Batch contact enrichment: the recruiting ops SOP for provider lists

Ben Argeband, Founder & CEO of Heartbeat.ai — Reduce confusion; clean decision tree.

Who this is for

This is for recruiting ops teams running enrichment at scale to keep ATS/CRM records usable for campaigns and recruiter workflows. If you import provider lists (often NPI-based), enrich contact fields in bulk, and then push updates back into an ATS, this page is your operating procedure.

Scope: batch enrichment across provider records, plus the governance pieces that prevent ATS pollution: dedupe, stable identity keys (including license matching when available), low-confidence routing, verification/suppression, and refresh cadence.

Quick Answer

Core Answer

Batch contact enrichment appends missing phone/email to provider records using stable identifiers, then verifies, suppresses, and refreshes so outreach stays deliverable and recruiters stop dialing dead numbers.

Key Insight

Batch enrichment only works as well as your keys and dedupe; it will not fix duplicates, and low-confidence matches must be routed before ATS writeback.

Best For

Recruiting ops running enrichment at scale for ATS hygiene and campaigns.

Compliance & Safety

This method is for legitimate recruiting outreach only. Always respect candidate privacy, opt-out requests, and local data laws. Heartbeat does not provide medical advice or legal counsel.

Framework: The Enrichment Readiness Check: Dedupe → Identify → Enrich → Refresh

If your batch runs keep disappointing, it is usually not the enrichment step. It is the steps around it. Use this sequence as a hard gate before you run anything at scale.

• Dedupe: collapse duplicates before enrichment so you do not enrich the same provider twice and write conflicting contact data back into the ATS.
• Identify: standardize stable identifiers (NPI first; license matching when you have it) so matching is deterministic.
• Enrich: append contact channels with match confidence and provenance, not just raw values.
• Refresh: re-run on a cadence because contact data decays; verification reduces bounces and dead dials.

Myth-bust: enrichment is not verification, and neither is refresh. Enrichment adds fields. Verification reduces bad channels before outreach. Refresh is repeating the process when performance decays.

Step-by-step method

Step 1: Confirm your batch input spec (minimum columns)

Batch enrichment needs stable keys. For provider records, the most reliable anchor is NPI. If you only have name + city, you will create false positives at scale and your recruiters will feel it immediately.

Step 2: Dedupe before enrichment (enrichment does not fix duplicates)

Enrichment does not fix duplicates; it amplifies them. If you enrich two duplicate rows, you can end up with two different phones/emails and then your ATS becomes a conflict generator.

1. Exact dedupe on NPI.
2. Fallback dedupe only when NPI is missing: normalized name + state + specialty (and keep a review queue for collisions).
3. Practice collisions: multiple providers can share a clinic phone/address. Keep separate provider identities, but label shared channels as office/practice, not personal.

For a full ops workflow, use how to dedupe a provider list using NPI as your pre-flight checklist.

Step 3: Define match confidence and accuracy before you run the batch

Teams argue about accuracy after the campaign fails. Define it up front so you can route low-confidence results and measure outcomes cleanly.

• Match confidence definition: a score or label indicating how likely the enriched contact belongs to the intended provider identity (as defined by your keys such as NPI, name, state, and license matching when available). High confidence means strong identity linkage; low confidence means the contact may belong to a different person or a shared office channel.
• Mobile accuracy definition: intended-provider reached / total enriched mobile numbers dialed (per 100 enriched mobile dials).
• Email accuracy definition: intended-provider positive identification (reply, call-back, or ATS-confirmed contact) / delivered enriched emails (per 100 delivered enriched emails).

What to do when match confidence is low: do not write it back as a primary channel. Route it to review, or store as an alternate with a clear label (office-only, unverified, or needs review).

Step 4: Run enrichment with writeback modes (append vs overwrite)

Batch enrichment should be a controlled append process, not a free-for-all overwrite. Pick a writeback mode per field and stick to it.

• Append-only (recommended default): write new values into alternate fields (or a structured contact table) and keep existing primary values unchanged.
• Overwrite-with-proof: overwrite only when verification and outcomes show the existing channel is dead or wrong-party, and the new channel is higher confidence.
• Do-not-writeback: keep enriched values in an ops layer only (for review or for controlled exports) when ATS governance is weak.

