What is contact data accuracy

Ben Argeband, Founder & CEO of Heartbeat.ai — Measurable, recruiter language, includes copy/paste scorecard.

Who this is for

Recruiters and ops leaders who need to evaluate contact data quickly, decide whether it’s usable this week, and instrument the workflow so “accuracy” becomes a measurable ops lever (not a debate).

Quick Answer

Core Answer

Contact data accuracy is the percent of outreach attempts where the identity is correct and the chosen channel works, measured separately for phone and email.

Key Statistic

Heartbeat observed typicals: mobile field validity 82% per first mobile dial (connects to the intended person’s mobile line); email deliverability 95% per first send; connect rate ~10% per 100 dials (segment-dependent).

Best For

Recruiters and ops leaders evaluating data quickly without jargon.

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 Accuracy Stack: Identity → Channel Validity → Answerability

Teams argue about “accuracy” because they mix three different checks. In recruiting ops, you need all three—measured separately—so you can fix the right failure mode.

• Identity: the record belongs to the right person (correct person-to-credential-to-employer match).
• Channel Validity: the phone number connects to a live line; the email address delivers.
• Answerability: a human answers the connected call; a delivered email earns a reply.

In this article, “accuracy” is an observed, per-attempt outcome by channel (per 100 dials and per 100 sent emails). Engagement is measured separately so you don’t blame data for a messaging problem (or vice versa).

The trade-off is… you can buy “more records” or you can buy “more usable attempts.” Recruiting throughput is driven by usable attempts.

Step-by-step method

Step 1: Use channel-specific definitions (don’t use one blended “accuracy” number)

Use these definitions in your scorecard so everyone measures the same thing.

• contact data accuracy definition: The percent of outreach attempts where (1) identity is correct and (2) the chosen channel works as intended for that attempt. Always state the channel and denominator (per 100 dials or per 100 sent emails).
• Identity accuracy (ops definition): Per 100 connected calls, the share that reach the intended person (not a wrong person). A practical proxy is Wrong-person rate = wrong-person connects / connected calls (per 100 connected calls).
• mobile accuracy definition: Per 100 dials to a “mobile” field, the share that connects to the intended person’s mobile line (not disconnected, wrong person, or business main). This is channel validity; answerability is separate.
• email accuracy definition: Per 100 emails sent to an address, the share that is delivered (not bounced). This is channel validity; replies are separate.
• deliverability definition: Deliverability Rate = delivered emails / sent emails (per 100 sent emails).

Define the related metrics so you don’t “fix” the wrong layer:

• Connect Rate = connected calls / total dials (per 100 dials). “Connected” means the call reached a live line (human, voicemail, IVR, or gatekeeper), not a failed attempt.
• Answer Rate = human answers / connected calls (per 100 connected calls).
• Bounce Rate = bounced emails / sent emails (per 100 sent emails).
• Reply Rate = replies / delivered emails (per 100 delivered emails).

Step 2: Instrument “per 100 attempts” in your ATS/CRM

If you don’t log attempts, you’ll end up debating anecdotes (“this data is bad”) instead of fixing a measurable bottleneck.

Measure this by… logging every dial and email as an attempt, then calculating each metric per 100 attempts for a defined time window and segment (specialty, geography, source, campaign).

Worked example (fill in your numbers; do not guess):

• Per 100 dials: __ connected calls; per 100 connected calls: __ human answers; __ wrong-person connects
• Per 100 sent emails: __ delivered; __ bounces; per 100 delivered emails: __ replies

Step 3: Separate identity errors from channel errors

When a recruiter says “bad data,” it usually means one of these:

• Identity mismatch: wrong person, outdated employer, duplicate profiles merged incorrectly.
• Phone channel failure: disconnected, wrong number, business main, call routing tree that never reaches the candidate.
• Email channel failure: hard bounce, domain rejects, mailbox full, spam filtering.
• Answerability failure: connects but no human answers; delivered but no reply.

