{"id":54196,"date":"2026-02-01T12:37:22","date_gmt":"2026-02-01T18:37:22","guid":{"rendered":"https:\/\/heartbeat.ai\/healthcare\/what-is-contact-data-accuracy\/"},"modified":"2026-08-28T09:11:44","modified_gmt":"2026-08-28T14:11:44","slug":"what-is-contact-data-accuracy","status":"publish","type":"post","link":"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/what-is-contact-data-accuracy\/","title":{"rendered":"What Is Contact Data Accuracy? A Recruiter Definition You Can Measure"},"content":{"rendered":"<p class=\"article-last-updated\"><strong>Last updated:<\/strong> August 28, 2026<\/p>\n<p><strong>Ben Argeband, Founder &amp; CEO of Heartbeat.ai<\/strong> \u2014 a measurable, recruiter-friendly framework with a copy\/paste scorecard.<\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\r\n<div class=\"ez-toc-title-container\">\r\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">What\u2019s on this page:<\/p>\r\n<span class=\"ez-toc-title-toggle\"><\/span><\/div>\r\n<nav><ul class='ez-toc-list ez-toc-list-level-1' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/what-is-contact-data-accuracy\/#Who_this_is_for\" >Who this is for<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/what-is-contact-data-accuracy\/#Quick_Answer\" >Quick Answer<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/what-is-contact-data-accuracy\/#Framework_identity_channel_validity_and_answerability\" >Framework: identity, channel validity, and answerability<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/what-is-contact-data-accuracy\/#Step-by-step_method\" >Step-by-step method<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/what-is-contact-data-accuracy\/#Step_1_Use_channel-specific_definitions\" >Step 1: Use channel-specific definitions<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/what-is-contact-data-accuracy\/#Step_2_Instrument_%E2%80%9Cper_100_attempts%E2%80%9D_in_your_ATSCRM\" >Step 2: Instrument &#8220;per 100 attempts&#8221; in your ATS\/CRM<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/what-is-contact-data-accuracy\/#Step_3_Separate_identity_errors_from_channel_errors\" >Step 3: Separate identity errors from channel errors<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/what-is-contact-data-accuracy\/#Step_4_Treat_recency_as_a_first-class_field\" >Step 4: Treat recency as a first-class field<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/what-is-contact-data-accuracy\/#Step_5_Decide_what_%E2%80%9Cgood_enough%E2%80%9D_means_for_your_workflow\" >Step 5: Decide what &#8220;good enough&#8221; means for your workflow<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/what-is-contact-data-accuracy\/#Diagnostic_table\" >Diagnostic table<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/what-is-contact-data-accuracy\/#Weighted_checklist\" >Weighted checklist<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/what-is-contact-data-accuracy\/#Outreach_templates\" >Outreach templates<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/what-is-contact-data-accuracy\/#Template_1_Phone_opener_when_you_get_a_human_answer\" >Template 1: Phone opener (when you get a human answer)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/what-is-contact-data-accuracy\/#Template_2_Email_deliverability_identity_confirmation\" >Template 2: Email (deliverability + identity confirmation)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/what-is-contact-data-accuracy\/#Template_3_Follow-up_when_you_suspect_the_wrong_channel\" >Template 3: Follow-up (when you suspect the wrong channel)<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/what-is-contact-data-accuracy\/#Common_pitfalls\" >Common pitfalls<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/what-is-contact-data-accuracy\/#How_to_improve_results\" >How to improve results<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/what-is-contact-data-accuracy\/#1_Build_a_weekly_measurement_worksheet\" >1) Build a weekly measurement worksheet<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-19\" href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/what-is-contact-data-accuracy\/#2_Fix_the_highest-leverage_failure_mode_first\" >2) Fix the highest-leverage failure mode first<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-20\" href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/what-is-contact-data-accuracy\/#3_Build_suppression_and_refresh_into_the_workflow\" >3) Build suppression and refresh into the