Why Enrichment Tools Show Different Results on the Same Profile

Run the same LinkedIn profile through three different enrichment tools and you'll get three different sets of contact data. Different emails, different phone numbers, sometimes different job titles. One tool finds a work email. Another finds a personal Gmail. A third finds nothing at all.

Once you understand why this happens, it changes how you evaluate which tools to use and which data to trust.

Each tool queries different databases

The most basic reason tools return different results is that they're looking in different places.

Every enrichment provider has its own data sources. Some scrape the public web. Some buy data from third-party aggregators. Some crowdsource information from their users (Apollo does this, for example, by collecting email signatures from people who use their platform). Some partner with data providers that specialize in specific industries or regions.

When you run a profile through Tool A, it checks databases X, Y, and Z. Tool B checks databases Y, W, and V. There's some overlap (database Y), but a lot of the sourcing is different. If the email you need happens to live in database W but not in X or Z, only Tool B finds it.

This is also why waterfall enrichment exists as a concept. Instead of relying on one tool's database, waterfall tools query multiple providers in sequence until they find a result. The coverage is better because the search space is wider.

Data freshness varies by provider

Even when two tools have access to the same underlying data, they might return different results because their data is different ages.

Provider A might have scraped a company's website in January and cached the results. Provider B might have scraped the same site in March. If someone changed jobs in February, Provider A still has the old data and Provider B has the new data. Both are "correct" relative to when they last checked. But only one reflects reality.

The refresh cycle matters a lot. Some providers re-verify their data monthly. Others do it quarterly. Some only refresh data when a user specifically requests a lookup, which means rarely-searched contacts can sit in the database for a year or more without being updated.

When I ran the enrichment comparison test on 25 profiles earlier this year, I saw this firsthand. For contacts who had recently changed jobs (January and February 2026 start dates), some tools had already updated their records and others hadn't. Which tool was faster depended entirely on when each provider last refreshed that specific contact's data. There was no consistent winner on freshness across the full sample.

Email discovery methods differ

Not all tools find emails the same way, even if they end up checking the same SMTP servers.

Pattern matching is the most common first step. The tool identifies the company's email format (firstname.lastname@company.com, first initial + lastname, etc.) and generates a guess. Then it verifies that guess against the mail server. If the pattern is correct and the mailbox exists, you get a result.

But companies don't all use the same pattern. Some use firstnamelastname with no separator. Some use nicknames. Some have different formats for different departments or offices. If Tool A's pattern-matching model guesses firstname.lastname and Tool B guesses f.lastname, they'll verify different addresses and potentially get different results, even though both checked the same mail server.

Some tools skip pattern matching entirely and rely on databases of known email addresses. If someone's email appeared in a data breach, a public directory, or a user-contributed dataset, the tool pulls it directly without guessing. This approach can find emails that pattern matching would miss, but it can also return outdated addresses that are no longer active.

Then there's the confidence threshold question. Tool A might find an email with 70% confidence and return it. Tool B might find the same email with 70% confidence and suppress it because their threshold is 80%. Same data, different results, because the tools have different standards for what counts as "found."

Phone number sourcing is even messier

Email is relatively structured. Phone numbers are chaos.

There's no reliable pattern-matching approach for phone numbers. You can't guess someone's direct dial the way you can guess their email format. Phone data comes from purchased lists, public filings, user contributions, and third-party data brokers. The quality varies wildly.

One tool might return a company's main switchboard number. Another might return the person's direct office line. A third might return a mobile number that the person listed on a form three years ago and has since changed. All three are "phone numbers." Only one is useful for reaching the actual person.

In the comparison test, Apollo returned phone numbers from the wrong country on three profiles. A contact based in Burnaby, BC got tagged with a New Jersey area code. That's not Apollo inventing data. It's Apollo pulling from a source that had an incorrect or outdated record, and the mismatch wasn't caught because phone number validation is much harder than email validation.

The same profile can have multiple valid emails

Sometimes the discrepancy between tools isn't an error at all. People have multiple email addresses.

A VP of Sales might have their work email (name@company.com), a secondary work email from an acquisition (name@acquired-company.com that forwards to the main inbox), and a personal email they used to sign up for a conference years ago. All three are technically valid. Different tools find different ones depending on which databases they check.

The question isn't which tool found "the right" email. It's which tool found the most useful email for your specific use case. For B2B outbound, the current work email at their current company is what you want. A personal Gmail or an old work email from a previous job technically counts as a "found" result in the tool's metrics, but it's not what you need.

What this means for how you evaluate tools

Knowing why results differ changes how you should think about tool evaluation.

Don't test on one profile. A single profile can show anything. Tool A might find data that Tool B misses on that specific person, but Tool B might outperform on the next 20 profiles. You need a sample of at least 20-25 profiles from your actual target market to see real patterns.

Look at accuracy, not just coverage. A tool that "finds" 90% of emails but returns personal addresses, outdated work emails, or wrong-company matches on 15% of those isn't actually giving you 90% useful data. Check what comes back, not just whether something comes back.

Test on your actual ICP. If you sell to mid-market SaaS companies in North America, test on those profiles. If you sell to manufacturing companies in Germany, test on those. Enrichment accuracy is heavily dependent on industry, company size, and geography. A tool that's great for US tech companies might be mediocre for European healthcare companies.

Track results over time, not just at the point of purchase. Run the same test quarterly. Data sources change. Providers add and lose partnerships. A tool that won your evaluation six months ago might not win today.

The practical takeaway

Different tools return different results because they check different databases, refresh data on different schedules, use different discovery methods, and apply different confidence thresholds. None of this is shady. It's just how the industry works.

The best defense is not to trust any single tool completely. Use multiple sources when accuracy matters. Verify the data that comes back before building campaigns on it. And run your own tests instead of relying on the vendor's self-reported numbers.