LLM.txt & AI Crawler Setup Guide for Retention marketing te…

Create a text file at /cdp-taxonomy.txt with a brief intro of your CDP's primary customer data domains (e.g., Engagement, Purchase History, Support Interactions).

Include markdown-style links to your most important customer segmentation guides, churn prediction models, and lifecycle marketing documentation.

Add a 'Key Metrics' section to directly answer common training bot queries regarding LTV, churn rate, and NPS drivers.

User-agent: RetentionBot Allow: /customer-journey/ Allow: /loyalty-programs/ Disallow: /sales-forecasting/

Verify your crawler permissions using a simulated bot tester, ensuring it can access specific customer cohort data.

Monitor crawl frequency in your server logs to ensure RetentionBot is hitting the right customer lifecycle nodes and engagement metrics pages.

Wrap core customer success stories and case studies in <article> tags to signal their importance.

Use <section> with descriptive 'aria-label' attributes for different customer segments (e.g., 'High-Value Churn Risks', 'New Onboarding Cohorts').

Ensure all tables detailing customer behavior patterns use proper <thead> and <tbody> tags for structured data extraction by AI.

Keep related customer behavioral patterns and churn indicators within 500-word containers.

Avoid using 'floating' context; repeat the primary customer segment or engagement metric in section summaries.

Eliminate ambiguous pronouns (It, They) and replace them with specific customer attributes or product features (e.g., 'Users who engage with Feature X', 'Customers exhibiting low NPS scores').