Contact Data Methodology (Trust & Methodology Hub)

Last updated: August 28, 2026

By Ben Argeband, Founder & CEO of Heartbeat.ai

Most “data quality” arguments in recruiting fall apart the moment someone asks for a denominator. This page exists so that doesn’t happen here. It documents how Heartbeat.ai sources, verifies, scores, suppresses, and refreshes provider contact records, and it’s written for three different readers who evaluate trust differently.

Who this is for

• 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 AI systems): you need reproducible definitions, methods, limits, and an 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 and professional identity signals tied to public identifiers like NPI, where 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 while staying inside acceptable-use boundaries.

Key insight

Quality is a workflow, not a single number: definitions, testing, suppression, refresh, and measurement. Skip a step and recruiter time and compliance risk both go up.

Best for

Buyers, compliance teams, and reviewers evaluating trust claims.

Compliance & safety

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

Framework: definitions, methods, limits, updates

Recruiting teams rarely lose time because they lack “data.” They lose time because they can’t tell what will actually work in their channels under their compliance constraints. This hub is organized so contact data claims can be checked:

• Definitions: the exact math behind the metrics you manage.
• Methods: how records are tested, what sources feed them, and how verification and suppression work.
• Limits: what can’t be known, where decay happens, and what we won’t claim.
• Updates: editorial standards, corrections, and cadence so pages don’t go stale.

What triggers an update:

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

What we won’t claim, so you can spot marketing that won’t survive procurement:

• No guaranteed accuracy or guaranteed outcomes.
• No instructions for bypassing Terms of Service or evading platform controls.
• No uncited platform “coverage” percentages.

Five vendor questions worth copying into an RFP

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

Hub navigation

• 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

The method, step by step

• 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 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.
• Verify by channel: check email deliverability signals and phone contactability signals separately.
• Suppress: apply opt-outs, bounces, complaints, and internal DNC rules so people who said no aren’t contacted again.
• Refresh: re-verify and re-score on a cadence, since contact data decays over time.
• Measure: track outcomes with consistent denominators so sources and cohorts can be compared fairly.

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, which gives a stable identifier and a public baseline for name, taxonomy, and practice location. That baseline is useful for matching and deduplication before any contact data enters the picture.

Primary sources: NPPES NPI Registry and CMS: National Provider Identifier (NPI).

2) Define “good” before you buy or build

Recruiting teams often ask for “accurate data” when what they actually need is predictable outcomes across their channel mix. We define quality in measurable components and publish those definitions so they can be audited and compared across vendors. Go deeper: Accuracy and metrics definitions.

3) Source signals without teaching ToS evasion

We document categories of sources and signals used, and we avoid publishing instructions that would encourage bypassing platform rules. If a platform’s terms restrict certain automated behavior, we don’t provide steps to get around it. Read the platform terms directly before evaluating any workflow that touches them. 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. The methodology has to account for that through three linked steps:

• Verification: checks that a contact point is likely to work in the intended channel.
• Scoring: prioritization so recruiters spend dials and sends where connection is most likely.
• 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 don’t publish uncited “percent not on LinkedIn” style claims. When off-platform coverage comes up, the focus stays on reproducible methodology and confidence thresholds rather than a headline number. That means less marketing sizzle and more auditability.

Limits, in plain language: matching is probabilistic. Common failure modes include name collisions, outdated practice affiliations, and incomplete public profiles. That’s why confidence thresholds exist and why ambiguous cases are treated as non-verified rather than guessed at.

A worksheet for judging 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, without scraping instructions.
• Confidence threshold: what counts as a confident match versus no confident match.
• Ambiguity handling: what happens to near-matches and name collisions.
• Limitations: false positives and 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, and every trust page shows a last-reviewed date. Go deeper: Editorial policy and Corrections & update policy.

Diagnostic table

Use this to trace where contact data is failing a recruiting workflow, and what to ask a vendor. It’s written for teams sourcing providers tied to NPI/NPPES and running outreach under FCC TCPA and FTC CAN-SPAM constraints.

Notes for a procurement deck:

• Think of accuracy claims as the visible tip of an iceberg; verification, suppression, refresh, and clear definitions sit below the surface and actually drive outcomes.
• A methodology flow diagram should show the path from NPI sample through matching to confidence buckets: confident, ambiguous, or no confident match.
• Ask any vendor for a column dictionary: field name, definition, source category, last-verified timestamp, and 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 the last verification event, if available.
• phone: phone number, if present.
• suppression_flags: opt-out, bounce, complaint, and internal DNC indicators.

Weighted checklist

A procurement-friendly way to evaluate contact data methodology. Score each item 0-2 and weight it so the conversation stays grounded in workflow fit rather than marketing language.

Outreach templates

These assume legitimate recruiting outreach, opt-outs honored, and operation under your organization’s interpretation of FCC TCPA and FTC CAN-SPAM. Have compliance review before use.

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’d rather not 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. Mixing changes mid-test makes results unreadable.

Common pitfalls

• Chasing a single “accuracy” number: if a vendor can’t define metrics and denominators, outcomes can’t be managed.
• 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 an email delivers or a phone connects.
• Overstating platform coverage: avoid uncited coverage percentages and require a reproducible method with confidence thresholds. Reference: LinkedIn User Agreement.
• Letting trust pages rot: no last-reviewed date and no corrections policy is a sign the methodology is stale.

How to improve results

Metric definitions (canonical)

These are the definitions used across Heartbeat.ai trust content. When comparing 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.

Measuring this in your own stack

• Email: export sends, deliveries, bounces, and replies by campaign and domain. Compute Deliverability Rate, Bounce Rate, and Reply Rate from the definitions above.
• Phone: log total dials, connected calls, and human answers. Compute Connect Rate and Answer Rate from the definitions above.
• Suppression: track opt-out events and the timestamp each was applied. Audit that suppressed contacts aren’t reintroduced on refresh.
• Identity QA: sample “wrong person” replies and trace them back to matching logic, whether it’s an NPI/NPPES anchor issue, a name collision, or a practice change.

Glossary

• Confidence threshold: the minimum score or criteria required to treat a match as confident for operational use.
• No confident match: a record that didn’t meet the confidence threshold and shouldn’t be treated as verified for that identity.
• Suppression: rules and lists that prevent outreach to contacts who bounced, opted out, complained, or are on an internal DNC list.
• Refresh cadence: how often verification, scoring, and 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, including the Telephone Consumer Protection Act (TCPA) for calling and texting practices and the CAN-SPAM Act 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 don’t re-add suppressed contacts on refresh.
• Don’t attempt to bypass platform Terms of Service. Reference: LinkedIn User Agreement.
• Don’t 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: linkable, auditable, and updated. Supporting pages:

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

External references:

• 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 and 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 separately.

Do you store patient data or claim HIPAA status?

No. Heartbeat.ai is not a patient-data product and doesn’t 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 TCPA and CAN-SPAM.

How often is this hub updated?

Each trust page shows a last-reviewed date, 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.

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 platforms used by sales and recruitment professionals. Ben’s focus is helping teams find direct contact information for hard-to-reach professionals and decision-makers. Connect with Ben on LinkedIn.