High Priority
Deploy Customer Data Taxonomy (/cdp-taxonomy.txt)
Establish a machine-readable summary of your entire customer data hierarchy specifically for AI agents focused on retention.
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.
Configure your Retention marketing teams crawler protocols effortlessly.
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High Priority
Retention Bot Selective Indexing
Fine-tune which sections of your retention marketing resources should be ingested by AI crawlers analyzing customer lifecycle data.
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.
Medium Priority
Semantic Customer Data Models
Use semantic HTML and structured data to help LLM scrapers understand the hierarchy and relationships within your customer data narratives.
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.
High Priority
RAG-Friendly Customer Insight Snippets
Structure your customer retention insights so they can be easily 'Chucked' and retrieved by RAG pipelines for personalized customer engagement.
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').
