Source Diversity Score

Extended definition

Source Diversity Score measures the variety of content types AI engines successfully retrieve and cite from your domain: articles, guides, case studies, documentation, research, tools, calculators, datasets, etc. High diversity indicates robust, multi-format authority; low diversity despite high content volume suggests only certain formats are AI-accessible. The score reveals whether AI engines see you as a one-dimensional source (e.g., 'only good for blog posts') or comprehensive authority. Diversity also provides resilience—if algorithm changes devalue one content type, others maintain visibility.

Why this matters for AI search visibility

AI engines favor sources that provide evidence in multiple formats because diverse sourcing increases answer quality and reduces hallucination risk. A brand cited only from blog posts appears less authoritative than one cited from research papers, tools, case studies, and technical docs. Source Diversity Score also reveals content gaps: if you publish case studies but AI never cites them, there's a format-specific optimization problem. For B2B brands, diversity ensures you can support different query intents—research queries pull white papers, how-to queries pull guides, comparison queries pull case studies.

Practical examples

• Brand cited from 8 distinct content types scores high diversity and appears in 3.2x more answers than competitors with single-format citations
• Analysis reveals strong blog citation but zero technical documentation citations, identifying optimization gap
• Increasing diversity from 3 to 7 content types correlates with 54% increase in overall citation rate