\n| What should I optimize for?<\/td>\n | Refresh cadence and connectability metrics<\/td>\n | Record count and \u201ccoverage\u201d claims that don\u2019t translate to connects<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n Buyer scorecard note:<\/strong> Require reporting on connect rate per 100 dials<\/strong> and deliverability per 100 sends<\/strong> for your pilot cohort. Don\u2019t accept a generic \u201caccuracy\u201d claim without definitions.<\/p>\n<\/span>Step-by-step method<\/span><\/h2>\n<\/span>Step 1: Build your target cohort using stable identity first<\/span><\/h3>\nStart with identity, not contact fields. In healthcare, the stable anchor is NPI<\/strong>. Use NPI to de-duplicate and to keep outreach tied to the correct physician across locations.<\/p>\n\n- Define the cohort: specialty, geography, setting, and must-have constraints.<\/li>\n
- Attach NPI and run license matching<\/strong> where state alignment matters.<\/li>\n
- Only then evaluate phone\/email fields for outreach.<\/li>\n<\/ul>\n
<\/span>Step 2: Treat phone and email as volatile fields (plan for decay)<\/span><\/h3>\nNPI is stable; phone\/email are volatile. Physicians change groups, rotate coverage, and switch inboxes. Your workflow should assume decay and rely on verification + refresh.<\/p>\n \n- Prefer phone data that is line tested<\/strong> and tagged by recency<\/strong>.<\/li>\n
- Prefer email data with validation, bounce handling, and suppression.<\/li>\n
- Ensure opt-outs persist across recruiters and campaigns.<\/li>\n<\/ul>\n
Heartbeat.ai includes ranked mobile numbers by answer probability<\/strong> so recruiters can start with the most likely-to-connect option when time is tight.<\/p>\n<\/span>Step 3: Run a pilot that mirrors your real recruiting workflow<\/span><\/h3>\nDon\u2019t pilot on easy segments. Pilot on the cohort you actually struggle to reach.<\/p>\n \n- Pull 100 physicians you would genuinely recruit this month.<\/li>\n
- Have one recruiter run a consistent call\/email sequence for 3\u20135 business days.<\/li>\n
- Track outcomes using the metric definitions below so you can compare apples-to-apples.<\/li>\n<\/ol>\n
The trade-off is\u2026 a smaller, fresher cohort with verified fields usually beats a massive stale export because it reduces wasted attempts and speeds up first conversations.<\/p>\n <\/span>Step 4: Use canonical metric definitions (so your reporting is real)<\/span><\/h3>\nUse these definitions consistently across vendors, tools, and recruiters:<\/p>\n \n- Connect Rate<\/strong> = connected calls \/ total dials (report per 100 dials).<\/li>\n
- Answer Rate<\/strong> = human answers \/ connected calls (report per 100 connected calls).<\/li>\n
- Deliverability Rate<\/strong> = delivered emails \/ sent emails (report per 100 sent emails).<\/li>\n
- Bounce Rate<\/strong> = bounced emails \/ sent emails (report per 100 sent emails).<\/li>\n
- Reply Rate<\/strong> = replies \/ delivered emails (report per 100 delivered emails).<\/li>\n<\/ul>\n
Mobile accuracy (definition):<\/strong> the share of first-listed mobile numbers that are valid and reach the intended physician when dialed, measured on a defined sample and time window.<\/p>\nEmail accuracy (definition):<\/strong> the share of emails that are valid for the intended physician and result in delivery (not bounce) when sent under normal sending practices, measured on a defined sample and time window.<\/p>\nDeliverability (definition):<\/strong> the share of sent emails that are delivered (not bounced). Delivered does not guarantee inbox placement.<\/p>\n<\/span>Step 5: Operationalize suppression and opt-out as a system<\/span><\/h3>\nOpt-out cannot be a \u201ccampaign setting.\u201d It must be enforced across your entire team and toolchain.<\/p>\n Ops requirement:<\/strong> Your CRM\/ATS should store opt-out and suppression outcomes at the physician identity level (NPI-linked where possible) so a new recruiter doesn\u2019t restart outreach on a previously suppressed record.<\/p>\n\n- Centralize opt-out<\/strong> and suppression lists.<\/li>\n
- Write back outcomes (wrong number, best call window, gatekeeper notes) to improve future attempts.<\/li>\n
- Segment by setting so you don\u2019t apply the same cadence to every physician.<\/li>\n<\/ul>\n
<\/span>Weighted Checklist:<\/span><\/h2>\nScore each item 0\u20135, multiply by weight, and compare totals across options (including \u201cdo nothing\u201d). This is built for recruiting ops decisions.<\/p>\n |
![\"54043\"](\"http:\/\/hc.heartbeat.ai\/wp-content\/webp-express\/webp-images\/uploads\/2026\/01\/physician-contact-database-b96a8d32.png.webp\")
<\/p>\n