AI Discovery & Answer Share
Win in AI-Driven Buyer Discovery

Buyers no longer start with Google. They start with ChatGPT, Claude, Perplexity, or Gemini—asking "Which CRM is best for mid-market B2B?" or "Compare Salesforce vs. HubSpot for enterprise."

AI systems answer these questions by synthesizing information from web sources, knowledge bases, and training data. If you're not cited by AI systems, you don't exist in AI-driven buyer discovery.

The problem: Unlike search (where you can track rankings, CTR, impressions), AI systems are a black box. You can't see:

• Which queries result in your company being mentioned
• Which competitors get cited instead of you
• Which narratives AI systems associate with your brand
• Which sources AI systems cite (your site, competitors, third-party reviews)
• How citation rates change over time

IQIEX tracks AI discovery systematically—measuring "answer share" across topics and providing actionable insights to improve citation rates.

1. Optimize for E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness)

AI systems favor sources with strong E-E-A-T signals:

• Experience: Author bylines with credentials, case studies, customer stories
• Expertise: In-depth technical content, original research, data/statistics
• Authoritativeness: Backlinks from trusted sources, media mentions, analyst reports
• Trustworthiness: Citations of sources, transparency about limitations, updated content

2. Create Comparison Content

AI systems heavily cite comparison content when buyers ask "X vs. Y" or "best X for Y." Create:

• Your product vs. competitor pages (honest, balanced comparisons)
• Category comparison guides (e.g., "CRM comparison: Salesforce vs. HubSpot vs. Pipedrive")
• Feature comparison matrices with evidence (screenshots, links)
• Use case comparisons (e.g., "Best CRM for enterprise vs. SMB")

3. Publish Original Research & Data

AI systems prioritize original data over synthesized content:

• Industry surveys and benchmarks (e.g., "State of Marketing Automation 2024")
• Customer data analysis (aggregated, anonymized insights)
• Product usage statistics (adoption rates, feature usage, outcomes)
• ROI calculators with methodology documentation

4. Structure Content for AI Parsing

AI systems parse content better when it's well-structured:

• Use semantic HTML (headings, lists, tables)
• Add schema markup (Product, Article, FAQPage, HowTo)
• Include clear definitions at the start of articles
• Use bullet points and numbered lists for key information
• Add metadata (published date, author, update date)

5. Build Topical Authority, Not Just Domain Authority

AI systems cite sources with topical expertise. Instead of broad content, create comprehensive coverage of specific topics:

• 20+ articles on a single topic cluster (e.g., "email deliverability")
• Link related articles together (pillar + spoke model)
• Update and expand content regularly (show recency)
• Cover topics from multiple angles (beginner, advanced, technical)

6. Get Cited by Third-Party Sources

AI systems weight third-party sources higher than vendor content. Get cited by:

• Industry publications (contributed articles, expert quotes)
• Review sites (G2, Capterra, TrustRadius—with detailed responses)
• Community forums (Reddit, Hacker News, industry Slack/Discord)
• Analyst reports (Gartner, Forrester, IDC)
• Academic or research publications (case studies, whitepapers)

7. Track Query-Level Citation Changes

Don't just track "are we cited?" Track "which queries result in citations?" and "which competitors are cited for which topics?" This reveals:

• Narrative gaps (topics where competitors dominate AI citations)
• Positioning opportunities (underserved query segments)
• Content ROI (which articles drive AI citations vs. which don't)