What’s the difference between match rate and accuracy?

Match rate is whether a field is returned. Accuracy is whether the returned field is correct now. High match rate can still produce low reachability if verification and recency are weak.

What does “reachability” mean in practice?

Reachability is whether your outreach can contact the person. For phone, it shows up as connected/answered calls. For email, it shows up as delivery. It’s the metric that drives wasted effort.

Why do two teams see different results from the same tool?

Variance comes from seat count, API usage patterns, list quality, industry churn, and how often records are re-checked for recency.

Is phone number verification the same as “this reaches the right person”?

No. Verification can reduce obvious invalids, but it can’t guarantee ownership. That’s why connect rate is the final check.

What should I check first if connect rate is low?

Start with integration and workflow: dialer acceptance rules, formatting, field mapping, overwrite rules, and whether you’re enriching stale records without re-checking recency.

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Swordfish Data Accuracy: Definitions, What to Measure, and What Breaks in Real Deployments

By Ben Argeband, Founder & CEO of Swordfish.AI

Author note: Clarify definitions fast (verified contact data, direct dial, mobile vs VoIP) and route readers to what to check first; keep answers tight.

Definitions used here: A direct dial is a number intended to reach an individual without going through a main switchboard. A mobile number is a carrier-assigned cellular line. VoIP is an internet-based line that can behave differently in dialers and can change hands more easily than teams assume.

Who this is for

This is for buyers and operators who have to justify a contact data tool and then clean up the mess when “accuracy” turns into dead dials, CRM overwrites, and integration rework. If you want quick definitions and next steps for common data quality questions, this page is the baseline.

Quick verdict

Core answer: Swordfish data accuracy is best evaluated with the Accuracy Triad: match rate → verification → connect rate. If you only measure match rate, you’re measuring how often the tool returns a field, not whether you can reach a person.
Key stat: There is no single accuracy percentage that transfers across teams. Results vary with seat count, API usage, list quality, and industry, plus how you manage recency.
Ideal user: Teams that want to reduce wasted outreach time by measuring reachability and not confusing it with match rate.

What Swordfish does differently

Most vendors optimize for match rate because it demos well. Operators pay for outcomes: fewer retries, fewer wrong numbers, and fewer hours burned on “looks good in the CRM” data. That’s why match rate vs accuracy vs reachability has to be separated and measured.

Swordfish prioritizes direct dials and provides ranked mobile numbers. That matters because phone data is not binary. A returned number can be a main line, a stale line, or a VoIP line that your dialer treats differently. Ranking plus verification can improve mobile reachability by pushing higher-confidence numbers to the top, which typically reduces wasted dials and agent idle time.

Swordfish offers unlimited access subject to a fair use policy. The operational point is that credit rationing causes teams to stop re-checking records, recency decays, and “accuracy” drops in production even if the dataset didn’t change.

If you want the database implementation of these standards (not just definitions), use Prospector to run searches and exports under the same verification and ranking logic described here.

Decision guide

When someone asks “How accurate is Swordfish?”, the only answer that survives procurement is: accurate for your use case, measured with your funnel. If you can’t measure outcomes, you can’t evaluate contact data, and you’ll end up arguing about screenshots instead of connect results.

The Accuracy Triad (framework): Match → Verify → Connect

Match rate: Did the tool return a contact field (email, mobile, direct dial) for the record you requested?
Verification: Is the returned field plausibly valid now based on validation signals? Verification reduces obvious invalids, but it cannot guarantee ownership or intent.
Connect rate: Did your real outreach connect (call connected/answered, email delivered)? This is where accuracy becomes a business outcome.

Connect outcomes are downstream and can be affected by your dialer settings, call routing, and outreach execution. Keep the workflow constant during testing so you’re measuring data quality, not process drift.

Here’s the trap: a tool can show a high contact data match rate by returning more fields. If verification is weak or data freshness is unmanaged, your connect rate drops and your cost per meeting rises. That cost shows up as SDR time, dialer reputation issues, and pipeline noise, not as a line item on the invoice.

Checklist: Feature Gap Table

What buyers ask for	What it often means in practice	Hidden cost if missing	What to verify in Swordfish
“High accuracy”	Often reported as match rate, not reachability	More attempts per meeting; inflated SDR hours	Measure connect rate vs match rate on the same list segment
“Verified contact data”	May be syntax-only checks or periodic batch validation	False confidence; you scale bad records faster	Confirm what verification means and when it runs (lookup-time vs batch)
“Mobile numbers”	Can include mobile, VoIP, and recycled lines unless ranked	Lower mobile reachability; more wrong-number retries	Check ranked mobile output and how mobile vs VoIP is handled
“Direct dials”	Can include main lines, IVRs, or outdated extensions	Call routing friction; lower connect rate	Confirm prioritization of direct dials and how stale numbers are deprioritized
“Fresh data”	Often a claim without a measurable recency policy	Decay over time; enrichment becomes a one-time event	Ask how recency is tracked and how re-checks fit under unlimited + fair use
“Easy integration”	API exists, but mapping, dedupe, and overwrite rules are on you	Engineering time; CRM field corruption; duplicates	Validate API usage expectations, rate limits, and your CRM field mapping plan

Decision Tree: Weighted Checklist

This checklist is weighted by standard failure points that create hidden costs: wasted outreach (bad reachability), decay (poor recency), and integration rework (mapping/dedupe). Use it to decide what to test first.

