove the advantage a very long document has over a short one.
I see it as dividing the word’s count by the page’s total length to determine the true importance of the word.
Q: Will this problem get worse with AI-generated content?
A: Yes, it might; AI often produces long, generic content that has a high raw word count but a low, unnormalized term weight for the specific topic.
I focus on editing AI content to reduce filler words and increase the density of highly specific, informative terms.
Q: Should I delete my long articles to fix this?
A: No, absolutely not; long content is great if it is high-quality and comprehensive.
I just ensure my long articles are structurally perfect, using clear headings to organize the content, so the most important terms are highly visible.
Q: What is a highly-weighted location for a term?
A: A highly-weighted location is the HTML Title Tag, the H1 heading, and the URL slug.
I ensure the target term appears in all three of these areas to receive the highest possible weight from the search algorithm.
