Connect rate definition for recruiters
By Ben Argeband, Founder & CEO of Heartbeat.ai — Keep stable; update-controlled; linkable citation target.
What’s on this page:
Who this is for
This page is for recruiting ops and leaders who need consistent measurement across teams, tools, and time. If you’ve ever had a weekly meeting derailed by “what counts as a connect?” or “are we measuring replies on sent or delivered?”, this is your standard.
Use it as the canonical link target for dashboards, SOPs, and stakeholder updates.
Quick Answer
- Core Answer
- Connect Rate = connected calls / total dials. Report it per 100 dials to separate phone reachability from messaging and keep weekly reporting comparable.
- Key Insight
- Most reporting drift comes from denominator changes. Lock definitions, log the raw events, and publish per-100 rates with raw counts.
- Best For
- Recruiting ops + leaders needing consistent measurement.
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: “Denominator discipline”: pick a denominator and stick to it
Outreach metrics fail when teams mix denominators. One report uses dials, another uses connected calls, and a third uses “attempts” that exclude voicemails. You can’t compare weeks, teams, or vendors if the denominator moves.
- Define each metric with a fixed numerator and denominator.
- Report in a consistent unit (per 100 attempts within each channel, and do not blend channels: phone per 100 dials; email deliverability/bounce per 100 sent emails; email reply metrics per 100 delivered emails).
- Log the event that creates the numerator so the metric is reproducible.
- Separate data quality from execution (reachability vs timing vs targeting vs message-market fit).
Worked example (no guessing): If you place D dials and get C connected calls, Connect Rate = C/D. Report as (C/D) × 100 connected calls per 100 dials, alongside the raw counts (C and D).
Step-by-step method
Step 1: Standardize your event vocabulary (before you touch a dashboard)
Your dialer, email provider, and ATS/CRM won’t agree on naming. Start by listing the events you can actually capture, then map them to stable definitions.
- Phone events: dial placed, connected call, human answer, voicemail, busy, no answer, failed, wrong person confirmed, do-not-contact/opt-out.
- Email events: sent, delivered, bounced (hard/soft), replied, unsubscribed/opt-out, complaint.
- Identity events: person match confidence, wrong-person confirmed, duplicate merged, suppressed.
Decide what counts as an “attempt” per channel. For phone, an attempt is a dial. For email, an attempt is a sent email. Keep channels separate unless you’re intentionally building a blended model.
Step 2: Use canonical metric definitions (formulas + denominators)
These are the canonical definitions for recruiting outreach measurement. They are designed to be implementable in any reporting stack.
| Metric | Canonical definition | Formula | Report as | Primary diagnostic use |
|---|---|---|---|---|
| Connect Rate | Share of dials that result in a connected call (not necessarily a human answer). | connected calls / total dials | Connected calls per 100 dials | Phone reachability + dialer execution |
| Answer Rate | Share of connected calls that are answered by a human. | human answers / connected calls | Human answers per 100 connected calls | Call timing + caller ID trust + targeting |
| Deliverability Rate | Share of sent emails that are delivered (not bounced). | delivered emails / sent emails | Delivered emails per 100 sent emails | List hygiene + domain reputation |
| Bounce Rate | Share of sent emails that bounce (hard + soft). | bounced emails / sent emails | Bounced emails per 100 sent emails | Bad addresses + sending practices |
| Reply Rate | Share of delivered emails that receive a reply. | replies / delivered emails | Replies per 100 delivered emails | Message-market fit + targeting |
| Positive Reply Rate | Share of delivered emails that receive a positive reply (interested, send details, open to talk). | positive replies / delivered emails | Positive replies per 100 delivered emails | Offer fit + targeting precision |
| Hard Bounce Rate | Share of sent emails that permanently fail (invalid address, non-existent domain). | hard bounces / sent emails | Hard bounces per 100 sent emails | Address validity |
| Soft Bounce Rate | Share of sent emails that temporarily fail (mailbox full, transient server issue, rate limiting). | soft bounces / sent emails | Soft bounces per 100 sent emails | Temporary deliverability issues |
| Recency | How recently a contact point (email/phone) was verified or observed as valid. | Now − last verified/observed date | Days since last verification | Decay risk |
| Refresh cadence | How often you re-verify and update contact points for your active segments. | Scheduled interval | Days between refresh runs | Freshness operations |
| Wrong-person rate | Share of outreach attempts that reach someone other than the intended candidate. | wrong-person confirmations / total attempts (phone: dials; email: delivered emails) | Wrong-person outcomes per 100 attempts | Identity resolution quality |
| Suppression | Operational rule that prevents outreach due to opt-out, complaint, wrong-person, or risk flags. | suppressed records / total records evaluated | Suppressed per 100 evaluated | Compliance + reputation protection |
Variable-only examples (for clean reporting):
- Answer Rate: if you have H connected calls and A human answers, Answer Rate = A/H (human answers per 100 connected calls = (A/H) × 100).
