\n| Slow ramp to first qualified conversation<\/td>\n | Track time from list build to first clinician-confirmed connect using the same cadence<\/td>\n | Time to first clinician-confirmed connect<\/td>\n | Gets a clinician-confirmed conversation sooner without increasing opt-outs<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n Conflicting results quick read:<\/strong><\/p>\n\n- If connect rate improves but wrong-person rate worsens, tighten identity matching and rerun Week 2 before you scale.<\/li>\n
- If email bounces rise, stop scaling volume and fix suppression and verification first.<\/li>\n
- If both sources perform the same, decide based on recruiter hours saved and workflow fit.<\/li>\n<\/ul>\n
<\/span>Step-by-step method<\/span><\/h2>\n<\/span>Step 1: Define your pilot so it can pass or fail<\/span><\/h3>\nPilot definition:<\/strong> a time-boxed test with a fixed list size, fixed outreach sequence, and pre-defined pass\/fail thresholds for connect rate and wrong-person rate.<\/p>\nPick one segment you actually recruit (one specialty, one geography band, one setting). Mixing segments hides failure modes.<\/p>\n <\/span>Step 2: Build two matched lists (test vs control)<\/span><\/h3>\n\n- Test group:<\/strong> contacts sourced from Seamless.AI for the segment.<\/li>\n
- Control group:<\/strong> contacts sourced from your current method (CRM, internal research, referrals, or another vendor).<\/li>\n<\/ul>\n
Keep the lists similar in size and difficulty. If they are not equal, normalize results per 100 dials and per 100 delivered emails.<\/p>\n Field parity checklist (use the same columns for both sources):<\/strong><\/p>\n\n- Full name (as sourced)<\/li>\n
- Specialty (your target specialty label)<\/li>\n
- Organization \/ facility name<\/li>\n
- City\/state (or service area)<\/li>\n
- Phone number(s) (store one per row if possible)<\/li>\n
- Email address (if used)<\/li>\n
- Source tag (Seamless.AI vs control)<\/li>\n
- Notes field for identity conflicts (e.g., \u201ctwo clinicians with same name\u201d)<\/li>\n<\/ul>\n
<\/span>Step 3: Standardize outreach so the source is the only variable<\/span><\/h3>\n\n- Calls:<\/strong> same caller(s), same number of attempts per contact, same local-time windows.<\/li>\n
- Email:<\/strong> same subject pattern, same follow-up timing, same sending domain.<\/li>\n
- Suppression:<\/strong> remove opt-outs, duplicates, and anyone already in process before you start.<\/li>\n<\/ul>\n
<\/span>Step 4: Set dispositions in your dialer\/CRM (so your metrics are real)<\/span><\/h3>\nCreate dispositions that let you separate reachability from accuracy. Use these exact buckets (or map to equivalents):<\/p>\n \n- Connected \u2014 Clinician (confirmed)<\/strong><\/li>\n
- Connected \u2014 Wrong person<\/strong> (not the clinician you intended)<\/li>\n
- Connected \u2014 Gatekeeper\/office<\/strong> (no clinician reached)<\/li>\n
- Voicemail<\/strong><\/li>\n
- No answer<\/strong><\/li>\n
- Bad number<\/strong><\/li>\n
- Do not contact \/ Opt-out<\/strong><\/li>\n<\/ul>\n
<\/span>Step 5: Track the deciding metrics (with canonical definitions)<\/span><\/h3>\nConnect Rate<\/strong> = connected calls \/ total dials (report per 100 dials).<\/p>\nAnswer Rate<\/strong> = human answers \/ connected calls (report per 100 connected calls).<\/p>\nWrong-person rate<\/strong> = wrong person confirmations \/ connected calls (report per 100 connected calls). Count \u201cthis isn\u2019t Dr. X,\u201d \u201cwrong specialty,\u201d \u201cno longer here,\u201d and \u201cthis is the office manager\u201d when you were aiming for the clinician.<\/p>\nIf email is part of your motion, track:<\/p>\n \n- Deliverability Rate<\/strong> = delivered emails \/ sent emails (per 100 sent).<\/li>\n
- Bounce Rate<\/strong> = bounced emails \/ sent emails (per 100 sent).<\/li>\n
- Reply Rate<\/strong> = replies \/ delivered emails (per 100 delivered).<\/li>\n<\/ul>\n
<\/span>Step 6: Decide pass\/fail using thresholds you set before the test<\/span><\/h3>\nWrite your decision rules before you start. Examples:<\/p>\n |
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<\/p>\n