workflow<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-21\" href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/what-is-contact-data-accuracy\/#4_Use_a_two-channel_rule_for_high-value_prospects\" >4) Use a two-channel rule for high-value prospects<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-22\" href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/what-is-contact-data-accuracy\/#Legal_and_ethical_use\" >Legal and ethical use<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-23\" href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/what-is-contact-data-accuracy\/#Evidence_and_trust_notes\" >Evidence and trust notes<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-24\" href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/what-is-contact-data-accuracy\/#FAQs\" >FAQs<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-25\" href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/what-is-contact-data-accuracy\/#Is_contact_data_accuracy_the_same_as_connect_rate\" >Is contact data accuracy the same as connect rate?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-26\" href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/what-is-contact-data-accuracy\/#Whats_the_difference_between_connect_rate_and_answer_rate\" >What&#8217;s the difference between connect rate and answer rate?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-27\" href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/what-is-contact-data-accuracy\/#How_do_I_define_email_accuracy_without_mixing_it_up_with_replies\" >How do I define email accuracy without mixing it up with replies?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-28\" href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/what-is-contact-data-accuracy\/#What_should_I_track_in_my_ATS_to_measure_accuracy_fast\" >What should I track in my ATS to measure accuracy fast?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-29\" href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/what-is-contact-data-accuracy\/#How_does_recency_affect_contact_data_accuracy\" >How does recency affect contact data accuracy?<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-30\" href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/what-is-contact-data-accuracy\/#Next_steps\" >Next steps<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-31\" href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/what-is-contact-data-accuracy\/#About_the_Author\" >About the Author<\/a><\/li><\/ul><\/nav><\/div>\r\n<h2><span class=\"ez-toc-section\" id=\"Who_this_is_for\"><\/span>Who this is for<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Recruiters and ops leaders who need to evaluate contact data quickly, decide whether it&#8217;s usable this week, and instrument the workflow so &#8220;accuracy&#8221; becomes a measurable ops lever instead of a debate.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Quick_Answer\"><\/span>Quick Answer<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<dl>\n<dt>Core Answer<\/dt>\n<dd>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.<\/dd>\n<dt>Why it matters<\/dt>\n<dd>Recruiting teams that treat &#8220;accuracy&#8221; as one blended number end up fixing the wrong problem \u2014 spending budget on new records when the real issue is stale phone fields, poor timing, or weak messaging.<\/dd>\n<dt>Best For<\/dt>\n<dd>Recruiters and ops leaders who want to evaluate contact data without jargon and build a repeatable QA process around it.<\/dd>\n<\/dl>\n<blockquote>\n<p><strong>Compliance &amp; Safety<\/strong><\/p>\n<p>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.<\/p>\n<\/blockquote>\n<h2><span class=\"ez-toc-section\" id=\"Framework_identity_channel_validity_and_answerability\"><\/span>Framework: identity, channel validity, and answerability<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Teams argue about &#8220;accuracy&#8221; because they&#8217;re mixing three different checks. In recruiting ops, you need all three, measured separately, so you can fix the right failure mode instead of guessing.<\/p>\n<ul>\n<li><strong>Identity<\/strong>: the record belongs to the right person \u2014 a correct match between name, credential, and employer.<\/li>\n<li><strong>Channel validity<\/strong>: the phone number connects to a live line, or the email address delivers.<\/li>\n<li><strong>Answerability<\/strong>: a human answers the connected call, or a delivered email earns a reply.