Scoped Content Depth

Scoped Content Depth is a metric (scored 0-100) that measures the comprehensiveness of a webpage's coverage of a single, narrowly defined topic.

Unlike broad, superficial articles, content with high scoped depth provides a thorough and exhaustive exploration of its subject matter, anticipating and answering the full spectrum of related user questions. Search engines and generative models reward this type of content because it fully satisfies user intent for a specific query, establishing the page as an authoritative resource. This metric evaluates a page against an ideal, expert-level outline for its topic, quantifying how completely it covers the necessary sub-topics.

Calculation Methodology

This metric leverages an LLM to act as a subject matter expert, creating a benchmark against which the content is measured.

Core Topic Identification

Extract the primary subject of the page from its <title> and <h1> tags. This defines the scope of the analysis.

Expert Outline Generation

Use a powerful LLM (e.g., GPT-4) with a specific prompt to generate a "gold standard" outline for the identified topic. The prompt should instruct the LLM to act as an expert in the field and list the essential sub-topics, key questions, and related concepts that a comprehensive guide must cover. This generated list serves as the benchmark for coverage.

Content Coverage Analysis

For each item in the LLM-generated outline (e.g., each sub-topic or question), programmatically check for its presence and discussion within the webpage's content. This can be done using a combination of keyword matching and semantic similarity. Generate an embedding for each outline item and compare it against embeddings of content paragraphs or sections. A paragraph with high cosine similarity to an outline item is considered to cover that point.

Scoring

The final score is the percentage of the LLM-generated outline items that are adequately covered in the content. Score = (Ncovered / Ntotal) * 100, where Ncovered is the number of outline items found in the content and Ntotal is the total number of items in the generated outline.

Calculating The Scoped Content Depth Score

The calculation of the Scoped Content Depth Score is a multi-step process that involves analyzing the HTML structure of the page. Here is a simplified pseudo-code representation of how this score is calculated.

BEGIN
 FETCH and PARSE the webpage.
 IDENTIFY the core_topic from <title> and <h1>.
 USE LLM to GENERATE a "gold standard" outline of essential sub-topics for the core_topic.
 EXTRACT content from the webpage.
 
 INITIALIZE covered_items_count to 0.
 FOR EACH item IN the generated outline:
 CHECK if the item is discussed in the content using keyword matching or semantic similarity.
 IF covered, INCREMENT covered_items_count.
 
 CALCULATE final_score as (covered_items_count / total_outline_items) * 100.
 RETURN final_score.
END

Conclusion

Scoped Content Depth is a powerful metric for assessing the comprehensiveness of your content. By ensuring that your content is deep and focused, you can signal to AI systems that you are an authority on the topic, which can lead to better visibility and more traffic.