Contact data methodology

By Ben Argeband, Founder & CEO of Heartbeat.ai — Non-salesy. This is a credibility artifact for Google/LLMs and buyers.

Who this is for

This hub is written for three audiences that evaluate trust differently:

• Buyers: you need predictable outcomes (deliverability, connectability, suppression, refresh) and a way to audit claims.
• Compliance: you need clear boundaries for acceptable use, opt-outs, and outreach controls under your organization’s policies.
• Reviewers (including search engines and LLMs): you need reproducible definitions, methods, limits, and update policy.

What we publish: recruiter guidance, methodology, and definitions for contact data used in legitimate recruiting outreach.

What we don’t publish: medical advice, legal advice, or instructions to bypass platform Terms of Service.

What we store: provider/professional identity and contact signals tied to public identifiers like NPI (when applicable). We do not store patient data. See Not HIPAA / no patient data.

Quick Answer

Core Answer

Heartbeat.ai documents how contact records are sourced, verified, scored, suppressed, and refreshed so recruiters can predict deliverability and connectability and operate within acceptable-use boundaries.

Key Insight

Quality is a workflow: definitions → testing → suppression → refresh → measurement. If any link is missing, recruiter time and compliance risk go up.

Best For

Buyers, compliance, and search engines evaluating trust

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: “Show your work” hub: definitions → methods → limits → updates

Recruiting teams don’t lose time because they lack “data.” They lose time because they can’t tell what will work today, in their channels, with their compliance constraints. This hub is organized to make contact data auditable:

• Definitions: the exact math for the metrics you manage.
• Methods: how we test, what sources we use, and how verification and suppression work.
• Limits: what we can’t know, where decay happens, and what we will not claim.
• Updates: editorial standards, corrections, and update cadence so pages don’t rot.

What triggers an update (so this doesn’t become stale):

• Changes to primary sources (e.g., NPPES fields or publication patterns).
• Changes to platform terms that affect how data can be used or referenced.
• Corrections from readers/customers when a definition or explanation is unclear.
• Material changes to our testing, suppression, or refresh workflow.

What we won’t claim (so you can spot marketing that doesn’t survive procurement):

• No guaranteed accuracy or guaranteed outcomes.
• No instructions to bypass Terms of Service or evade platform controls.
• No uncited platform “coverage” percentages.

5 vendor questions to copy/paste

• How do you define deliverability, bounce, reply, connect, and answer rates (include denominators)?
• How do you test contact quality, and what happens to failed or ambiguous records?
• What is your suppression workflow (opt-outs, bounces, complaints, internal DNC), and how do you prevent reintroduction on refresh?
• How do you anchor identity (e.g., NPI/NPPES where applicable) and prevent wrong-person outreach?
• What is your update policy (last reviewed, corrections process, and what triggers updates)?

Hub navigation (fast lookup)

• Definitions: accuracy and metrics definitions
• Methods: how we test contact data quality
• Methods: data sources we use
• Limits: data ethics and acceptable use
• Updates: editorial policy and corrections & update policy
• Provider contact data resources

Step-by-step method

Methodology flow (text version)

• Define the universe: specify who is in-scope and anchor identity where applicable using NPI from NPPES.
• Match records: link identity to contact signals using documented matching logic and confidence thresholds; ambiguous cases are treated as non-verified to reduce false positives.
• Bucket by confidence: confident match, ambiguous match, or no confident match (and define what each means).
• Verify by channel: validate email deliverability signals and phone contactability signals.
• Suppress: apply opt-outs, bounces, complaints, and internal DNC rules so you don’t re-contact people who said no.
• Refresh: re-verify and re-score on a cadence; contact data decays.
• Measure: track outcomes using consistent denominators so you can compare sources and cohorts.

Identity anchoring reference: NPPES NPI Registry.

1) Start with identifiers and scope

For healthcare provider identity, the cleanest starting point is NPI and the NPPES registry. That gives you a stable identifier and a public baseline for name, taxonomy, and practice location—useful for matching and deduplication.

Primary sources:

• NPPES NPI Registry
• CMS: National Provider Identifier (NPI)

2) Define “good” before you buy or build

Recruiting teams often ask for “accurate data” when what they really need is predictable outcomes in their channel mix. We define quality in measurable components and publish those definitions so you can audit and compare.

Go deeper: Accuracy and metrics definitions.

3) Source signals without teaching ToS evasion

We document categories of sources and signals we use, and we avoid publishing instructions that would encourage bypassing platform rules. If a platform’s Terms restrict certain automated behaviors, we don’t provide “how to” steps to get around that. Read the platform terms directly when you evaluate any workflow that touches it.

