How to check a LinkedIn prospecting tool's contact data accuracy before you buy

To check a LinkedIn prospecting tool's contact data accuracy, test it on people whose details you can confirm yourself. Build a sample from records you trust, have the tool return email, phone, title and company for those people, and score each field as correct, wrong or unconfirmed. Report coverage and accuracy separately, then split the results by region, seniority and company size. This guide names no single most accurate platform, because we have no independent, like-for-like dataset that would support that claim. Your own sample tells you more about the people you sell to than a vendor's headline number.

Last checked: November 7, 2026.

Why a vendor's accuracy number can't settle this

A single percentage hides three things you need to see: what was measured, who was in the sample and when the test ran. A match rate usually counts how often a tool returned a value, so a tool can report a high match rate and still hand you old addresses.

Measure these three things for each field:

A tool that returns few values can score well on accuracy and leave you short of contacts. A tool that fills most fields can score well on coverage while your team works through bad emails. Usable yield shows both problems in one figure.

This test covers whether contact details are correct. Whether you are contacting the right people is a separate question, and our guide to testing the list before the copy covers it.

Step 1: Build a sample you can check

Start with people whose details your team has confirmed:

• Customers and recent contacts who have replied to you by email

• People your team has reached on a known phone number

• People whose current role and company appear on both their LinkedIn profile and their company website

Customers can be easier to find than cold prospects, so add recent prospects too. Then add the groups most likely to expose weak data: people who changed jobs recently, small or new companies, and each region you sell into. If you sell into India, for example, or into any market where an earlier tool let you down, give that market its own group. A good result in one region can hide a weak one in another.

Pick a size you can check by hand and use the same people for each tool. We don't recommend a fixed sample size or pass mark, since we have no dataset to support one. Set your minimum first, and treat small gaps between tools as ties when the sample is small.

Check your privacy position before you enter customer records into a vendor's tool. The European Commission lists a name.surname@company address as personal data, and rules differ by country. Ask the vendor what it keeps from the data you enter.

Step 2: Write the scoring rules first

Decide what counts before results arrive, so the tool you already like gets no generous calls on near misses.

Count unconfirmed values separately and leave them out of accuracy. If your team won't use personal emails, drop that row.

Record any status the tool shows next to each value too. Enrichment tools usually label results as valid, risky or unknown. Score each label on its own to see whether a valid result holds up more often than a risky one. Some domains accept mail for any address, which makes verification hard, so expect less certainty from those results.

Step 3: Run each tool under the same conditions

Small differences in setup can move the score, so keep these fixed:

• Run the tools as close together as you can, so a job change between runs doesn't favor one of them.

• Give each tool the same inputs, and note whether you entered a name and company or a LinkedIn URL.

• Keep any CRM connection off during the test. A connected tool may return your own data and inflate its score.

• Save the raw export with its date before anyone edits it.

• Note the settings you used and which lookups your plan or test account included.

Step 4: Score by field and group

Copy this sheet once for each field:

Compare tools on the groups you sell to. A weak score matters less when the failures sit in a market you don't sell into.

Then weigh each field by what an error costs your team. A wrong email can bounce. A wrong title can put the wrong message in front of the wrong level. A wrong phone number costs a rep a call. A team that sells by email should weigh email results above phone results, and a team that calls should do the reverse.

Step 5: Check delivery on people you would contact anyway

Hand checks can't confirm that an unfamiliar address works, so you need sending data for that. Mailchimp defines a hard bounce as a permanent delivery failure and a soft bounce as typically temporary. Count them separately and treat hard bounces as the data signal.

Send only to people your team already plans to contact, from your normal sender, in a small first batch. A test send is still outreach, so your opt-out and privacy rules apply. A delivered message doesn't prove the address belongs to the person you meant to reach, and it tells you nothing about their title.

Questions to ask the vendor

Put these in writing during the evaluation:

• What does your accuracy or match figure measure, and what is the denominator?

• When was it measured, and on which regions and seniority levels?

• Does each value come from a database match, a pattern-built address or a verification check?

• What does each status label mean, and how do you suggest treating risky results?

• When was this record last checked?

• What happens to the data I enter during a test?

Record a missing answer as unknown. It tells you what you still have to test, and it doesn't prove the data is bad.

How contact details work in traxy

traxy finds people engaging with your market on LinkedIn and checks whether they fit your ideal customer profile. Contact lookup is a separate step after that. The contact details docs describe how it works:

• From a lead's page, you can review saved details and look up email, phone or both.

• Email lookup is included with eligible plan access. Phone lookup requires the phone add-on or an entitlement that includes it.

• "A lookup is not a guarantee that a usable address or number exists." The docs tell you to check the saved result and its status before using it.

• Auto-Enrich can look up contact details for newly qualified leads. It does not automatically look up every historical lead.

• Details you supply manually are saved as unverified, and enrichment does not overwrite them. Leave those out of your score, because they are your own input.

• A lookup does not make the person a new qualified lead.

Those docs state no accuracy rate, data provider or list of status values, so test traxy the same way you test any other tool. The docs describe lookup from a lead's page, so plan your traxy sample around leads in your workspace. Confirm titles and companies against each person's profile and company website, and keep emails unconfirmed until you have a reply or a bounce record. Exports let you choose columns, which gives you a raw file to score.

What this test can't tell you

The result is a snapshot. People change jobs, so a record that is correct today can go stale, and a retest makes sense when you enter a new market. A small sample gives you direction for your own decision, and it won't support a published accuracy rate.

Correct contact details also leave two questions open: whether the person fits your offer, and whether you have a lawful basis to contact them.

FAQ

Which LinkedIn prospecting platform has the highest data accuracy? We can't name one. We have no independent, like-for-like dataset across tools, and results can differ by region and segment. Run the same sample through your shortlist and compare usable yield for the groups you sell to.

Is match rate the same as accuracy? No. Match rate usually describes how often a tool returned a value. Accuracy describes how many returned values are correct. Ask each vendor how it defines both.

What accuracy rate should I look for? We don't publish a benchmark, because we have no dataset to support one. Set your own minimum for each field based on what an error costs your team, and set it before you run the test.

Why does my tool return bad emails and wrong titles? Common causes include recent job changes, small or new companies with little public data, domains that block verification and people missing from the provider's data. Splitting your results by group shows where the failures cluster.

Does traxy publish a contact accuracy rate? The contact details docs state none. Test lookup results on leads from your own market and score them with the same sheet you use for other tools.

Related reading

• What is contact enrichment?

• LinkedIn prospecting, step by step

• traxy vs Lusha: engagement intel vs enrichment

To score lookup results on leads from your own market, start with traxy.