Identity problems require record-level remediation. Channel problems require verification/refresh and suppression. Answerability problems require better timing, sequencing, and role-based messaging.

Step 4: Treat recency as a first-class field

Recency is how recently the contact channel was observed as working. In recruiting, recency is what keeps “accurate last quarter” from becoming “dead this week.” Put a date on it.

• Store last_verified_phone_date and last_verified_email_date (or equivalent) per record.
• Store verification_method (observed outreach outcome vs. validation tool).
• Store source and source_date so you can compare decay by source.

Step 5: Decide what “good enough” means for your workflow

“Accurate” depends on what you’re trying to do:

• High-urgency outreach: prioritize phone channel validity and connect rate to compress time-to-first-conversation.
• Pipeline building: prioritize email deliverability and reply rate to scale touches without burning call blocks.
• Ops QA: prioritize identity accuracy and recency to prevent wasted recruiter hours and compliance risk.

Heartbeat.ai is built for this reality: you’re not buying a spreadsheet—you’re buying a workflow you can audit and improve, including ranked mobile numbers by answer probability when you need to prioritize dials.

Diagnostic Table:

Use this to diagnose what “accuracy” problem you actually have. Copy/paste into a QA sheet.

ATS logging fields (minimum viable)

• attempt_type (dial/email)
• attempt_timestamp
• attempt_outcome (connected/failed; delivered/bounced; human_answer/voicemail; reply/no_reply)
• wrong_person_flag (yes/no)
• channel_used (mobile/direct dial/main; work/personal email)
• source (vendor/list/referral/etc.)
• recency_date (last verified)

Weighted Checklist:

Evaluate a dataset or provider without getting trapped in a single “accuracy %.” Score each item 0–2, multiply by weight, and compare totals.

Outreach Templates:

These templates are designed to generate measurable outcomes you can attribute to data quality (connect/answer/deliverability), not just “activity.”

Template 1: Phone opener (when you get a human answer)

Goal: confirm identity fast, then ask permission to continue.

Script: “Hi Dr. [Last Name]—this is [Name]. Quick check: is this still your best number for recruiting outreach? If not, what is? I’ll be brief—do you have 30 seconds?”

• Log outcomes: human_answer (yes/no), wrong_person (yes/no), best_number_confirmed (yes/no), updated_number (captured/not).

Template 2: Email (deliverability + identity confirmation)

Subject: “Quick confirmation”

Body: “Dr. [Last Name]—I recruit for [Org]. Before I send details, can you confirm this is the best email for recruiting messages? If not, what should I use?”

• Log outcomes: delivered/bounced, reply/no_reply, updated_email (captured/not).

Template 3: Follow-up (when you suspect the wrong channel)

Subject: “Best way to reach you”

Body: “I tried calling and may have caught you at a bad time. What’s the best way to reach you for a 2-minute recruiting question—phone or email?”

Common pitfalls

• Using one blended “accuracy” number. If you don’t split identity vs channel validity vs answerability, you’ll spend time and budget fixing the wrong layer.
• Confusing connect rate with answer rate. A low connect rate is usually a channel/data problem; a low answer rate is often timing and sequencing. See connect rate vs answer rate (definitions and how to use them).
• Optimizing for replies before deliverability. If you only look at replies, you can miss that you’re not reaching inboxes. Start with deliverability and bounce rate, then optimize messaging.
• Ignoring recency. “Accurate” without a date is a workflow risk. Recency lets ops forecast decay and schedule refreshes.
• Over-calling the same stale number. Repeated failed dials burn recruiter time and can create compliance risk. Use suppression lists and rotate channels.

How to improve results

1) Implement the MEASUREMENT_FORMULA worksheet (uniqueness hook)

Turn “accuracy” into a weekly ops report. Keep it simple so it survives real recruiter workflows.