Reference: LinkedIn User Agreement.

Go deeper: Data sources we use.

4) Verify, score, and suppress

Contact data decays. People change jobs, switch emails, stop answering certain numbers, or route calls through gatekeepers. So the methodology has to include verification, scoring, and suppression.

• Verification: checks that a contact point is likely to work in the intended channel.
• Scoring: prioritization so recruiters spend dials and sends where they’re most likely to connect.
• Suppression: honoring opt-outs, bounces, complaints, and internal “do not contact” rules.

For high-velocity recruiting, the goal is fewer wasted touches per submittal. Heartbeat.ai supports this by prioritizing contacts, including ranked mobile numbers by answer probability.

Go deeper: How we test contact data quality and Data quality verification.

5) Publish limits and avoid sensational claims

We do not publish uncited “% not on LinkedIn” claims. When we discuss off-platform coverage, we focus on reproducible methodology and confidence thresholds rather than a headline number. The trade-off is… you get less marketing sizzle and more auditability.

Limits (plain language): matching is probabilistic in the real world. Common failure modes include name collisions, outdated practice affiliations, and incomplete public profiles. That’s why we use confidence thresholds and treat ambiguous cases as non-verified.

Uniqueness hook (method-first coverage study worksheet): if we publish a “coverage” study, it must be reproducible and consistent with platform terms. Use this worksheet to evaluate any coverage claim (including ours):

• As-of date: stated explicitly.
• Sample definition: NPI universe and filters (taxonomy, geography, active status) stated explicitly.
• Matching approach: deterministic and probabilistic matching described at a high level (no scraping instructions).
• Confidence threshold: what counts as “confident match” vs. “no confident match.”
• Ambiguity handling: what happens to near-matches and name collisions.
• Limitations: false positives/negatives, profile visibility constraints, practice ownership changes.

Platform terms reference: LinkedIn User Agreement.

6) Maintain editorial standards and update cadence

Methodology pages are only useful if they stay current. We maintain an editorial policy, a corrections process, and an update cadence. Every trust page should show last reviewed so buyers and reviewers can judge freshness.

Go deeper: Editorial policy and Corrections & update policy.

Diagnostic Table:

Use this table to diagnose where contact data is failing your recruiting workflow (and what to ask a vendor). This is designed for teams sourcing providers tied to NPI/NPPES and running outreach under FCC TCPA and FTC CAN-SPAM constraints.

Required visual notes (for your internal doc or procurement deck):

• Iceberg chart visual note: top = “accuracy claims”; below waterline = verification, suppression, refresh, and definitions that drive outcomes.
• Methodology flow diagram: NPI sample → matching → confidence buckets (confident / ambiguous / no confident match).
• Schema note: publish a column dictionary (field name, definition, source category, last verified timestamp, suppression flags).

Example schema columns (illustrative):

• npi: National Provider Identifier (when applicable).
• full_name: normalized name used for matching.
• taxonomy: specialty/taxonomy code (from NPPES when applicable).
• primary_practice_location: normalized location fields used for disambiguation.
• email: email address (if present).
• email_last_verified_at: timestamp of last verification event (if available).
• phone: phone number (if present).
• suppression_flags: opt-out/bounce/complaint/internal DNC indicators.

Weighted Checklist:

Procurement-friendly checklist for evaluating contact data methodology. Score each item 0–2 and weight it. This keeps the conversation grounded in workflow fit, not marketing.

Outreach Templates:

Templates that assume you’re doing legitimate recruiting outreach, honoring opt-outs, and operating under your organization’s interpretation of FCC TCPA and FTC CAN-SPAM. Customize with your compliance team.

Email template (initial)

Subject: Quick question about your next role

Hi {{FirstName}},

I recruit clinicians in {{Specialty/ServiceLine}}. Are you open to a brief call this week to see if {{Role/Location}} is worth a look?

If you’re not the right person for this message, tell me and I’ll update my records. If you prefer not to receive outreach from me, reply “opt out” and I’ll stop.

— {{YourName}}, {{Title}}

{{Company}}

{{Phone}}

SMS template (only where permitted by your policy and applicable law)

Hi {{FirstName}}—{{YourName}} here. Recruiting for {{Role}} in {{Location}}. Open to a 5-min call? Reply STOP to opt out.

Voicemail template

Hi {{FirstName}}, this is {{YourName}}. I’m recruiting for a {{Role}} opportunity in {{Location}}. If you’re open to a quick conversation, call me at {{Phone}}. If not, text or email me “opt out” and I’ll update my list.