- Deliverability Rate: if you send S emails and L are delivered, Deliverability Rate = L/S (delivered per 100 sent = (L/S) × 100).
- Bounce Rate: if you send S emails and B bounce, Bounce Rate = B/S (bounces per 100 sent = (B/S) × 100).
- Reply Rate: if you have L delivered emails and R replies, Reply Rate = R/L (replies per 100 delivered = (R/L) × 100).
Step 3: Map tool outcomes to the definitions (so the math is reproducible)
Define two terms in your ops doc and keep them stable:
- Connected call: the carrier connects the call (a connection event), regardless of whether a person answers.
- Human answer: a person answers the call (a human-answer event), not voicemail or an automated message.
Then create a mapping table that says which raw outcomes count toward each numerator/denominator. If you change dialers, update this table the same day.
| Raw outcome (example) | Counts toward total dials? | Counts toward connected calls? | Counts toward human answers? | Notes |
|---|---|---|---|---|
| Connected (carrier connected) | Yes | Yes | No | Connected does not imply a human answered. |
| Human answered | Yes | Yes | Yes | Use a consistent rule for what qualifies as “human.” |
| Voicemail reached | Yes | Depends on dialer | No | If your dialer marks voicemail as connected, document it and keep it consistent. |
| Busy / no answer / failed | Yes | No | No | Still counts as a dial attempt. |
For email, do the same mapping: sent → denominator for deliverability and bounce; delivered → denominator for reply and positive reply; bounced → numerator for bounce (split hard/soft).
Step 4: Build a weekly scorecard that can’t drift
Publish a scorecard with per-100 rates and raw counts. If someone challenges a number, you should be able to trace it back to events.
- Phone: total dials, connected calls, human answers, wrong-person confirmations, opt-outs/suppressions.
- Email: sent, delivered, hard bounces, soft bounces, replies, positive replies, unsubscribes/complaints, suppressions.
The trade-off is… per-100 reporting is easy to read, but it can hide volume changes. Always show raw counts next to rates so leaders can see whether performance changed or volume changed.
Diagnostic Table:
Use this to triage performance without guessing. It’s written for recruiting ops: what broke, where to look, and what to change.
| Symptom | Most likely cause | Where to check | What to do next |
|---|---|---|---|
| Connect Rate drops | Number decay, carrier filtering, or dialer config change | Dialer outcome codes; recency distribution; time-zone call blocks | Increase refresh cadence for active segments; adjust call windows; review caller ID strategy |
| Connect Rate stable, Answer Rate drops | Timing mismatch or caller ID trust issue | Answer Rate by hour/day; spam labeling reports; caller ID settings | Shift call blocks; rotate caller IDs; tighten targeting to reduce low-intent connects |
| Deliverability Rate drops | Domain reputation or list hygiene issue | Google Postmaster Tools reputation signals; bounce breakdown (hard vs soft) | Pause risky segments; remove hard bounces; slow sending; improve authentication and warm-up practices |
| Hard Bounce Rate rises | Invalid or stale addresses | Hard bounce logs; email recency | Refresh active segments; suppress known-invalid; verify before sending |
| Reply Rate flat, Positive Reply Rate falls | Offer mismatch or role confusion | Reply sentiment tagging; wrong-person confirmations | Fix role/facility context; tighten filters; include concrete schedule/call details earlier |
| Wrong-person rate increases | Identity resolution drift (shared lines, similar names, recycled numbers) | Wrong-person flags by source; match confidence | Feed wrong-person outcomes into suppression and matching rules; require identity confirmation in first touch |
Weighted Checklist:
Score each item 0–2 (0 = not in place, 1 = partial, 2 = solid). Multiply by weight. This prioritizes changes that reduce wasted attempts and protect deliverability.