<\/li>\n<\/ul>\n<p>Throughout this article, &#8220;accuracy&#8221; is an observed, per-attempt outcome by channel \u2014 per 100 dials and per 100 sent emails. Engagement is tracked separately so you don&#8217;t blame the data for a messaging problem, or the reverse.<\/p>\n<p>The practical trade-off: you can buy more records, or you can build a workflow that produces more usable attempts. Recruiting throughput depends on the second one.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Step-by-step_method\"><\/span>Step-by-step method<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3><span class=\"ez-toc-section\" id=\"Step_1_Use_channel-specific_definitions\"><\/span>Step 1: Use channel-specific definitions<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Put these definitions in your scorecard so everyone on the team is measuring the same thing.<\/p>\n<ul>\n<li><strong>Contact data accuracy<\/strong>: the percent of outreach attempts where identity is correct and the chosen channel works as intended for that attempt. Always state the channel and the denominator (per 100 dials or per 100 sent emails).<\/li>\n<li><strong>Identity accuracy<\/strong>: per 100 connected calls, the share that reach the intended person rather than a wrong person. A practical proxy is Wrong-person rate = wrong-person connects \/ connected calls.<\/li>\n<li><strong>Mobile accuracy<\/strong>: per 100 dials to a &#8220;mobile&#8221; field, the share that connects to the intended person&#8217;s mobile line \u2014 not disconnected, not a wrong person, not a business main line. This is channel validity; answerability is separate.<\/li>\n<li><strong>Email accuracy<\/strong>: per 100 emails sent to an address, the share that is delivered rather than bounced. Replies are a different metric.<\/li>\n<li><strong>Deliverability rate<\/strong> = delivered emails \/ sent emails (per 100 sent emails).<\/li>\n<\/ul>\n<p>Related metrics that keep you from fixing the wrong layer:<\/p>\n<ul>\n<li>Connect rate = connected calls \/ total dials (per 100 dials). &#8220;Connected&#8221; means the call reached a live line \u2014 human, voicemail, IVR, or gatekeeper \u2014 not a failed attempt.<\/li>\n<li>Answer rate = human answers \/ connected calls (per 100 connected calls).<\/li>\n<li>Bounce rate = bounced emails \/ sent emails (per 100 sent emails).<\/li>\n<li>Reply rate = replies \/ delivered emails (per 100 delivered emails).<\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"Step_2_Instrument_%E2%80%9Cper_100_attempts%E2%80%9D_in_your_ATSCRM\"><\/span>Step 2: Instrument &#8220;per 100 attempts&#8221; in your ATS\/CRM<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>If you don&#8217;t log attempts, you end up arguing about anecdotes \u2014 &#8220;this data is bad&#8221; \u2014 instead of pointing at a measurable bottleneck.<\/p>\n<p>Log every dial and email as an attempt, then calculate each metric per 100 attempts for a defined time window and segment (specialty, geography, source, campaign).<\/p>\n<p><strong>Worked example (fill in your own numbers; don&#8217;t guess):<\/strong><\/p>\n<ul>\n<li>Per 100 dials: __ connected calls; per 100 connected calls: __ human answers; __ wrong-person connects<\/li>\n<li>Per 100 sent emails: __ delivered; __ bounces; per 100 delivered emails: __ replies<\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"Step_3_Separate_identity_errors_from_channel_errors\"><\/span>Step 3: Separate identity errors from channel errors<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>When a recruiter says &#8220;bad data,&#8221; it usually means one of these:<\/p>\n<ul>\n<li><strong>Identity mismatch<\/strong>: wrong person, outdated employer, duplicate profiles merged incorrectly.<\/li>\n<li><strong>Phone channel failure<\/strong>: disconnected number, wrong number, business main line, or a call-routing tree that never reaches the candidate.<\/li>\n<li><strong>Email channel failure<\/strong>: hard bounce, domain rejection, full mailbox, or spam filtering.<\/li>\n<li><strong>Answerability failure<\/strong>: the call connects but no human answers; the email delivers but nobody replies.<\/li>\n<\/ul>\n<p>Identity problems require record-level remediation. Channel problems require verification, refresh, and suppression. Answerability problems require better timing, sequencing, and role-based messaging \u2014 not a new data source.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Step_4_Treat_recency_as_a_first-class_field\"><\/span>Step 4: Treat recency as a first-class field<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Recency is how recently a contact channel was observed as working. It&#8217;s what keeps &#8220;accurate last quarter&#8221; from becoming &#8220;dead this week.