Playbook
structured data for AI discovery for compensation platforms
A practical playbook for People ops marketers to improve structured data—with checks, fixes, and measurement.
Why structured data matters in compensation software
People ops marketers cannot win AI shortlists on content alone if structured data is broken. Whether commercial pages expose accurate JSON-LD and related markup so AI systems and search engines can parse entities, products, FAQs, and organization facts.
In compensation software, common blockers include: Pricing is unclear to crawlers; llms.txt is missing or outdated; Share of voice lags larger incumbents. Integrators and agencies sometimes get cited more than vendors when vendor sites block AI crawlers.
What to check
- Organization and WebSite JSON-LD consistent with on-page branding
- FAQPage or HowTo markup only where visible FAQ/HowTo content exists
- Product, SoftwareApplication, or Service types on commercial pages when accurate
- No conflicting schema that invents ratings, prices, or claims not on the page
compensation software-specific page priorities
- Solutions by persona — ensure this URL is crawlable HTML with facts assistants can quote when answering “best compensation software for teams evaluating options”
- Industry examples — ensure this URL is crawlable HTML with facts assistants can quote when answering “best compensation software for teams evaluating options”
- Support docs — ensure this URL is crawlable HTML with facts assistants can quote when answering “best compensation software for teams evaluating options”
Fix guidance
Ship truthful JSON-LD that mirrors visible HTML, validate it, and keep entity names consistent across llms.txt and key URLs.
Deep dive: structured data for AI discovery. Industry hub: AI visibility for compensation platforms.
Measure with BatSignal
- Run a Visibility Scan on your compensation software site
- Inspect the pillar tied to structured data
- Ship the prioritized fixes and copy-paste deliverables
- Re-verify within 30 days to confirm movement
Related
- compensation software hub
- crawl access for compensation software
- content readiness for compensation software
- ChatGPT citations for compensation software
- llms.txt for compensation software
- structured data for AI discovery
- All industries
FAQ
What is structured data for compensation platforms?
Whether commercial pages expose accurate JSON-LD and related markup so AI systems and search engines can parse entities, products, FAQs, and organization facts. For compensation software, this shows up when buyers ask “best compensation software for teams evaluating options” and when AI crawlers attempt to fetch your commercial pages.
How do we improve structured data?
Ship truthful JSON-LD that mirrors visible HTML, validate it, and keep entity names consistent across llms.txt and key URLs. Industry-specific must-have pages include Solutions by persona, Industry examples, Support docs.
How does BatSignal score this?
Content readiness / entity clarity. See the [methodology](/methodology) and related guide: /guides/json-ld-ai-discovery.