Expectation-Aware Candidate Matching for ATS — Implementation Guide & Pilot Checklist

The Woo integration for Greenhouse connects passive, high-quality candidates discovered by Woo’s machine learning to your Greenhouse ATS, enabling recruiters to convert latent interest into actionable pipelines without manual data entry. This introduction explains what the integration does, who benefits from it, and the concrete gains you can expect when you replace scattershot outreach with targeted, expectation-aware matching. You’ll find actionable setup guidance, a practical implementation checklist, key metrics to track post-launch, common pitfalls and how to avoid them, and developer considerations for secure, reliable data flows. The goal is to give recruiting teams and engineering partners a clear playbook for deploying Woo inside the Greenhouse ecosystem and measuring ROI quickly.

What the integration does in practice is twofold: it syncs candidate profiles and match signals from Woo into Greenhouse while preserving match metadata (skills fit, interest level, contact history, and campaign source). Recruiters can then manage outreach, interview scheduling, and pipeline workflows entirely inside Greenhouse while leveraging Woo’s off-the-radar candidate discovery and expectation-matching algorithms.

How the Woo → Greenhouse flow typically works

• Candidate discovery Woo uses ML to identify passive candidates who match skills and expected career moves without requiring they become active job seekers.
• Match scoring Each discovered candidate receives a match score and an expectation score (compensation, location, role seniority) that travels with the profile.
• Sync to Greenhouse Selected or automated matches are pushed into Greenhouse as candidate records, with custom fields mapping match metadata and campaign source.
• Recruiter workflow Recruiters use Greenhouse to review, outreach, and progress candidates; activity and outcomes are sent back to Woo for performance feedback.
• Feedback loop Conversion and response data refine Woo’s matching over time, improving precision and reducing irrelevant outreach.

Core integration features to expect

• Automated candidate sync Push candidates and preserve match metadata without manual CSV exports or copy/paste.
• Custom field mapping Map Woo’s match score, expectation fields, and source tags to Greenhouse custom fields for filtering and pipeline rules.
• Two-way activity tracking Send outreach outcomes back to Woo to improve future match quality.
• Consent and contact preferences Carry candidate contact preferences and communication opt-ins through the integration to stay compliant and respectful.

Who benefits from the Woo + Greenhouse integration

Efficiency gains are the most immediate, measurable impact of the integration. By syncing only algorithmically qualified candidates into Greenhouse, teams reduce manual sourcing time, lower the number of low-quality outreach attempts, and eliminate repetitive data entry. That saved time translates directly to more recruiter bandwidth for candidate engagement and interviewing.

Candidate quality improves because Woo filters not only for technical fit but also for candidate expectations (compensation, geography, role type). That combination increases response and conversion rates: recruiters spend less time disqualifying unfit matches and more time advancing genuinely interested candidates through the funnel.

Implementation checklist (pre-launch to first 30 days)

• Define field mapping Identify which Woo fields (match score, expectation fields, source, consent flags) map to Greenhouse custom fields and create them in Greenhouse before syncing.
• Agree matching thresholds Decide the minimum match and expectation thresholds for automated pushes versus manual review to avoid flooding ATS with low-probability leads.
• Set up webhooks and permissions Configure authentication, webhook endpoints, and least-privilege API keys on both sides for secure data exchange.
• Pilot with one team Start with a single hiring team or role to validate mappings, outreach templates, and acceptance criteria.
• Instrument analytics Track source attribution, response rate, conversion to interview and offer, and recruiter time saved to measure ROI.

Key metrics to track after launch

Common pitfalls and how to prevent them

• Over-importing low-match candidates Prevent ATS clutter by setting conservative automated thresholds and using a manual review queue for lower-score matches.
• Poor field alignment Map data types explicitly (e.g., numeric match score vs. text labels) and test sample records before full sync.
• Ignoring consent flags Respect candidate communication preferences sent from Woo and implement them in outreach sequences to avoid candidate churn or compliance issues.
• Not instrumenting feedback Without sending outcome data back to Woo, match models can’t learn; ensure two-way activity logging.

Developer considerations: plan for API rate limits, retry logic, secure storage of API keys, and thorough logging for match sync events. Use idempotent operations to avoid duplicate candidate creation and implement webhook verification to ensure data integrity. Also, document field schemas and version any mapping scripts so future changes to Woo or Greenhouse APIs don’t break production flows.

A short hypothetical scenario: a mid-size SaaS company uses Woo to identify senior backend engineers who are passively open to opportunities, syncs top matches into Greenhouse, and limits automated pushes to scores above an agreed threshold. Recruiters receive pre-matched, expectation-aligned candidates and focus outreach on a shorter, higher-impact list. The team sees higher response rates and spends more time interviewing instead of sourcing.

Frequently asked questions about the integration

How Woo + Greenhouse compares to other sourcing approaches