Score a B2B Email List Before Cold Outreach

Most cold-outreach lists need routing, not a binary pass or fail. A valid company domain with a role inbox is different from a malformed address, and both are different from a disposable mailbox. Treating them alike wastes good leads or sends to obvious junk.

Use the Email Verifier & Validator to get a transparent 0–100 score plus the observable inputs behind it. Use the built-in score directly or adapt it to your campaign policy.

Build a simple risk score

Start at 100 and subtract for evidence:

def email_score(row):
    if row["status"] == "bad":
        return 0

    score = row["score"]
    if row["role"]:
        score -= 10
    if row["free"]:
        score -= 5

    health = row.get("domainHealth") or {}
    if not health.get("spf"):
        score -= 10
    if not health.get("dmarc"):
        score -= 10
    return max(score, 0)

The exact weights should reflect the campaign. A partnership campaign may welcome partnerships@; a named-account sequence should prefer person-level business addresses.

Create operational bands

Scores are useful only when they lead to a decision. A workable policy is:

Measure bounce and reply rates by band. After several sends, adjust weights from your own outcomes rather than copying a generic industry model.

Join verification with account context

Email hygiene should not replace lead qualification. Join the result to company size, intent, territory, and source. A high-scoring address at the wrong account is still a bad lead. A role address at a perfect target account may deserve manual research.

Save technical_status, reason, and the raw flags alongside the final score. If the policy changes, you can rescore the list without repeating DNS and SMTP checks.

The result is a smaller, better-routed send list and a scoring process sales operations can inspect line by line.