&#8221; Put a date on it.<\/p>\n<ul>\n<li>Store last_verified_phone_date and last_verified_email_date (or equivalent) per record.<\/li>\n<li>Store verification_method (observed outreach outcome vs. a validation tool).<\/li>\n<li>Store source and source_date so you can compare decay rates across sources.<\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"Step_5_Decide_what_%E2%80%9Cgood_enough%E2%80%9D_means_for_your_workflow\"><\/span>Step 5: Decide what &#8220;good enough&#8221; means for your workflow<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>&#8220;Accurate&#8221; depends on what you&#8217;re trying to do:<\/p>\n<ul>\n<li><strong>High-urgency outreach<\/strong>: prioritize phone channel validity and connect rate to compress time-to-first-conversation.<\/li>\n<li><strong>Pipeline building<\/strong>: prioritize email deliverability and reply rate to scale touches without burning call blocks.<\/li>\n<li><strong>Ops QA<\/strong>: prioritize identity accuracy and recency to prevent wasted recruiter hours and reduce compliance risk.<\/li>\n<\/ul>\n<p>Heartbeat.ai is built around this reality: you&#8217;re not buying a static spreadsheet, you&#8217;re buying a workflow you can audit and improve \u2014 including ranked mobile numbers by answer probability when you need to prioritize which candidates to dial first.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Diagnostic_table\"><\/span>Diagnostic table<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Use this to diagnose what &#8220;accuracy&#8221; problem you actually have. Copy it into a QA sheet.<\/p>\n<div class=\"table-scroll\" style=\"overflow:auto;-webkit-overflow-scrolling:touch;width:100%\">\n<table class=\"separated-content\">\n<thead>\n<tr>\n<th>Symptom in workflow<\/th>\n<th>Likely root cause<\/th>\n<th>What to measure (per 100 attempts)<\/th>\n<th>Fix<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Wrong-person pickups<\/td>\n<td>Identity mismatch<\/td>\n<td>Wrong-person rate = wrong-person connects \/ connected calls<\/td>\n<td>Tighten identity matching rules; require credential + employer cross-check; dedupe<\/td>\n<\/tr>\n<tr>\n<td>Many dials fail (disconnected\/invalid)<\/td>\n<td>Phone channel validity issue<\/td>\n<td>Connect Rate = connected calls \/ total dials<\/td>\n<td>Refresh phone fields; prioritize recent verification; suppress known bad numbers<\/td>\n<\/tr>\n<tr>\n<td>Calls connect but nobody answers<\/td>\n<td>Answerability\/timing issue<\/td>\n<td>Answer Rate = human answers \/ connected calls<\/td>\n<td>Change call windows and sequencing; measure answer rate by hour and day<\/td>\n<\/tr>\n<tr>\n<td>Emails bounce<\/td>\n<td>Email channel validity issue<\/td>\n<td>Bounce Rate = bounced emails \/ sent emails<\/td>\n<td>Verify emails; suppress hard bounces; improve sending hygiene<\/td>\n<\/tr>\n<tr>\n<td>Emails deliver but no replies<\/td>\n<td>Targeting\/message issue<\/td>\n<td>Reply Rate = replies \/ delivered emails<\/td>\n<td>Rewrite subject lines; tighten persona; add a clear ask; adjust cadence<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p><strong>ATS logging fields (minimum viable)<\/strong><\/p>\n<ul>\n<li>attempt_type (dial\/email)<\/li>\n<li>attempt_timestamp<\/li>\n<li>attempt_outcome (connected\/failed; delivered\/bounced; human_answer\/voicemail; reply\/no_reply)<\/li>\n<li>wrong_person_flag (yes\/no)<\/li>\n<li>channel_used (mobile\/direct dial\/main; work\/personal email)<\/li>\n<li>source (vendor\/list\/referral\/etc.)<\/li>\n<li>recency_date (last verified)<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"Weighted_checklist\"><\/span>Weighted checklist<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Evaluate a dataset or provider without getting trapped in a single &#8220;accuracy %.&#8221; Score each item 0\u20132, multiply by weight, and compare totals.