Learn what to do when your article gets low AI search mentions. Discover how adding secondary keywords and fixing internal links improves content visibility.
Question
Table of Contents
You’re reviewing an attribution report for your renewable energy campaign. The report shows:
“Solar Storage Basics”: 42 AI mentions
“Future of Renewable Energy”: 28 AI mentions
“Wind Power 101”: 12 AI mentions
Which action is most likely to improve performance for “Wind Power 101”?
A. Double the word count without adding new value.
B. Replace all mentions of “wind power” with the higher-ranking keyword “solar storage.”
C. Add secondary keywords into subheadings and link it back to the “Solar Storage Basics” pillar page.
D. Add more stock images without captions or context.
Answer
C. Add secondary keywords into subheadings and link it back to the “Solar Storage Basics” pillar page.
Explanation
When an attribution report shows one article lagging significantly behind the rest of your campaign, the most effective strategy is to deepen its contextual relevance and strengthen its internal links. Adding secondary keywords into subheadings directly addresses how artificial intelligence reads and categorizes web pages. AI language models heavily rely on headers to understand the structure and semantic depth of an article. By integrating natural, highly relevant secondary terms—such as specific wind turbine metrics, installation costs, or efficiency rates—you signal to the algorithm that the page provides a thorough, comprehensive answer rather than a shallow overview.
Linking the struggling article to a top-performing piece creates a massive structural advantage. In this scenario, “Solar Storage Basics” acts as a highly trusted, frequently cited hub within your renewable energy cluster. By directing an internal link from the wind article to this successful solar page, you weave the underperforming post into a proven semantic web. Search crawlers follow this connection, registering that the wind article belongs to the same high-quality, authoritative network that they already trust. This internal distribution of ranking power naturally lifts the visibility of the weaker page.
The alternative tactics fail because they ignore how machine learning evaluates digital quality. Simply doubling the word count without adding new factual value actively dilutes the information density of the page. AI algorithms prioritize precise, accurate answers; padding a post with empty text damages your credibility and directly violates core Experience, Expertise, Authoritativeness, and Trustworthiness (E-E-A-T) principles. Similarly, dropping stock images into a post without captions or descriptive alt text provides zero value to search bots. AI systems process text and code; they cannot derive meaning from an uncaptioned photograph.
Finally, replacing accurate terminology with a completely different, higher-ranking keyword destroys the fundamental purpose of the content. Swapping the phrase “wind power” for “solar storage” in an article actually written about wind energy breaks user intent completely. The text becomes factually incorrect and logically incoherent. Search engines instantly recognize this tactic as keyword stuffing and will heavily penalize the page for attempting to manipulate search results with deceptive phrasing. A strategic optimization always respects the original topic while making it easier for machines to read and index.