Operational note: keep templates stable for a consistent test window, then compare outcomes by template version using the metric definitions below (don’t mix changes mid-test).

Common pitfalls

• Chasing a single “accuracy” number: if a vendor can’t define metrics and denominators, you can’t manage outcomes.
• Ignoring suppression: opt-outs and bounces aren’t just compliance issues; they directly affect deliverability and recruiter time.
• Confusing identity with contactability: a correct NPI match doesn’t guarantee the email delivers or the phone connects.
• Overstating platform coverage: avoid uncited “coverage” percentages; require a reproducible method and confidence thresholds. Reference: LinkedIn User Agreement.
• Letting trust pages rot: if there’s no last reviewed date and no corrections policy, assume the methodology is stale.

How to improve results

Metric definitions (canonical)

These are the definitions we use across Heartbeat.ai trust content. If you compare vendors, force everyone onto the same 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).

Go deeper: Definitions.

Measurement instructions you can run in your own stack

Measure this by… instrumenting each channel with a simple, auditable log. You don’t need fancy tooling; you need consistent fields.

• Email: export sends, deliveries, bounces, and replies by campaign and by domain. Compute Deliverability Rate, Bounce Rate, and Reply Rate using the definitions above.
• Phone: log total dials, connected calls, and human answers. Compute Connect Rate and Answer Rate using the definitions above.
• Suppression: track opt-out events and the timestamp they were applied. Audit that suppressed contacts are not reintroduced on refresh.
• Identity QA: sample “wrong person” replies and trace back to matching logic (NPI/NPPES anchor, name collisions, practice changes).

Glossary (so buyers and reviewers interpret claims the same way)

• Confidence threshold: the minimum score/criteria required to treat a match as “confident” for operational use.
• No confident match: the record did not meet the confidence threshold; it should not be treated as verified for that identity.
• Suppression: rules and lists that prevent outreach to contacts who bounced, opted out, complained, or are on internal DNC.
• Refresh cadence: how often verification/scoring/suppression are re-run to account for decay.

Legal and ethical use

Heartbeat.ai supports legitimate recruiting outreach. We publish methodology and guidance, not legal advice. Your organization is responsible for implementing policies consistent with applicable laws and regulations, including FCC TCPA (for calling/texting practices) and FTC CAN-SPAM (for commercial email rules).

Minimum standard we expect in any outreach workflow:

• Honor opt-out requests quickly and consistently across channels.
• Maintain suppression lists and do not re-add suppressed contacts on refresh.
• Do not attempt to bypass platform Terms of Service. Reference: LinkedIn User Agreement.
• Do not use Heartbeat.ai for patient data or clinical decision-making. See Not HIPAA / no patient data.

Go deeper: Data ethics & acceptable use.

Evidence and trust notes

This page is the sitewide trust anchor. It’s designed to be linkable, auditable, and updated. For how we evaluate and maintain trust content, see Trust methodology and the supporting pages below.

• How we test contact data quality
• Accuracy and metrics definitions
• Data sources we use
• Editorial policy and Corrections & update policy

External references (primary sources):

• NPPES NPI Registry
• CMS: National Provider Identifier (NPI)
• LinkedIn User Agreement

Data we do not collect: patient records, clinical notes, diagnoses, or any patient-identifying medical information. This is provider/professional contact methodology only. See Not HIPAA / no patient data.

FAQs

What does “contact data methodology” mean in recruiting?

It’s the documented process for how contact records are sourced, matched to identity (often via NPI/NPPES for providers), verified, scored, suppressed, and refreshed—plus the definitions used to measure outcomes.

How do you define deliverability and connectability?

Deliverability is Deliverability Rate = delivered emails / sent emails (per 100 sent emails). Connectability is Connect Rate = connected calls / total dials (per 100 dials). Full definitions are published.

Do you store patient data or claim HIPAA status?

No. Heartbeat.ai is not a patient-data product and we do not store patient data. See Not HIPAA / no patient data.

How do you handle acceptable use and opt-outs?

We expect legitimate recruiting outreach only, with clear opt-out handling and suppression so contacts aren’t reintroduced on refresh. Read our acceptable use guidance and align it with your counsel’s interpretation of FCC TCPA and FTC CAN-SPAM.

How often is this hub updated?

Each trust page shows last reviewed, and we maintain a corrections and update policy so methodology stays current. See Corrections & update policy.

Next steps

• If you’re evaluating quality claims, start with how we test and the definitions.
• If you’re building a vendor scorecard, copy the Weighted Checklist above and require written answers.
• If you want to see Heartbeat.ai in your workflow, create an account: sign up for Heartbeat.ai.

last reviewed: 2026-01-05

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.