SalesIntel for healthcare recruiting: proof-first pilot, scorecard, and decision outcomes

Ben Argeband, Founder & CEO of Heartbeat.ai — Keep it neutral and useful.

Who this is for

This is for recruiters evaluating SalesIntel for healthcare recruiting who need a defensible way to decide whether it will improve clinician outreach outcomes without creating deliverability issues or ATS cleanup.

Healthcare outreach usually fails for one of three reasons: identity mismatch (wrong clinician), channel mismatch (office lines and generic inboxes), or decay (contact points go stale). This page gives you a pilot plan, a scorecard, and decision outcomes you can use in an ops review.

Quick Answer

Core Answer

Evaluate SalesIntel for healthcare recruiting with a cohort-based pilot that defines verification and refresh cadence, then measures email deliverability and phone connect outcomes.

Key Insight

Clinician data quality is best judged by identity match and reachable channels, not record counts; require timestamps, suppression, and outcome attribution by cohort.

Best For

Recruiters evaluating SalesIntel for clinician outreach.

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.

TL;DR decision

Decision outcomes

• Adopt: identity resolution holds up in your audit, suppression is enforceable, and you can attribute outcomes by cohort and pull date.
• Adopt with guardrails: it works for one channel or cohort, but you need staging, stricter suppression, or an enrichment/verification layer before broad rollout.
• Do not adopt: wrong-person rate is high, corrections don’t propagate, or you can’t measure outcomes cleanly (you’ll pay for it in recruiter time and deliverability).

Framework: The “Proof Over Claims” Approach: ask for pilot + definitions

Compare decisions go sideways when teams accept vague terms like “verified” or “fresh” without operational definitions. The fix is to define terms, run a cohort-based pilot, and decide based on measured outcomes.

One nuance for healthcare: general B2B contact data can look “fine” on paper while still failing clinician outreach. Clinicians share clinic numbers, rotate locations, and have gatekeepers. If identity resolution and channel type aren’t clear, you’ll waste dials and create wrong-person contacts.

• Define outcomes you care about: deliverability, connectability, reply rate, and recruiter time spent fixing records.
• Define vendor terms in writing: verification definition and refresh cadence definition.
• Run a cohort-based pilot against your baseline source using the same outreach motion.
• Require a correction loop so bounces, wrong numbers, and opt-outs stop repeating.

The trade-off is… a disciplined pilot takes coordination, but it prevents months of wasted dials, domain damage, and messy attribution.

Questions to ask SalesIntel (and any vendor) before the pilot

• Identity resolution: What fields do you provide to disambiguate clinicians with similar names across facilities (and how do you handle moves)?
• Verification definition: What evidence is used to confirm a contact belongs to the intended clinician identity and is reachable, and how recent must that evidence be?
• Refresh cadence definition: How often are records re-checked, what triggers updates, and how quickly do corrections propagate to exports/API?
• Suppression: How do you recommend handling opt-outs and bounces so they don’t re-enter future pulls?
• Attribution: What stable identifiers and timestamps are available so we can measure outcomes by source and pull date?

Request these fields in a sample export (for audit-ready evaluation notes)

• Stable person identifier (so you can reconcile across pulls and systems)
• Contact type (email type and phone type, if provided)
• Last updated timestamp (per contact point, not just per record)
• Source tag / pull date (or a way for you to attach it reliably)
• Suppression indicator (or documented method to prevent re-importing suppressed contacts)

Step-by-step method

Step 1: Write your definitions (so “verified” means something)

• Verification definition (operational): the vendor’s documented process for confirming that a contact point belongs to the intended clinician identity and is currently reachable via that channel (email/phone), including what evidence is used, how recent it must be, and what gets suppressed.
• Refresh cadence definition: how often the vendor re-checks and updates contact records (and what triggers an update), including how quickly corrections propagate to your exports or API pulls.

Define the metrics you will report (with denominators):

• 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).
• Connect Rate = connected calls / total dials (per 100 dials).
• Answer Rate = human answers / connected calls (per 100 connected calls).

Step 2: Build 3–5 cohorts that match your real recruiting pain

• Employed clinicians (health system / group) vs. independent practice (often different routing and gatekeeping).
• Your hardest specialty cohort (the one that currently burns the most recruiter time).
• Your highest-volume geography (where you place most often).
• Early-career vs. established clinicians (different mobility and contact patterns).

Step 3: Run a controlled A/B pilot against your baseline

1. Select the same clinician identities for both sources (same cohorts, same list).
2. Audit identity resolution before outreach: confirm the record maps to the correct clinician (name, specialty, location, and organization context you track).
3. Test email and phone separately so one channel doesn’t hide the other.
4. Keep outreach constant (same copy, same cadence, same sending rules) during the pilot window.

Step 4: Set up suppression and a correction loop (so errors stop repeating)

• Suppression list: maintain a single suppression source of truth (email + phone) and apply it before any send/dial.
• Error capture: log dispositions like “wrong clinician,” “wrong number,” “office main line,” “bounced,” and “opt-out.”
• Correction propagation: confirm how quickly those errors can be reflected in future exports/pulls (your refresh cadence definition should cover this).

Step 5: Report results with consistent denominators

Measure this by… reporting each metric per 100 attempts and by cohort, with the same outreach motion across sources. That’s how you avoid misleading comparisons.

• Email: Deliverability Rate, Bounce Rate, Reply Rate.
• Phone: Connect Rate, Answer Rate.
• Ops: recruiter minutes spent on record cleanup per 100 contacts (manual research, wrong numbers, wrong clinician).