• Phone Channel Validity (per 100 dials) = (connected calls / total dials) × 100
• Phone Answerability (per 100 connected calls) = (human answers / connected calls) × 100
• Email Deliverability (per 100 sent emails) = (delivered emails / sent emails) × 100
• Email Bounce (per 100 sent emails) = (bounced emails / sent emails) × 100
• Email Reply (per 100 delivered emails) = (replies / delivered emails) × 100

Measurement instructions (required): run the worksheet by source and by recency band (for example: verified in the last 30/60/90 days). If a source looks fine overall but collapses in older recency bands, you don’t have a sourcing problem—you have a refresh problem.

2) Fix the highest-leverage failure mode first

• If connect rate is low: prioritize phone validation/refresh and suppress known bad numbers. See phone validation for provider direct dials.
• If answer rate is low but connect rate is fine: change call windows and sequencing; measure answer rate by hour and day.
• If deliverability is low: clean lists, verify addresses, and improve sending hygiene before you scale volume.

3) Build suppression and refresh into the workflow

Accuracy decays. Treat suppression (don’t retry known bad channels) and refresh (re-verify channels on a schedule) as part of your operating system, not a one-time cleanup.

• Suppress: hard bounces, disconnected numbers, wrong-person confirmations.
• Refresh: records with high value but old recency dates; prioritize by hiring urgency.

4) Use a two-channel rule for high-value prospects

For candidates you truly care about, don’t bet on one channel. Pair a dial attempt with a deliverable email attempt and measure both. This reduces the chance that a single stale field blocks the conversation.

Legal and ethical use

Recruiting outreach has real compliance constraints. Build your process so it’s respectful and auditable:

• Honor opt-outs immediately and maintain suppression lists.
• Don’t misrepresent who you are or why you’re contacting someone.
• Be careful with calling/texting rules and consent requirements. For U.S. phone outreach baseline context, review the FCC’s TCPA overview: https://www.fcc.gov/general/telephone-consumer-protection-act-1991-tcpa.

Heartbeat.ai supports legitimate recruiting workflows; you are responsible for complying with applicable laws and policies.

Evidence and trust notes

Trust comes from transparent definitions, measurement, and repeatable QA—not marketing claims. References used in this article:

• How we define and validate outcomes: Heartbeat trust methodology.
• Deliverability monitoring (operational signal for inbox health): https://postmaster.google.com/.
• Deliverability basics (what affects delivery and bounces): https://support.google.com/a/answer/81126?hl=en.

Observed typicals are based on first-attempt outcomes in real outreach workflows; your results vary by segment, message, and recency.

We do not claim an accuracy guarantee or guaranteed deliverability. Treat any dataset as something you continuously measure and refresh.

FAQs

Is contact data accuracy the same as connect rate?

No. Connect rate is a phone metric: Connect Rate = connected calls / total dials (per 100 dials). Contact data accuracy is broader and must specify channel plus identity correctness.

What’s the difference between connect rate and answer rate?

Connect rate measures whether the call reaches a live line. Answer rate measures whether a human answers: Answer Rate = human answers / connected calls (per 100 connected calls). More detail: connect rate vs answer rate.

How do I define email accuracy without mixing it up with replies?

Define email accuracy as deliverability: Deliverability Rate = delivered emails / sent emails (per 100 sent emails). Replies are separate: Reply Rate = replies / delivered emails (per 100 delivered emails).

What should I track in my ATS to measure accuracy fast?

Track attempt_type, attempt_outcome, wrong_person_flag, channel_used, source, and last-verified dates. Then report connect/answer/deliverability/bounce/reply per 100 attempts by source and recency band.

How does recency affect contact data accuracy?

Recency is the “freshness” of a phone/email field. Older fields decay and drive failed dials and bounces. Put a last-verified date on each channel so you can refresh before recruiters waste cycles.

Next steps

• If you need a clean definition for phone outcomes, use: connect rate vs answer rate.
• If your bottleneck is phone reachability, review: phone validation for provider direct dials.
• Ready to test with your own attempts? 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.