Every enrichment tool has a landing page with an accuracy claim. "95% email accuracy." "90% match rate." "Verified contact data you can trust." The numbers are always high, the methodology behind them is always vague, and they almost never match what you'll actually see when you run the tool on your own prospect list.
This isn't because vendors are lying (though some are rounding generously). It's because accuracy depends heavily on who you're looking up. A tool might genuinely hit 95% on US-based contacts at companies with 500+ employees in tech. That same tool might drop to 60% when you search for contacts at 20-person companies in Germany. The vendor tested on a sample that flatters their product. Your prospect list probably doesn't look like that sample.
The only way to know if a tool works for your use case is to test it yourself, on your own prospects, with your own criteria for what counts as a usable result.
Why vendor accuracy claims don't transfer
There are a few specific reasons why the number on the landing page won't match your experience.
The test sample is optimized. Vendors pick profiles that their database covers well. Large US tech companies, people with common names, contacts who've been in their role for over a year. These are the easiest profiles to enrich accurately. Your prospect list includes edge cases the vendor's sample avoided.
"Accuracy" isn't defined consistently. Some vendors count a "match" as any email returned, regardless of whether it's current. Others count it only if the email passes SMTP verification. Some count personal emails (Gmail, Yahoo) as matches. Others don't. When Tool A claims 93% and Tool B claims 88%, they might be measuring completely different things.
Recency isn't factored in. A tool might have found the right email six months ago, but the contact changed jobs since then. The email still exists in the database, it still passes verification (because the old employer hasn't deactivated it yet), but it's no longer the right address. The vendor counts this as accurate. You'll count it as a bounce when the email fails three months from now.
Geography skews everything. Most enrichment databases are strongest on US and UK contacts. Coverage drops significantly for Europe, Asia, and emerging markets. If your ICP includes international prospects, the vendor's US-heavy test sample tells you almost nothing about your expected match rate.
How to run your own test
You don't need a huge sample. Twenty to thirty profiles is enough to see meaningful patterns. Here's the setup I used when I compared three enrichment tools side by side.
Pick real prospects from your pipeline. Don't grab random LinkedIn profiles. Use contacts you'd actually reach out to. This matters because your ICP has specific characteristics (geography, company size, industry, seniority) that affect enrichment coverage in predictable ways.
Make the sample representative. If 40% of your prospects are in the US, 30% in Europe, and 30% everywhere else, your test sample should reflect that. If you test 25 US contacts and the tool gets 90%, then you run your real campaign that's 30% European and get 70%, the test didn't prepare you for reality.
Include hard cases. A few contacts at small companies (under 50 employees), a few who changed jobs recently, a few in industries with low data coverage like manufacturing or government. These are the profiles where tools diverge. Every tool does well on easy profiles. The hard ones reveal the actual differences.
Test at least two tools. You need a baseline to compare against. A 75% match rate means nothing in isolation. If the competing tool gets 60% on the same profiles, 75% looks good. If the other tool gets 85%, it doesn't.
What to measure (and what most people miss)
Most people count two things: did the tool find an email, and did it find a phone number. That's a start, but it misses the important details.
Check if the email is at the right company. If a contact works at Acme Corp but the tool returns their email from a company they left eight months ago, that's not a match. It's stale data that will either bounce or reach someone who's confused about why you're emailing them at their old job. In my 25-profile test, I found this exact problem with multiple tools. The email "existed" but pointed to the wrong employer.
Check if the phone number is in the right country. This sounds obvious, but it happens more than you'd expect. I tested a contact in Burnaby, British Columbia, and one tool returned a New Jersey area code. The tool counted it as a "phone found." In practice, that number was useless. If you're scoring tools and you don't check country codes, you'll overcount their accuracy.
Check email type. A work email (firstname@company.com) and a personal email (randomname@gmail.com) are not equivalent for B2B outreach. Some tools return personal emails and count them as matches. For cold outbound, a personal email is often worse than no email because sending unsolicited sales messages to personal addresses has higher spam complaint rates and can feel intrusive.
Track "found but wrong" separately from "not found." A tool that returns nothing for a contact wastes a lookup credit. A tool that returns wrong data wastes the credit plus the rep's time following up on bad info plus potential damage to sender reputation. "Not found" is a miss. "Found but wrong" is a liability. Most vendor accuracy claims don't distinguish between the two.
The AI caveat
A lot of enrichment tools now advertise AI-powered features: AI-verified emails, AI-enriched profiles, AI-confidence scores. Some of this is real and useful. AI can cross-reference multiple data points to estimate whether an email is current. It can flag contacts who likely changed jobs based on tenure patterns and company hiring signals.
But "AI-powered" on a marketing page doesn't tell you anything about whether the output is actually more accurate for your specific use case. The same evaluation framework applies. Test it on your prospects, measure what comes back, and check whether the results are usable. An AI-confidence score of 95% that still gives you a 7% bounce rate isn't meaningfully different from a non-AI tool with the same bounce rate.
The tools that use AI well tend to be transparent about what the AI does. "We use machine learning to predict email patterns when we can't find a verified address" is specific and testable. "AI-powered enrichment" with no further explanation is marketing. Ask the vendor what the AI actually does. If they can't explain it in one sentence, it's probably not doing much.
The free tier test
Most enrichment tools have free tiers or trial periods. Use them. But be aware of how free tiers can mislead.
Some tools limit free users to their highest-quality data and reserve the full (lower-accuracy) database for paid plans. This makes the free tier look better than the paid experience. Others do the opposite: limit free users to basic lookups and save the good data for paying customers. Either way, the free tier might not represent what you'll get at scale.
The better approach is to run your 25-profile test during the free trial, but also ask the vendor for their average bounce rate across paid accounts in your industry and geography. If they can provide that, you have a real benchmark. If they can't or won't, the free trial results are the best data you'll get, so run the test carefully.
What to ask the vendor
Beyond running your own test, there are a few questions that separate transparent vendors from ones who'd rather you not look too closely.
What's your bounce rate for outbound campaigns? Not their internal accuracy metric. The actual bounce rate that customers see when they send emails to addresses the tool provided. If they track this and it's under 3%, that's a strong signal. If they don't track it, or they dodge the question, that tells you something too.
How often is your database updated? Some providers update continuously as new data comes in. Others do batch updates monthly or quarterly. The update frequency directly affects how often you'll get stale data. A database that updates quarterly will have more outdated emails than one that updates in real time.
How many data sources do you query per lookup? Tools that check a single database (their own) will always have coverage gaps for certain geographies, industries, or company sizes. Tools that use waterfall enrichment across 10-20+ providers cover more ground. The number of sources correlates with match rate, especially on hard-to-find contacts.
What happens when you can't find data? Some tools return nothing. Others return a low-confidence guess. Others charge you a credit even for a miss. Understanding the failure mode matters because it affects your workflow and your cost per usable contact.
Putting it together
The evaluation process doesn't take long. A 25-profile test across two tools takes about an hour. Scoring the results takes 10 minutes if you throw the data into Claude or ChatGPT to analyze. That's just over an hour of work that saves you from committing to a tool based on a landing page claim that may not apply to your situation.
The tools that perform well on your test are the ones worth paying for. The tools that underperform on your specific ICP will underperform at scale too, regardless of what their accuracy page says. Trust your own data over anyone's marketing.
If you want to include ShareCo SalesSync in your comparison test, it checks 20+ providers per lookup and has a free tier on the Chrome Web Store. Or test whatever tools are on your shortlist. The hour is worth it either way.