| Area | Check | Weight | Why it matters |
|---|---|---|---|
| Definitions | All teams use the same formulas and denominators (per 100 dials; per 100 sent; per 100 delivered) | 5 | Stops reporting drift and makes tests comparable |
| Instrumentation | ATS/CRM captures dial outcomes, delivery/bounce type, replies, positive replies, and suppression reasons | 5 | Without fields, you can’t audit or improve |
| Phone reachability | Phone records include recency metadata and a refresh cadence for active segments | 4 | Prevents wasted dials and repeated carrier failures |
| Email reputation | Deliverability monitored with Google Postmaster Tools; hard/soft bounces tracked separately | 4 | Protects domain and keeps the delivered denominator real |
| Suppression | Central suppression list enforced across tools (opt-outs, complaints, wrong-person) | 4 | Reduces risk and prevents repeat mistakes |
| Identity quality | Wrong-person outcomes are logged and fed back into matching rules | 3 | Improves candidate experience and recruiter efficiency |
| Execution | Call blocks and email sends are scheduled by time zone and role patterns | 3 | Improves Answer Rate and reply likelihood without more volume |
Outreach Templates:
These templates are designed to reduce wrong-person outcomes and produce clean measurement. Keep the structure stable while you test one variable at a time.
Phone opener (30 seconds)
- Confirm identity: “Hi Dr. [Last Name]—this is [Name]. Quick check: is this still the best number for you?”
- If yes: “Thanks. I’m recruiting for a [Role] at [Facility/Group] in [Location]. Do you have 60 seconds now, or should I call back at [two specific windows]?”
- If wrong person: “Appreciate it—sorry about that. I’ll remove this number.” (Log wrong-person + suppress.)
- What to log: dial outcome, connected flag, human answer flag, wrong-person flag, suppression reason if applicable.
Email 1 (identity-first)
Subject: Quick confirm — [Role] at [Facility/Group]?
Hi [First Name] — I’m recruiting for [Role] at [Facility/Group] in [Location]. Before I send details, can you confirm this is the right email for you?
If not, reply “no” and I’ll suppress you.
— [Name], [Title] at Heartbeat.ai
What to log: sent, delivered, bounce type (if any), reply, positive reply, suppression reason.
Email 2 (details + next step)
Subject: Details + call window?
Thanks, [First Name]. The role is [2–3 specifics: schedule/call/setting]. If you’re open to a quick call, what’s a good window this week?
If you’d rather not get outreach from me, reply “opt out” and I’ll suppress you.
Common pitfalls
1) Counting “connected” differently across dialers
Some dialers treat voicemail as connected; others don’t. If you change dialers and don’t update your mapping table, your Connect Rate will jump or drop without any real change in reachability.
2) Measuring replies on sent instead of delivered
Reply Rate is replies / delivered emails. If you use sent as the denominator, you can hide list hygiene problems and misread message performance.
3) Blending wrong-person outcomes into “not interested”
Wrong-person is an identity/data failure. “Not interested” is a targeting/offer failure. Keep them separate so you fix the right thing.
4) Treating suppression as a manual side task
Suppression must be enforced across tools. If opt-outs live only in an inbox label, you will re-contact people and create avoidable complaints.
5) Uniqueness hook: Trust hub as product (prevent definition drift)
If definitions live in multiple docs, they will drift. Run your trust hub like a product: one stable, linkable definitions page that every metric/claims page points to, with a visible Last reviewed date and a lightweight Change log that records what changed and why.
- Owner: recruiting ops (or RevOps) owns the definitions; marketing can format, but ops owns meaning.
- Cadence: review on tool changes (dialer/ESP/ATS) and quarterly otherwise.
- UI note: publish a hub “card grid” for Definitions / Testing / Sources / Ethics / Not HIPAA / Editorial / Corrections / Security, and show “Last reviewed” + “Change log” on each page.
How to improve results
Improvement starts with measurement hygiene. If you can’t trust the denominator, you can’t trust the change.
Measurement instructions (required)
Measure this by… building a weekly scorecard that reports each metric with its canonical denominator and includes raw counts next to the rate.
- Connect Rate = connected calls / total dials. Report: connected calls per 100 dials + raw counts (connected calls, total dials).
- Answer Rate = human answers / connected calls. Report: human answers per 100 connected calls + raw counts.