<\/p>\n<div class=\"table-scroll\" style=\"overflow:auto;-webkit-overflow-scrolling:touch;width:100%\">\n<table class=\"separated-content\">\n<thead>\n<tr>\n<th>Category<\/th>\n<th>Check<\/th>\n<th>Weight<\/th>\n<th>Score (0\u20132)<\/th>\n<th>Notes<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Identity<\/td>\n<td>Clear identity resolution rules (name + credential + employer) and dedupe<\/td>\n<td>3<\/td>\n<td><\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td>Recency<\/td>\n<td>Each phone\/email has a last-verified date you can export<\/td>\n<td>3<\/td>\n<td><\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td>Phone validity<\/td>\n<td>Phone fields labeled (mobile vs direct vs main) and suppression for known bad numbers<\/td>\n<td>2<\/td>\n<td><\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td>Email validity<\/td>\n<td>Email verification + bounce handling workflow<\/td>\n<td>2<\/td>\n<td><\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td>Measurement<\/td>\n<td>Supports per-100 attempt reporting (connect, answer, deliverability, bounce, reply)<\/td>\n<td>3<\/td>\n<td><\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td>Workflow fit<\/td>\n<td>Easy export\/API + ATS field mapping for attempt outcomes<\/td>\n<td>2<\/td>\n<td><\/td>\n<td><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<h2><span class=\"ez-toc-section\" id=\"Outreach_templates\"><\/span>Outreach templates<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>These templates are built to generate outcomes you can attribute to data quality \u2014 connect, answer, deliverability \u2014 rather than just activity.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Template_1_Phone_opener_when_you_get_a_human_answer\"><\/span>Template 1: Phone opener (when you get a human answer)<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>Goal:<\/strong> confirm identity fast, then ask permission to continue.<\/p>\n<p><strong>Script:<\/strong> &#8220;Hi Dr. [Last Name]\u2014this is [Name]. Quick check: is this still your best number for recruiting outreach? If not, what is? I&#8217;ll be brief\u2014do you have 30 seconds?&#8221;<\/p>\n<ul>\n<li>Log outcomes: human_answer (yes\/no), wrong_person (yes\/no), best_number_confirmed (yes\/no), updated_number (captured\/not).<\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"Template_2_Email_deliverability_identity_confirmation\"><\/span>Template 2: Email (deliverability + identity confirmation)<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>Subject:<\/strong> &#8220;Quick confirmation&#8221;<\/p>\n<p><strong>Body:<\/strong> &#8220;Dr. [Last Name]\u2014I recruit for [Org]. Before I send details, can you confirm this is the best email for recruiting messages? If not, what should I use?&#8221;<\/p>\n<ul>\n<li>Log outcomes: delivered\/bounced, reply\/no_reply, updated_email (captured\/not).<\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"Template_3_Follow-up_when_you_suspect_the_wrong_channel\"><\/span>Template 3: Follow-up (when you suspect the wrong channel)<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>Subject:<\/strong> &#8220;Best way to reach you&#8221;<\/p>\n<p><strong>Body:<\/strong> &#8220;I tried calling and may have caught you at a bad time. What&#8217;s the best way to reach you for a 2-minute recruiting question\u2014phone or email?&#8221;<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Common_pitfalls\"><\/span>Common pitfalls<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li><strong>Using one blended &#8220;accuracy&#8221; number.<\/strong> If you don&#8217;t split identity, channel validity, and answerability, you&#8217;ll spend time and budget fixing the wrong layer.<\/li>\n<li><strong>Confusing connect rate with answer rate.<\/strong> A low connect rate usually points to a channel or data problem; a low answer rate is often timing and sequencing. See <a href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/connect-rate-vs-answer-rate\/\">connect rate vs answer rate<\/a>.<\/li>\n<li><strong>Optimizing for replies before deliverability.<\/strong> If you only look at replies, you can miss that you&#8217;re not reaching inboxes at all. Start with deliverability and bounce rate, then optimize messaging.<\/li>\n<li><strong>Ignoring recency.<\/strong> &#8220;Accurate&#8221; without a date attached is a workflow risk. Recency lets ops forecast decay and schedule refreshes proactively.<\/li>\n<li><strong>Over-calling the same stale number.<\/strong> Repeated failed dials burn recruiter time and can create compliance exposure. Use suppression lists and rotate channels instead.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"How_to_improve_results\"><\/span>How to improve results<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3><span class=\"ez-toc-section\" id=\"1_Build_a_weekly_measurement_worksheet\"><\/span>1) Build a weekly measurement worksheet<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Turn &#8220;accuracy&#8221; into a weekly ops report. Keep it simple enough to survive real recruiter workflows.