5 Ways to Stop Wasting API Credits on LinkedIn Enrichment
The most common ways teams burn through Datagma and LeadMagic credits with nothing to show for it — and the specific settings and Sheets formulas that prevent each one.
API credits for LinkedIn enrichment are not expensive — Datagma and LeadMagic both offer pay-as-you-go at fractions of a cent per lookup. But credits burned on rows that were never going to return a result, or rows that already had results, add up fast and produce nothing.
Here are the five most common ways teams waste enrichment credits, and exactly how to prevent each one.
The Credit Waste Problem
On a typical 500-row LinkedIn export, a team running without any optimization might realistically waste:
| Waste type | Rows affected | Credits lost |
|---|---|---|
| Invalid URL formats (company pages, Sales Nav) | 30–50 rows | 30–50 credits |
| Re-enriching already-found rows (no skip enabled) | 200+ rows on a re-run | 200+ credits |
| Wrong-audience rows (low ICP match enriched first) | 100 rows | 100 credits on non-priority contacts |
| No test batch before large run | — | Potential 500 credits on a misconfigured run |
| Fallback enabled unnecessarily | 175 fallback lookups | 175 credits with poor return on commodity lists |
That’s potentially 500+ wasted credits on a single 500-row run — credits that could have enriched another list instead.
Tip 1: Validate LinkedIn URLs Before You Enrich
The LinkedIn Enricher add-on sends URLs to the provider API as-is. If the URL is a company page, a Sales Navigator search, a shortened link, or a malformed string, the API will return no result — but you still consume a credit for the attempt.
Valid profile URL formats:
✅ https://www.linkedin.com/in/jane-doe
✅ https://linkedin.com/in/jane-doe-123abc
✅ www.linkedin.com/in/jane-doe
✅ linkedin.com/in/jane-doeInvalid formats that will always return empty:
❌ https://www.linkedin.com/company/acme-corp (company page, not a person)
❌ https://www.linkedin.com/sales/people/123 (Sales Navigator URL)
❌ https://lnkd.in/abc123 (shortened link)
❌ Jane Doe - https://linkedin.com/in/jane-doe (extra text around the URL)
❌ linkedin.com/in/ (no username)Google Sheets formula to flag invalid LinkedIn profile URLs:
=IF(
REGEXMATCH(
LOWER(TRIM(A2)),
"^(https?://)?(www\.)?linkedin\.com/in/[a-zA-Z0-9\-_%]+"
),
"Valid",
"Invalid — fix before enriching"
)Add this to a helper column, filter for “Invalid”, and fix or remove those rows before running enrichment. On a typical scraped or manually assembled list, 5–10% of URLs fail this check.
Tip 2: Use “Skip Already-Enriched Rows”
The most impactful single setting in the add-on. When enabled, any row that already has content in the email column is skipped entirely — no API call, no credit consumed.
Why this matters on re-runs:
If you enriched a 500-row list last month and got results for 320 rows, then added 50 new rows and want to enrich those:
| Scenario | Rows processed | Credits consumed |
|---|---|---|
| Re-run without skip enabled | 500 | 500 |
| Re-run with skip enabled | 180 (new + not-found only) | 180 |
| Credits saved | 320 |
The checkbox is in the Enrich tab of the sidebar. Enable it for every re-run of any list that has been partially enriched before.
One nuance: “Skip” checks whether the email column is non-empty. If a row has a “not found” status but the email column is empty, it will be re-processed — which is the correct behavior for not-found rows that you want to retry.
Tip 3: Sort and Prioritize Before Enriching
Not all rows on your list are worth the same enrichment spend. If you have ICP scoring, lead scoring, or even simple tiering (enterprise vs. SMB, key accounts vs. long-tail), enrich your highest-priority rows first.
Why this matters:
If your budget allows 300 credits this month and you have 500 rows, enriching the top 300 by ICP score means you’re spending credits on the contacts most likely to convert. Enriching the full list randomly means some of your best prospects may not get enriched at all.