- Deliverability Rate = delivered emails / sent emails. Report: delivered per 100 sent + raw counts.
- Bounce Rate = bounced emails / sent emails. Report: bounces per 100 sent, split hard/soft + raw counts.
- Reply Rate = replies / delivered emails. Report: replies per 100 delivered + raw counts.
Also track wrong-person rate per channel (phone: wrong-person per 100 dials; email: wrong-person per 100 delivered emails) and suppression volume by reason.
Operational levers that usually move the numbers
- Connect Rate: refresh active segments; call in role-appropriate windows; reduce repeated attempts to stale numbers; keep caller ID strategy consistent. Heartbeat.ai supports workflows that include ranked mobile numbers by answer probability so recruiters spend dials where answers are more likely.
- Answer Rate: adjust call blocks; tighten targeting; confirm identity in the opener to reduce defensive screening.
- Deliverability Rate: monitor domain reputation signals (Google Postmaster Tools), suppress risky segments quickly, and keep bounce handling strict.
- Reply Rate: shorten the first email, confirm identity, and ask for a specific next step (a call window). Keep the template stable while testing one variable at a time.
ATS/CRM field map (compact)
Here’s a minimal field map that supports the definitions above and keeps reporting auditable across tools.
| Metric | Minimum fields/events to log | System of record |
|---|---|---|
| Connect Rate | dial_timestamp, dial_outcome, connected_flag | Dialer (synced to ATS/CRM) |
| Answer Rate | human_answer_flag, call_duration_seconds (optional), answer_timestamp | Dialer |
| Deliverability Rate | email_sent_timestamp, delivered_flag, bounce_flag, bounce_type | Email provider / outreach tool |
| Bounce Rate | bounce_flag, bounce_type (hard/soft) | Email provider / outreach tool |
| Reply Rate | reply_flag, reply_timestamp, reply_thread_id | Email provider / outreach tool |
| Positive Reply Rate | positive_reply_flag (manual tag or classifier), tag_timestamp | ATS/CRM (review workflow) |
| Wrong-person rate | wrong_person_flag, wrong_person_channel, confirmation_timestamp | ATS/CRM |
| Suppression | suppression_flag, suppression_reason, suppression_timestamp | ATS/CRM (enforced across tools) |
Legal and ethical use
This page defines measurement terms and operational logging. It is not legal advice. Use these metrics to run legitimate recruiting outreach with clear opt-out handling and respectful contact frequency.
- Honor opt-outs and suppress across all tools.
- Minimize data: store what you need to recruit and measure; avoid collecting sensitive information you don’t need.
- Be transparent: identify yourself, your purpose, and provide a simple opt-out path.
Do not represent any dataset as “HIPAA compliant,” “safe harbor,” or “guaranteed accurate.” Heartbeat does not provide legal counsel.
Evidence and trust notes
This page is part of the Heartbeat trust methodology hub. If you want the broader context for how we define and test quality, start here: Heartbeat trust methodology.
Related trust detail: How we test contact data quality.
Implementation notes and editorial stability should follow user-first documentation principles. References:
FAQs
What is the connect rate definition for recruiters?
Connect Rate = connected calls / total dials. Report it as connected calls per 100 dials, and keep the definition stable across teams and tools.
What’s the difference between connect rate and answer rate?
Connect Rate is connected calls per 100 dials. Answer Rate is human answers per 100 connected calls. Use both so you can separate reachability from call timing and trust.
How do you define deliverability rate and bounce rate for recruiting email?
Deliverability Rate = delivered emails / sent emails (delivered per 100 sent). Bounce Rate = bounced emails / sent emails (bounces per 100 sent), split into hard and soft bounces.
How do you calculate reply rate?
Reply Rate = replies / delivered emails. Use delivered as the denominator so bounces don’t distort the result.
What should we log to make these metrics auditable?
Log the raw events that create the numerators and denominators: dials, connected calls, human answers, sent, delivered, bounce type, replies, positive replies, wrong-person confirmations, and suppression reasons.
Next steps
- Interpretation deep-dive: Connect rate vs answer rate.
- Operational tracking: ATS/CRM field schema for outreach metrics.
- Email measurement workflow: Reply rate tracking for physician outreach.
- Data freshness ops: Provider data refresh cadence.
- Instrument this in your workflow: Create a Heartbeat.ai account.
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.
