See llms.txt for all machine-readable content.
This workflow automates employee retention analytics by combining candidate performance data with trait-level retention statistics. It scores candidates, validates data, and generates a polished Retention Digest HTML email using GPT (Azure OpenAI). Hiring managers receive structured insights weekly, highlighting top/weak traits, candidate scores, and actionable JD refinement tips.
โก Manual Trigger โ Starts workflow execution on demand.
๐ Candidate Data Fetch (Google Sheets โ Hires Tracking) โ Pulls candidate-level details like name, role, traits, start date, and retention status.
๐ Trait Summary Fetch (Google Sheets โ Retention Summary) โ Fetches aggregated trait-level retention statistics, including hires, stayed, left, retention %, and weight adjustments.
๐ Merge Candidate + Trait Data โ Combines both datasets into a unified stream for scoring.
๐งฎ Candidate Scoring & Data Normalization (Code Node) โ
Google Sheets (Hires Tracking + Retention Summary + Error Log)
Gmail API credentials
Azure OpenAI access (gpt-4o-mini model)
n8n instance (self-hosted or cloud)
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Automates retention analytics & reporting
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Provides AI-powered insights in structured HTML
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Improves hiring strategy with trait-based scoring
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Reduces manual effort in weekly retention reviews
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Ensures reliability with error handling & validation
HR & Recruitment teams monitoring post-hire retention
Organizations optimizing job descriptions & hiring strategy
Talent analytics teams needing automated, AI-driven insights
Stakeholders requiring clear weekly digest emails