If you are using Heartbeat.ai, this is where you operationalize the differentiator: ranked mobile numbers by answer probability. The trade-off is… you still need governance on what gets written back and when, because more channels without routing creates recruiter thrash.

Step 5: Verify and suppress before outreach (protect deliverability and recruiter time)

Verification reduces bounces and dead dials. Treat this as a pre-send gate, not an optional cleanup step.

• Opt-out sync: apply your suppression list across ATS, email platform, and dialer before any send or dial session.
• Email suppression: suppress known invalids and risky addresses; do not send to suppressed records.
• Phone labeling: label mobile vs office vs switchboard so sequences do not waste prime call windows.
• Provenance check: ensure each channel has source + date enriched so you can debug failures and set refresh priorities.
• Confidence gate: do not include low-confidence channels in direct-to-provider sequences; route to review or office-only workflows.
• Consent handling: follow your internal policy for channel use and document it in the SOP.

Step 6: Refresh on a cadence (because results decay)

Refresh when results decay. Do not wait for a recruiter revolt. Prioritize refresh by segment (specialty, geography, source list, and age since last enrichment) and by observed performance drops.

To operationalize cadence, use provider data refresh cadence as your policy doc.

Diagnostic Table:

Metric definitions (use these consistently):

• Connect Rate = connected calls / total dials (per 100 dials).
• Answer Rate = human answers / connected calls (per 100 connected calls).
• Deliverability Rate = delivered emails / sent emails (per 100 sent emails).
• Bounce Rate = bounced emails / sent emails (per 100 sent emails).
• Reply Rate = replies / delivered emails (per 100 delivered emails).

Weighted Checklist:

Use this as your enrichment readiness checklist and your low-confidence routing policy. Score each line 0/1 and multiply by weight. If you score below your internal go/no-go threshold, fix inputs before you run a batch.

Low-confidence routing rules (copy into your SOP):

• Route to review if: multiple providers match the same contact, contact appears shared (clinic main line), or identity keys conflict.
• Approve only if: reviewer can corroborate via internal notes, prior conversations, or a trusted identity link.
• Reject if: contact clearly belongs to a different provider or is a generic switchboard.
• Mark office-only if: it is a legitimate practice number but not appropriate for direct-to-provider sequences.

Outreach Templates:

These templates assume legitimate recruiting outreach with consent and opt-out handling. Keep them short and operational.

Template 1: First-touch email (provider, direct)

Subject: Quick question about your next role

Hi Dr. {{LastName}} — I am reaching out because we are hiring a {{Specialty}} in {{City/Region}} with {{1-2 constraints: schedule/call/setting}}. If you are open to a quick conversation, what is the best number/time this week?

If you would rather not get messages from me, reply “opt out” and I will stop.

— {{RecruiterName}}, Heartbeat.ai

Template 2: Voicemail (when you hit an office line)

Hi Dr. {{LastName}}, this is {{RecruiterName}}. I am calling about a {{Specialty}} role in {{Region}} with {{constraint}}. If you are open to a quick chat, call me at {{CallbackNumber}}. If this is not a good number for you, tell me and I will update my records.

Template 3: Wrong number / update request SMS (only where permitted)

Hi Dr. {{LastName}} — this is {{RecruiterName}}. If this is not the right number for you, reply STOP and I will remove it. If it is, are you open to a quick call about a {{Specialty}} role in {{Region}}?

Ops note: If the reply indicates opt-out, suppress across ATS + dialer + email platform immediately.

Common pitfalls

• Running enrichment on a messy list: if you skip dedupe, you will create conflicting contact fields and recruiter distrust. Fix the list first.
• Confusing enrichment with verification: enrichment adds channels; verification reduces bounces/dead dials. You need both if you care about deliverability and recruiter time.
• Overwriting good data with new data: new is not automatically better. Store alternates and use verification + outcomes to decide what becomes primary.
• No low-confidence routing: if uncertain matches write back into the ATS, you will poison the system of record and create compliance risk.
• Ignoring refresh: contact data decays. If you do not refresh, your campaign metrics will drift and you will blame messaging when it is really list rot.
• Opt-out not centralized: if suppression lives in one tool but not another, you will re-contact people who asked you to stop.

