Commercial banking

A summary of what distinguishes high-visibility domains in this industry, based on signal correlation patterns and LLM recommendation data.

Focus on signals with the strongest Rank Influence — these have the most reliable relationship with AI recommendations for this industry.

This industry has 11 identified audience personas derived from LLM query patterns. Each represents a distinct user intent with its own signal correlations and top domains.

Signals and top-ranked domains can differ significantly between personas — use the links below to jump directly to a persona's analysis.

Spearman rank correlations (ρ) between each measurable signal and the LLM recommendation score across all 87 domains in this industry.

Higher absolute ρ values (closer to ±1.0) indicate stronger relationships. Positive ρ means more of this signal correlates with better AI visibility. Rank Influence summarizes the practical strength — prioritize Dominant and Strong signals when optimizing.

How often AI models recommended each domain when answering Commercial banking queries across all personas. Each LLM run represents one query — the left chart shows raw recommendation counts, the right shows what percentage of queries included that domain.

275 total LLM query runs across all personas for this industry. A domain appearing in 50% of queries means AI models recommended it in half of all runs — regardless of list position.

A cross-signal snapshot for the 25 most LLM-recommended domains, showing values across all tracked signal categories — from backlinks and crawl data to Reddit, Wikipedia, and search engine visibility.

Scan across rows to find which signal combinations appear most consistently in high-visibility domains. Darker cells indicate stronger values relative to this industry. Columns are color-grouped by category.

Domains appearing most frequently in Google search results for Commercial banking queries — counts are filtered to only the search queries associated with this industry.

Compare with the LLM recommendations chart above. Gaps between the two — domains that rank high in search but low in AI, or vice versa — reveal strategic opportunities or risks depending on your channel focus.

Domains ranked by backlink authority signals derived from the Common Crawl web graph: PageRank, total backlinks, link authority (AUC), and harmonic centrality.

Backlink authority is one of the strongest predictors of LLM visibility in most industries. Domains with high PageRank but a low LLM score may be underrepresented in AI recommendations despite their web authority — a potential gap worth investigating.

This persona represents a distinct audience segment identified from LLM query patterns within this industry. The description reflects how AI models characterize users making queries in this segment.

Domains recommended for this persona are scored based on how frequently they appear in persona-specific queries — which may differ from the industry-wide rankings above.

Signal correlations calculated only for domains recommended in the context of this persona's queries. These may differ from the industry-wide pattern above.

Differences from the overall industry table reveal which signals matter specifically for reaching this audience — useful for targeting content strategy and acquisition priorities for this segment.

How often AI recommended each domain when answering neutral queries within Commercial banking. Each persona has 25 query runs — count is raw appearances, percentage is out of those runs.

Compare with the industry-wide charts above to see which domains over- or under-index for this specific audience segment.

Signal matrix for the top 25 LLM-recommended domains filtered to this persona's query context.

Look for patterns in which signal combinations characterize domains that resonate specifically with this audience — these may differ from industry-wide norms and point to persona-specific ranking drivers.

Domains in Google search results for neutral queries within Commercial banking. Counts reflect only the Google search results for keywords mapped to this persona — filtered to domains that appear in this industry.

Note: the number of results naturally varies by persona because different query intents return different domains. A persona with fewer results here is not a data gap — it reflects that those specific queries surface a narrower or different set of sites.

Backlink authority rankings for top domains within this persona segment.

Authority signals reflect broader domain strength that generally carries across personas. High-authority domains appearing only in some personas may indicate specialized trust or content relevance that isn't fully general.

This persona represents a distinct audience segment identified from LLM query patterns within this industry. The description reflects how AI models characterize users making queries in this segment.

Domains recommended for this persona are scored based on how frequently they appear in persona-specific queries — which may differ from the industry-wide rankings above.

Signal correlations calculated only for domains recommended in the context of this persona's queries. These may differ from the industry-wide pattern above.

Differences from the overall industry table reveal which signals matter specifically for reaching this audience — useful for targeting content strategy and acquisition priorities for this segment.

How often AI recommended each domain when answering Startup Finance Lead queries within Commercial banking. Each persona has 25 query runs — count is raw appearances, percentage is out of those runs.

Compare with the industry-wide charts above to see which domains over- or under-index for this specific audience segment.

Signal matrix for the top 25 LLM-recommended domains filtered to this persona's query context.

Look for patterns in which signal combinations characterize domains that resonate specifically with this audience — these may differ from industry-wide norms and point to persona-specific ranking drivers.

Domains in Google search results for Startup Finance Lead queries within Commercial banking. Counts reflect only the Google search results for keywords mapped to this persona — filtered to domains that appear in this industry.

Note: the number of results naturally varies by persona because different query intents return different domains. A persona with fewer results here is not a data gap — it reflects that those specific queries surface a narrower or different set of sites.

Backlink authority rankings for top domains within this persona segment.

Authority signals reflect broader domain strength that generally carries across personas. High-authority domains appearing only in some personas may indicate specialized trust or content relevance that isn't fully general.

This persona represents a distinct audience segment identified from LLM query patterns within this industry. The description reflects how AI models characterize users making queries in this segment.

Domains recommended for this persona are scored based on how frequently they appear in persona-specific queries — which may differ from the industry-wide rankings above.

