We Enriched 500 LinkedIn URLs in 20 Minutes — Here's What the Data Showed
A real run of 500 LinkedIn profile URLs through the LinkedIn Enricher add-on with Datagma and LeadMagic fallback. Email hit rates, phone rates, cost per result, and what the not-found rows had in common.
Our sales team exported 500 LinkedIn profile URLs from a Sales Navigator search — mid-market SaaS, VP and Director level, US and EMEA. The standard move at that point is to manually open profiles, hunt for emails, and paste them into a sheet. That takes a full afternoon.
Instead, we ran the list through the LinkedIn Enricher add-on for Google Sheets with Datagma as the primary provider and LeadMagic as fallback. Here’s what came back — including the parts that didn’t work.
The Setup
- List: 500 LinkedIn profile URLs, all in column A of a Google Sheet
- Primary provider: Datagma (API key entered in the Settings tab)
- Fallback provider: LeadMagic (enabled as secondary)
- Skip already-enriched rows: checked (though this was a fresh list, so no effect)
- Start time to completion: 22 minutes
No other configuration. The add-on opened in the sidebar, we selected the column, clicked Enrich, and watched the progress bar tick forward.
Results
| Metric | Count | % of 500 |
|---|---|---|
| Emails found | 347 | 69.4% |
| Phones found | 201 | 40.2% |
| Fallback triggered (Datagma returned nothing) | 89 | 17.8% |
| LeadMagic recovered from fallback | 34 | 38.2% of fallback rows |
| Not found after both providers | 119 | 23.8% |
| Total run time | 22 min | — |
Net email hit rate: 69.4%. For a cold LinkedIn export to verified direct emails with no manual work, that’s a workable number.
Phone recovery rate was lower at 40.2%, which is typical — phone data is harder to source than email across all enrichment providers.
Per-Industry Breakdown
We tagged each row by industry before running, which let us analyze hit rates by vertical.
| Industry | Rows | Emails Found | Email Hit Rate |
|---|---|---|---|
| SaaS / Software | 187 | 141 | 75.4% |
| Financial Services | 94 | 59 | 62.8% |
| Healthcare / MedTech | 76 | 44 | 57.9% |
| Professional Services | 68 | 49 | 72.1% |
| Manufacturing / Industrial | 75 | 54 | 72.0% |
SaaS had the best hit rate — expected, since tech professionals tend to have more public digital footprints and more data in provider databases. Healthcare had the lowest, partly because healthcare email formats are more variable and HIPAA-era caution makes professionals less publicly visible.
What the “Not Found” Rows Had in Common
We manually reviewed a sample of 40 rows from the 119 not-found group. Patterns:
Personal LinkedIn profiles (no company affiliation visible): ~30% of not-found rows. Providers find work emails by correlating a LinkedIn profile with a company domain. If the profile shows no current employer, there’s no domain to generate a match against.
Very recently joined LinkedIn (< 6 months): ~18% of not-found rows. Provider databases have a crawl lag. New profiles may not yet appear in their index.
Senior executives with deliberately minimal public profiles: ~15%. Some C-level contacts actively reduce their LinkedIn presence. Less public data = fewer signals for providers to work from.
Profiles with only personal (Gmail/Yahoo) email patterns: ~12%. If a provider’s only data point is a personal email and the outreach requires a work email, the row comes back empty.
Remaining not-found: Data genuinely doesn’t exist in either provider’s database for these profiles — a real gap, not a solvable one without a different data source.
Cost Analysis
Exact provider pricing varies by plan and volume, but here’s the approximate cost model for this run:
| Item | Estimate |
|---|---|
| Datagma credits consumed (500 lookups) | ~500 credits |
| LeadMagic fallback credits (89 lookups) | ~89 credits |
| Total credits consumed | ~589 |
| Emails found | 347 |
| Effective cost per found email | ~1.7 credits per email |
At Datagma’s pay-as-you-go pricing, 589 credits works out to roughly $5–12 depending on your plan tier. At $8 total, that’s approximately $0.023 per found email — about 2.3 cents.
Compare that to an all-in-one prospecting tool at $100–400/seat/month where you still get roughly the same data for contacts outside their primary database.
When This Workflow Makes Financial Sense
| Scenario | Recommended approach |
|---|---|
| You have existing LinkedIn exports and need emails | LinkedIn Enricher + pay-as-you-go credits |
| You’re doing high-volume prospecting daily | All-in-one tool (Apollo, ZoomInfo) may be cheaper at scale |
| Your team uses Google Sheets as the primary workspace | LinkedIn Enricher keeps everything in one place |
| You’re a recruiter or agency building targeted lists | LinkedIn Enricher; per-credit pricing beats per-seat |
| You need CRM integration, sequences, and enrichment together | All-in-one tool; the add-on is enrichment only |
The add-on is not a replacement for full prospecting platforms if you need the whole workflow in one place. It’s the right tool when you already have the LinkedIn URLs and just need the contact data — without exporting CSVs, uploading to another tool, and reimporting.
What the Timestamp Column Gave Us
Every enriched row got a timestamp. That turned out to be more useful than expected: we could immediately see which rows ran through Datagma vs. LeadMagic by filtering the provider column, and we could schedule re-enrichment for the not-found rows in 60 days without having to remember which ones they were.
The status logging is the part most people overlook. It turns a one-time enrichment run into an auditable record.
Ready to run your own LinkedIn list? LinkedIn Profile Enricher for Google Sheets — bulk email and phone finder →