How to improve results

Improvement is measurement discipline plus routing discipline. If you cannot see outcomes by source list and by date enriched, you cannot manage decay or prove ROI to leadership.

1) Instrument outcomes by segment and by date enriched

Tag each record with:

• source list
• date enriched
• match confidence bucket (high/medium/low)
• channel type (mobile/office/email)
• writeback status (written, alternate only, suppressed, review)

2) Use canonical metric definitions (so teams stop arguing)

Measure this by… tracking the following weekly, broken out by source list and by age since last enrichment:

• Deliverability Rate = delivered emails / sent emails (per 100 sent emails).
• Bounce Rate = bounced emails / sent emails (per 100 sent emails).
• Connect Rate = connected calls / total dials (per 100 dials).
• Answer Rate = human answers / connected calls (per 100 connected calls).
• Reply Rate = replies / delivered emails (per 100 delivered emails).

3) Tighten writeback rules using outcomes, not opinions

• Raise your confidence threshold if you see wrong-party contacts or shared office channels being treated as personal.
• Lower your confidence threshold only if review capacity is the bottleneck and outcomes remain clean.
• Prioritize refresh for segments where deliverability/connect outcomes are decaying fastest.

4) Make the workflow recruiter-proof inside the ATS

Recruiters do not want more fields; they want the next best action. Expose:

• one primary phone (with channel label)
• one primary email (verified)
• alternates (collapsed)
• confidence + date enriched
• opt-out status (prominent)

If you are building the broader provider contact workflow, pair this with physician contact enrichment so stakeholders understand the difference between one-off enrichment and batch operations.

Legal and ethical use

Batch enrichment touches personal data. Treat it like a controlled process, not a growth hack.

• Consent: follow your organization policy for outreach and channel use. Do not assume a channel is appropriate just because it exists.
• Opt-out: maintain a durable suppression list and apply it everywhere (ATS, email platform, dialer). If someone opts out, stop.
• Data minimization: store what you need for recruiting operations. Avoid collecting fields you will not use.
• Auditability: keep provenance (source, date, confidence) so you can explain why a contact was used.

Heartbeat.ai supports legitimate recruiting outreach workflows; it does not provide legal counsel.

Evidence and trust notes

Provider identity baselines commonly start with NPI. NPPES is the standard reference for NPI identity fields: https://nppes.cms.hhs.gov/.

For how Heartbeat.ai approaches sourcing, matching, and trust controls at a high level, see our trust methodology.

Scope note: This page is about batch enrichment across provider records. It separates enrichment vs verification vs refresh, and it assumes you have a dedupe gate and a low-confidence review route.

FAQs

What do I need before I run batch contact enrichment?

You need stable keys (ideally NPI), a deduped list, and writeback rules. Without those, you will enrich the wrong records and pollute your ATS.

Does batch enrichment remove duplicates in my ATS?

No. Enrichment does not fix duplicates; it can make them worse by adding conflicting phones/emails to multiple records. Dedupe first, then enrich.

How should I handle low-confidence matches?

Do not write them back automatically. Route them to a review queue where ops/sourcers can approve, reject, or mark office-only, and keep provenance and confidence labels.

How often should I refresh enriched provider contact data?

Refresh when results decay. Use deliverability/connect outcomes and date enriched to prioritize which segments to refresh first, rather than refreshing everything on a calendar.

What is the difference between enrichment and verification?

Enrichment appends missing contact fields. Verification reduces bounces and dead dials by checking whether a channel is likely to work now and by suppressing bad channels before outreach.

Next steps

• If your list is not clean, start with deduping by NPI before enrichment.
• If you need an operational cadence, use this refresh cadence guide to prevent decay from killing campaign performance.
• If you want to run a controlled batch workflow with preview and confidence routing, start free search & preview data.

About the Author

Ben Argeband is the Founder and CEO of Swordfish.ai and Heartbeat.ai. With deep expertise in data and SaaS, he has built two successful platforms trusted by over 50,000 sales and recruitment professionals. Ben’s mission is to help teams find direct contact information for hard-to-reach professionals and decision-makers, providing the shortest route to their next win. Connect with Ben on LinkedIn.