Simple prioritization in Sheets:
- Add an “ICP Score” column (or “Priority” column with values like High/Medium/Low)
- Sort the sheet by that column, highest priority first
- Enrich top-down; stop when you’ve hit your credit budget for the session
The add-on processes rows in order from top to bottom. Sorting before enriching is a one-step change that materially improves how your credits are spent.
Tip 4: Test With a Small Batch Before a Full Run
Before committing 500+ credits to a large list, run a 10–20 row test batch. This costs 10–20 credits and catches the most expensive failure modes before they scale.
What to check in the test batch:
[ ] URL format: did the API accept all URLs without errors?
[ ] API key validity: did you get results rather than 401/403 errors?
[ ] Column mapping: are results writing to the right columns?
[ ] Provider response: do the emails look legitimate (not empty, not garbled)?
[ ] Fallback: did LeadMagic trigger on rows Datagma missed?If anything fails in the test batch, you’ve spent 20 credits to catch the problem instead of 500.
Which rows to use for the test: Choose a mix — a few rows you expect to find easily (US enterprise, clear company affiliation) and a few harder ones (EMEA, founder-level, SMB). This gives you a realistic preview of your overall hit rate before the full run.
Tip 5: Be Strategic About Fallback
Fallback (running LeadMagic when Datagma returns nothing) is the right call on high-value targeted lists. It’s not always the right call on bulk commodity lists.
The math on a large commodity list:
500 rows, 65% primary hit rate, fallback enabled:
- Primary credits: 500
- Fallback credits (35% of rows): 175
- Additional emails from fallback: ~61
- Total cost: 675 credits for 61 extra emails
- Cost per extra email via fallback: ~2.9 credits
If those 61 contacts are part of a targeted enterprise outreach where each has significant deal value, 2.9 credits per additional contact is cheap. If they’re on a bulk list where you’d have been happy with the 65% primary result, those 175 credits might be better spent enriching 175 new rows.
Decision rule:
- Average deal value > $1,000: enable fallback always
- Bulk lists, commodity volume, low deal value: test primary-only first
- Recruiting: enable fallback always
- Agency client deliverables: enable fallback, price credits into your rate
For the detailed decision matrix, see When Enrichment Provider Fallback Helps (And When It Just Wastes Credits) →.
Credit Cost Reference
Approximate costs as of early 2026. Check current provider pricing before budgeting.
| Provider | Pricing model | Approximate cost per credit |
|---|---|---|
| Datagma | Pay-as-you-go credit bundles | $0.005–$0.015/credit depending on bundle size |
| LeadMagic | Pay-as-you-go | $0.01–$0.02/credit depending on plan |
At these rates, 500 credits costs roughly $2.50–$7.50 depending on your bundle. A 500-row enrichment run with fallback enabled (675 credits total) costs approximately $3.40–$10.
The credit cost is rarely the bottleneck. The waste is in spending credits on rows that were never going to return useful results.
Monthly Credit Budget Template
Use this table to plan your enrichment sessions before committing credits:
| Item | Value |
|---|---|
| Total rows to enrich | ___ |
| Expected primary hit rate | ~65% |
| Rows where fallback will trigger | ___ × 35% = ___ |
| Expected fallback recovery rate | ~35% |
| Invalid URLs (estimate 5–8%) | ___ |
| Already-enriched rows to skip | ___ |
| Net credits to budget (primary) | Total rows − already-enriched − invalid |
| Net credits for fallback (if enabled) | Fallback rows × 1 |
| Total credit budget | Primary + fallback |
Running this calculation before each session takes two minutes and prevents the most common form of credit waste: committing to a full run on a list that’s 40% already enriched or 15% invalid URLs.
Enrich smarter, not just more. LinkedIn Profile Enricher for Google Sheets — with skip-enriched and fallback controls →