Highest weight: Reachability outcomes — Run a controlled test that compares match rate vs accuracy vs reachability on the same lead list. Business outcome: higher reachability reduces wasted dials and lowers cost per meeting.
Highest weight: Verification at the point of use — Confirm how verification is applied and whether it runs at lookup-time. Business outcome: fewer disconnected numbers and fewer retries per prospect.
High weight: Recency policy — Decide how often you will re-check records and whether your plan supports that behavior. Business outcome: managing recency reduces decay-driven performance drops after rollout.
High weight: Mobile vs VoIP handling — Validate line-type logic and how ranked results are presented. Business outcome: better mobile reachability improves connects per hour for phone-first outreach.
Medium weight: CRM field mapping + dedupe rules — Define source-of-truth fields and conflict handling before you enrich. Business outcome: prevents silent overwrites and duplicate sequences.
Medium weight: API usage and rate limits — Model expected enrichment volume (batch + real-time) before rollout. Business outcome: avoids throttling that forces teams to skip re-checks and harms recency.
Lower weight: UI convenience — Useful, but it won’t fix decay or verification gaps. Business outcome: small time savings compared to reachability improvements.

Troubleshooting Table: Conditional Decision Tree

If a vendor reports “accuracy” but cannot separate match rate, verification, and connect rate, then treat the claim as non-auditable and run your own list test before expanding seats.
If your outreach is phone-first, then prioritize ranked mobile numbers and direct dials because higher mobile reachability increases connects per hour and reduces agent idle time.
If your connect rate is low but match rate is high, then the likely failure is verification quality or recency decay; re-test with a newer list segment and compare outcomes by record age.
If your CRM enrichment creates duplicates or overwrites good fields, then fix mapping/dedupe before blaming data quality; integration mistakes can look like “bad accuracy” in reporting.
Stop condition: If you cannot measure connect rate (calls connected, emails delivered) from your systems, stop the evaluation and instrument tracking first.

Limitations and edge cases

Contact data decays. People change jobs, carriers recycle numbers, and companies reroute lines. If your process enriches once and never re-checks, your results will degrade over time regardless of provider. That’s a recency problem.

Use case shapes perceived accuracy. High-volume outbound will surface errors quickly because small percentage drops create large absolute waste. Low-volume, high-intent outreach may tolerate lower match rate if verification and reachability are strong on the records that do return.

Integration can create false negatives and false positives. A common failure mode is field precedence: your enrichment writes a phone into the wrong CRM field, your dialer reads a different field, and reporting blames “bad data” when the workflow is miswired. Another is overwriting: a stale number can overwrite a previously good number if you don’t set overwrite rules and dedupe logic before turning on automation.

Evidence and trust notes

This page does not claim a universal accuracy percentage. Variance is driven by seat count, API usage, list quality, and industry, plus how you manage recency and verification timing. If you want a structured way to compare tools without mixing definitions, contact data benchmarks shows how to separate match, verification, and connect outcomes.

If your connect outcomes do not improve versus your baseline under the same workflow, treat that as a failed evaluation and do not scale seats or API usage.

What to keep for auditability: save the frozen input list snapshot, enrichment timestamps, the exact field mapping/overwrite rules you used, and dialer/ESP outcomes (dispositions, delivery events). Without those artifacts, you can’t explain variance, and you can’t reproduce results.

How to test with your own list (5–8 steps):

Freeze a sample list from your CRM or outbound tool so the input doesn’t change mid-test.
Split by record age (recently updated vs older records) to expose decay.
Run enrichment the way you will in production (API usage vs manual lookup). Log returned fields to compute match rate by field type.
Record verification signals you can observe (line type, formatting acceptance, suppression outcomes) and note what your dialer/CRM rejects.
Push the enriched records through your actual workflow (dialer sequences, routing rules, email sending) so you measure real friction.
Measure connect outcomes in your systems (connected/answered calls, delivered emails) and compare them to your baseline.
Compare connect rate vs match rate across the two record-age cohorts to isolate verification/recency/integration issues.
Document assumptions (seat count, API usage volume, list source, industry segment) so you can reproduce results after rollout.

If you need the definitions behind the metrics, start with data quality. If you want the direct question answered with the same triad framing, see how accurate is Swordfish. If your evaluation is blocked by credit rationing, read unlimited contact credits.

FAQs

What’s the difference between match rate and accuracy? Match rate is whether a field is returned. Accuracy is whether the returned field is correct now. High match rate can still produce low reachability if verification and recency are weak.
What does “reachability” mean in practice? Reachability is whether your outreach can contact the person. For phone, it shows up as connected/answered calls. For email, it shows up as delivery. It’s the metric that drives wasted effort.
Why do two teams see different results from the same tool? Variance comes from seat count, API usage patterns, list quality, industry churn, and how often records are re-checked for recency.
Is phone number verification the same as “this reaches the right person”? No. Verification can reduce obvious invalids, but it can’t guarantee ownership. That’s why connect rate is the final check.
What should I check first if connect rate is low? Start with integration and workflow: dialer acceptance rules, formatting, field mapping, overwrite rules, and whether you’re enriching stale records without re-checking recency.

Next steps

Day 0–1: Define your measurement in writing: match rate, what counts as verification, and where connect outcomes are logged.
Day 2–3: Run the list test with two record-age cohorts and document API usage vs manual lookup behavior.
Day 4–5: Review variance drivers (seat count, API usage, list quality, industry) and isolate whether the gap is verification, recency, or integration mapping.
Week 2: If results hold, scale usage and implement a recency policy (scheduled re-checks) so performance doesn’t decay after rollout.

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.