Diagnostic Table:

Weighted Checklist:

VENDOR_SCORECARD worksheet (uniqueness hook)

Use this scorecard to make a decision that holds up in a recruiting ops review. Score each category 1–5, multiply by weight, and attach proof notes from your pilot.

Outreach Templates:

Use consistent templates during the pilot so you’re measuring data quality, not copy changes. Add a source tag (example: “SalesIntel pilot”) and pull date to every record before outreach.

Email template (initial)

Subject: Quick question about your next role

Hi {{FirstName}} — I recruit {{Role/Specialty}} roles in {{Region}}. Are you open to a brief call this week, or should I send details by email?

If you’re not the right person or prefer not to be contacted, tell me and I’ll update my records.

— {{YourName}}, {{Title}}
{{Company}} recruiting

Call opener (15 seconds)

“Hi {{FirstName}}, this is {{YourName}}. I recruit {{Role/Specialty}} roles in {{Region}}. Did I catch you at an okay time for 20 seconds?”

• If yes: “Are you open to hearing about a role that’s {{1–2 constraints}}?”
• If no: “What’s a better time window, or should I email details?”

Voicemail (10 seconds)

“{{FirstName}}, {{YourName}}. Recruiting a {{Role/Specialty}} role in {{Location}}. Call me at {{Number}}. If you prefer email, reply and I’ll send details. If you want me to stop contacting you, tell me and I’ll stop.”

Common pitfalls

• Letting “verified” stay undefined. If you can’t write the vendor’s verification definition in one sentence, you can’t compare it to anything.
• Piloting on easy segments. Test your hardest cohort, or you’ll overestimate impact.
• Mixing experiments. Don’t change copy, cadence, and data source at the same time.
• Ignoring identity mismatches. Wrong-person outreach wastes recruiter time and creates reputational risk.
• No suppression loop. If opt-outs and bounces don’t suppress future pulls, you’ll repeat the same mistakes.

How to improve results

1) Instrument attribution so you can compare sources cleanly

Measurement instructions:

• Tag records with source and pull date before outreach (example: “SalesIntel pilot”, “2026-01-05 pull”).
• Email tracking: capture sent, delivered, bounced, replied. Report Deliverability Rate (delivered/sent per 100 sent), Bounce Rate (bounced/sent per 100 sent), Reply Rate (replies/delivered per 100 delivered).
• Phone tracking: capture dialed, connected, human answer, voicemail, wrong number, office line. Report Connect Rate (connected/total dials per 100 dials) and Answer Rate (human answers/connected calls per 100 connected calls).
• Ops tracking: log recruiter minutes spent on record cleanup per 100 contacts so you can see the hidden cost of low-quality data.

2) ATS hygiene workflow (reduce pollution and decay exposure)

• Stage: keep new contacts in a staging list with source + pull date.
• Validate: run your identity audit and channel tests on the staged list.
• Suppress: apply opt-outs and known bad endpoints before any outreach.
• Promote: push only outreach-ready records into your ATS/CRM.

3) Tighten refresh expectations with timestamps and propagation checks

Refresh cadence definition matters because clinician contact points decay. Require timestamps on contact points and verify how quickly corrections appear after you report bounces, wrong numbers, or opt-outs.

4) Reduce identity mismatches using your own context

If you track internal context (facility, specialty taxonomy, department, NPI, or employer), use it to validate matches and reduce mis-maps before outreach.

Legal and ethical use

• Legitimate recruiting outreach only: contact clinicians for real roles and keep messages relevant.
• Honor opt-outs: maintain suppression lists across email and phone and apply them before every send/dial.
• Data minimization: store only what you need for recruiting workflow and measurement.
• Local laws vary: confirm your outreach and data handling practices with counsel. Heartbeat.ai does not provide legal advice.

Evidence and trust notes

This page avoids unsourced claims about verification or performance. Use vendor materials as a baseline, then validate with a pilot and your own measurement.

• SalesIntel official site (vendor baseline description)
• Heartbeat.ai trust methodology (how we evaluate data quality claims)
• Physician contact enrichment (how to use enrichment without polluting your CRM)
• Data quality verification (definitions, tests, and what to document)

FAQs

What should I validate first when using SalesIntel for healthcare recruiting?

Validate clinician identity resolution and reachable channels first. If you’re contacting the wrong person or only reaching office lines, the rest of the funnel won’t matter.

How do I define “verification” in a vendor evaluation?

Use a verification definition that states what evidence is used to confirm the contact belongs to the intended clinician identity, how recent it must be, and what gets suppressed when it fails.

What is a refresh cadence definition I can actually enforce?

Refresh cadence definition should specify how often records are re-checked, what triggers updates, and how quickly corrections propagate to your exports/API after you report errors.

Which metrics matter most in a contact-data pilot?

For email: Deliverability Rate (delivered/sent per 100 sent), Bounce Rate (bounced/sent per 100 sent), Reply Rate (replies/delivered per 100 delivered). For phone: Connect Rate (connected/total dials per 100 dials) and Answer Rate (human answers/connected calls per 100 connected calls).

How do I keep the pilot fair across sources?

Use the same cohorts, the same outreach motion, and the same measurement window. Tag records by source and pull date so you can attribute outcomes cleanly.

Next steps

• Copy the VENDOR_SCORECARD worksheet into your internal doc and assign an owner for each proof item (identity audit, email test, phone test, suppression loop).
• If you want a workflow to run cohort-based searches and validate contacts before outreach, start free search & preview data.
• For deeper evaluation criteria, read how to evaluate provider contact data vendors before you commit to any dataset.

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.