Score How Much Unique Value
Your Content Adds Over Competitors

Information gain measures how much new, unique, or deeper knowledge your content provides compared to what already exists for a given topic. AI engines prefer content that covers the topic comprehensively and adds angles, data points, or perspectives that other cited sources do not cover. Content with low information gain — covering the same subtopics and reaching the same conclusions as top competitors — is significantly less likely to be cited regardless of how well it is written.

Traditional content gap analysis identifies keywords competitors rank for that you do not. Information gain analysis looks inside individual pieces of content to identify the specific subtopics, entities, data points, and questions that competing pages cover within a single topic that your content does not address. Instead of finding new pages to create, it finds specific additions to existing pages that will move the citation needle.

Pages scoring above 65 are cited at approximately 4x the rate of pages scoring below 35. In highly competitive niches like digital marketing and finance, you typically need above 75 for consistent AI citations. The scorer benchmarks your score against actual AI-cited content in your specific niche, not just top Google results.

The fastest improvements come from: Missing subtopics — add a dedicated section for each major subtopic that top competitors cover but your content does not. Missing entities — add the key brands, tools, and organisations central to your topic. Missing data points — add specific statistics, benchmarks, and research citations. The scorer before and after each addition shows exactly how much each improvement contributes.

No. You provide your article URL and the tool fetches your live page content automatically. This means the analysis reflects exactly what AI engine crawlers see when they access your page, including any dynamic content rendered by your CMS. Content must be publicly accessible.

Yes. Fully available on the free plan with 15 runs per month. Every run includes the complete 0–100 information gain score, full subtopic gap list, entity gap analysis, unique angle detection, and AI citation probability score.

Content quality scores typically measure writing quality, readability, grammar, and structural completeness. Information gain score measures uniqueness of coverage — how much new, distinct knowledge your content adds that top-competing pages do not already contain. A perfectly written article that covers exactly the same points as every competitor scores low on information gain regardless of writing quality. AI engines weight information gain more heavily than writing quality when selecting citation sources.

Yes — and this is the most efficient approach. The Information Gain Scorer identifies the specific missing subtopics, entities, and data points ranked by citation impact. Adding targeted sections covering the highest-gap subtopics (typically 200 to 400 words per section) is faster and higher-impact than rewriting existing content. Most pages move from a score of 35 to 65+ by adding 3 to 5 targeted sections without changing any existing text.

Original research with novel data — surveys, proprietary analyses, unique datasets — consistently produces the highest information gain scores because the data is by definition not available on competing pages. However, original research must still cover the foundational subtopics that AI engines expect for comprehensive coverage of the topic. The highest-scoring pages combine original data (providing unique information gain) with thorough coverage of established subtopics (satisfying AI engine completeness expectations).

For newly published pages with limited content, the tool scores the existing content and provides a gap analysis against the full competitor set — showing the complete list of subtopics and entities to add to reach citation-eligible scores. For thin pages with under 500 words, the tool provides a minimum viable content specification showing the exact word count and section structure needed to meet the baseline information gain threshold for the target query.