Signal correlations calculated only for domains recommended in the context of this persona's queries. These may differ from the industry-wide pattern above.

Differences from the overall industry table reveal which signals matter specifically for reaching this audience — useful for targeting content strategy and acquisition priorities for this segment.

How often AI recommended each domain when answering Middle-market treasurer queries within Commercial banking. Each persona has 25 query runs — count is raw appearances, percentage is out of those runs.

Compare with the industry-wide charts above to see which domains over- or under-index for this specific audience segment.

Signal matrix for the top 25 LLM-recommended domains filtered to this persona's query context.

Look for patterns in which signal combinations characterize domains that resonate specifically with this audience — these may differ from industry-wide norms and point to persona-specific ranking drivers.

Domains in Google search results for Middle-market treasurer queries within Commercial banking. Counts reflect only the Google search results for keywords mapped to this persona — filtered to domains that appear in this industry.

Note: the number of results naturally varies by persona because different query intents return different domains. A persona with fewer results here is not a data gap — it reflects that those specific queries surface a narrower or different set of sites.

Backlink authority rankings for top domains within this persona segment.

Authority signals reflect broader domain strength that generally carries across personas. High-authority domains appearing only in some personas may indicate specialized trust or content relevance that isn't fully general.

This persona represents a distinct audience segment identified from LLM query patterns within this industry. The description reflects how AI models characterize users making queries in this segment.

Domains recommended for this persona are scored based on how frequently they appear in persona-specific queries — which may differ from the industry-wide rankings above.

Signal correlations calculated only for domains recommended in the context of this persona's queries. These may differ from the industry-wide pattern above.

Differences from the overall industry table reveal which signals matter specifically for reaching this audience — useful for targeting content strategy and acquisition priorities for this segment.

How often AI recommended each domain when answering CFO of manufacturer queries within Commercial banking. Each persona has 25 query runs — count is raw appearances, percentage is out of those runs.

Compare with the industry-wide charts above to see which domains over- or under-index for this specific audience segment.

Signal matrix for the top 25 LLM-recommended domains filtered to this persona's query context.

Look for patterns in which signal combinations characterize domains that resonate specifically with this audience — these may differ from industry-wide norms and point to persona-specific ranking drivers.

Domains in Google search results for CFO of manufacturer queries within Commercial banking. Counts reflect only the Google search results for keywords mapped to this persona — filtered to domains that appear in this industry.

Note: the number of results naturally varies by persona because different query intents return different domains. A persona with fewer results here is not a data gap — it reflects that those specific queries surface a narrower or different set of sites.

Backlink authority rankings for top domains within this persona segment.

Authority signals reflect broader domain strength that generally carries across personas. High-authority domains appearing only in some personas may indicate specialized trust or content relevance that isn't fully general.

This persona represents a distinct audience segment identified from LLM query patterns within this industry. The description reflects how AI models characterize users making queries in this segment.

Domains recommended for this persona are scored based on how frequently they appear in persona-specific queries — which may differ from the industry-wide rankings above.

Signal correlations calculated only for domains recommended in the context of this persona's queries. These may differ from the industry-wide pattern above.

Differences from the overall industry table reveal which signals matter specifically for reaching this audience — useful for targeting content strategy and acquisition priorities for this segment.

How often AI recommended each domain when answering Healthcare practice operator queries within Commercial banking. Each persona has 25 query runs — count is raw appearances, percentage is out of those runs.

Compare with the industry-wide charts above to see which domains over- or under-index for this specific audience segment.

Signal matrix for the top 25 LLM-recommended domains filtered to this persona's query context.

Look for patterns in which signal combinations characterize domains that resonate specifically with this audience — these may differ from industry-wide norms and point to persona-specific ranking drivers.

Domains in Google search results for Healthcare practice operator queries within Commercial banking. Counts reflect only the Google search results for keywords mapped to this persona — filtered to domains that appear in this industry.

Note: the number of results naturally varies by persona because different query intents return different domains. A persona with fewer results here is not a data gap — it reflects that those specific queries surface a narrower or different set of sites.

Backlink authority rankings for top domains within this persona segment.

Authority signals reflect broader domain strength that generally carries across personas. High-authority domains appearing only in some personas may indicate specialized trust or content relevance that isn't fully general.

This persona represents a distinct audience segment identified from LLM query patterns within this industry. The description reflects how AI models characterize users making queries in this segment.

Domains recommended for this persona are scored based on how frequently they appear in persona-specific queries — which may differ from the industry-wide rankings above.

Signal correlations calculated only for domains recommended in the context of this persona's queries. These may differ from the industry-wide pattern above.

Differences from the overall industry table reveal which signals matter specifically for reaching this audience — useful for targeting content strategy and acquisition priorities for this segment.

How often AI recommended each domain when answering Nonprofit Finance Director queries within Commercial banking. Each persona has 25 query runs — count is raw appearances, percentage is out of those runs.

Compare with the industry-wide charts above to see which domains over- or under-index for this specific audience segment.

Signal matrix for the top 25 LLM-recommended domains filtered to this persona's query context.

Look for patterns in which signal combinations characterize domains that resonate specifically with this audience — these may differ from industry-wide norms and point to persona-specific ranking drivers.