<\/p>\n<ul>\n<li>Phone channel validity (per 100 dials) = (connected calls \/ total dials) \u00d7 100<\/li>\n<li>Phone answerability (per 100 connected calls) = (human answers \/ connected calls) \u00d7 100<\/li>\n<li>Email deliverability (per 100 sent emails) = (delivered emails \/ sent emails) \u00d7 100<\/li>\n<li>Email bounce rate (per 100 sent emails) = (bounced emails \/ sent emails) \u00d7 100<\/li>\n<li>Email reply rate (per 100 delivered emails) = (replies \/ delivered emails) \u00d7 100<\/li>\n<\/ul>\n<p>Run the worksheet by source and by recency band \u2014 for example, verified in the last 30, 60, or 90 days. If a source looks fine overall but collapses in older recency bands, that&#8217;s not a sourcing problem. It&#8217;s a refresh problem.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"2_Fix_the_highest-leverage_failure_mode_first\"><\/span>2) Fix the highest-leverage failure mode first<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ul>\n<li>If connect rate is low: prioritize phone validation and refresh, and suppress known bad numbers. See <a href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/phone-validation-for-provider-direct-dials\/\">phone validation for provider direct dials<\/a>.<\/li>\n<li>If answer rate is low but connect rate is fine: change call windows and sequencing, and measure answer rate by hour and day.<\/li>\n<li>If deliverability is low: clean lists and verify addresses before you scale sending volume.<\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"3_Build_suppression_and_refresh_into_the_workflow\"><\/span>3) Build suppression and refresh into the workflow<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Accuracy decays over time. Treat suppression \u2014 not retrying known bad channels \u2014 and refresh \u2014 re-verifying channels on a schedule \u2014 as part of your operating system, not a one-time cleanup project.<\/p>\n<ul>\n<li>Suppress: hard bounces, disconnected numbers, wrong-person confirmations.<\/li>\n<li>Refresh: high-value records with old recency dates, prioritized by hiring urgency.<\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"4_Use_a_two-channel_rule_for_high-value_prospects\"><\/span>4) Use a two-channel rule for high-value prospects<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>For candidates you truly care about, don&#8217;t bet on one channel. Pair a dial attempt with a deliverable email attempt and measure both. That way a single stale field doesn&#8217;t block the conversation entirely.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Legal_and_ethical_use\"><\/span>Legal and ethical use<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Recruiting outreach carries real compliance constraints, and the rules around automated calls and texts have been shifting. Build your process so it&#8217;s respectful and auditable regardless of which interpretation ultimately prevails:<\/p>\n<ul>\n<li>Honor opt-outs immediately and maintain suppression lists.<\/li>\n<li>Don&#8217;t misrepresent who you are or why you&#8217;re contacting someone.<\/li>\n<li>Be careful with calling and texting consent requirements, and confirm current guidance before running automated campaigns. For U.S. phone outreach baseline context, review the FCC&#8217;s TCPA overview: <a href=\"https:\/\/www.fcc.gov\/general\/telephone-consumer-protection-act-1991-tcpa\">https:\/\/www.fcc.gov\/general\/telephone-consumer-protection-act-1991-tcpa<\/a>.<\/li>\n<\/ul>\n<p>Heartbeat.ai supports legitimate recruiting workflows; you are responsible for complying with applicable laws and policies.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Evidence_and_trust_notes\"><\/span>Evidence and trust notes<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Trust comes from transparent definitions, measurement, and repeatable QA \u2014 not marketing claims. References used in this article:<\/p>\n<ul>\n<li>How we define and validate outcomes: <a href=\"http:\/\/heartbeat.ai\/resources\/trust-methodology\/\">Heartbeat trust methodology<\/a>.<\/li>\n<li>Deliverability monitoring (operational signal for inbox health): <a href=\"https:\/\/postmaster.google.com\/\">https:\/\/postmaster.google.com\/<\/a>.<\/li>\n<li>Deliverability basics (what affects delivery and bounces): <a href=\"https:\/\/support.google.com\/a\/answer\/81126?hl=en\">https:\/\/support.google.com\/a\/answer\/81126?hl=en<\/a>.<\/li>\n<\/ul>\n<p>Results vary by segment, message quality, and recency of the underlying data. Treat any dataset as something you continuously measure and refresh, not a fixed asset. We do not claim an accuracy guarantee or guaranteed deliverability.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"FAQs\"><\/span>FAQs<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3><span class=\"ez-toc-section\" id=\"Is_contact_data_accuracy_the_same_as_connect_rate\"><\/span>Is contact data accuracy the same as connect rate?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>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.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Whats_the_difference_between_connect_rate_and_answer_rate\"><\/span>What&#8217;s the difference between connect rate and answer rate?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>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: <a href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/connect-rate-vs-answer-rate\/\">connect rate vs answer rate<\/a>.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"How_do_I_define_email_accuracy_without_mixing_it_up_with_replies\"><\/span>How do I define email accuracy without mixing it up with replies?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>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).<\/p>\n<h3><span class=\"ez-toc-section\" id=\"What_should_I_track_in_my_ATS_to_measure_accuracy_fast\"><\/span>What should I track in my ATS to measure accuracy fast?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Track attempt_type, attempt_outcome, wrong_person_flag, channel_used, source, and last-verified dates. Then report connect, answer, deliverability, bounce, and reply rates per 100 attempts by source and recency band.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"How_does_recency_affect_contact_data_accuracy\"><\/span>How does recency affect contact data accuracy?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Recency is the &#8220;freshness&#8221; of a phone or 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 on dead contacts.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Next_steps\"><\/span>Next steps<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li>If you need a clean definition for phone outcomes, use: <a href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/connect-rate-vs-answer-rate\/\">connect rate vs answer rate<\/a>.<\/li>\n<li>If your bottleneck is phone reachability, review: <a href=\"http:\/\/heartbeat.ai\/resources\/data-quality-verification\/phone-validation-for-provider-direct-dials\/\">phone validation for provider direct dials<\/a>.<\/li>\n<li>Ready to test with your own attempts? <a href=\"https:\/\/heartbeat.ai\/signup\">start free search &amp; preview data<\/a>.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"About_the_Author\"><\/span><b>About the Author<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><a href=\"http:\/\/heartbeat.ai\/resources\/author\/ben-argeband\"><span style=\"font-weight: 400;\">Ben Argeband<\/span><\/a><span style=\"font-weight: 400;\"> 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&#8217;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 <\/span><a href=\"https:\/\/www.linkedin.com\/in\/ben-m-argeband-2427a8a3\/\"><span style=\"font-weight: 400;\">LinkedIn<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><br \/>\n<script type=\"application\/ld+json\">{\"@context\":\"https:\/\/schema.org\",\"@type\":\"Article\",\"about\":[\"contact data accuracy\",\"connect rate\",\"answer rate\",\"deliverability\",\"bounce rate\",\"recency\"],\"author\":{\"@type\":\"Person\",\"name\":\"Ben Argeband\"},\"headline\":\"What Is Contact Data Accuracy? 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Put a last-verified date on each channel so you can refresh before recruiters waste cycles.\"},\"name\":\"How does recency affect contact data accuracy?\"}]}<\/script><\/p>","protected":false},"excerpt":{"rendered":"<p>A recruiter-grade definition of contact data accuracy, split by identity, phone, and email, with per-100 attempt measurement and templates.<\/p>","protected":false},"author":5,"featured_media":54195,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_custom_permalink":"data-quality-verification\/what-is-contact-data-accuracy","footnotes":""},"categories":[1],"tags":[],"class_list":["post-54196","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\r\n<title>What Is Contact Data Accuracy? 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