Why the problem matters
Most TAM/SAM/SOM calculations rely on lagging indicators: LinkedIn profiles that haven't been updated in months, enrichment databases built on stale or inferred data, and tools that prioritize coverage over accuracy.
The result is a distorted view of your market. You think you're targeting 10,000 potential buyers — but in reality, only a fraction of those roles actually exist or are reachable.
This creates a cascade of downstream problems. Outbound effort goes to non-existent roles. ABM campaigns are built on false assumptions. Forecasts get inflated by contacts who will never respond because they're no longer there.
In other words: you're optimizing strategy on top of bad data. And bad data doesn't just reduce efficiency — it kills precision.
The core distinction: probabilistic vs. deterministic market sizing
Traditional market sizing is probabilistic. You estimate based on what you can observe: headcount data, job postings, industry reports.
SMTP probing makes parts of that model deterministic. Instead of asking "How many engineers does this company probably have?", you ask "How many engineers can I verify actually exist — right now?"
That's a meaningful shift for ABM and competitive intelligence. A role that existed in Q1 may not exist today. A department that appeared in a LinkedIn search may have been restructured. SMTP verification reflects the current state of the inbox, not a snapshot from six months ago.
Step-by-step: refining TAM, SAM, and SOM with real-time probing
Step 1: Identify your target roles
Start with the job functions relevant to your ICP: engineering leads, sustainability officers, finance decision-makers — whatever roles you're targeting at a given account tier.
Step 2: Generate email permutations
Most companies use predictable email formats. Generate permutations for each target role at each account:
firstname@company.comf.lastname@company.comfirstname.lastname@company.comfirstnamelastname@company.com
For functional departments rather than named individuals, test role-based aliases:
engineering@company.comsustainability@company.comesg@company.comhr@company.com,hr1@company.com,hr2@company.com
Abstract's Bulk Email Verification lets you run these permutations at scale — thousands of addresses across entire account lists in a single batch request.
Step 3: Run verification and interpret results
The API contacts the target mail server directly via SMTP and returns a response for each address. The fields that matter most for market intelligence:
FieldWhat it tells youis_smtp_valid: trueThe inbox exists and is activedeliverability: DELIVERABLESafe to send; high confidence the address is realdeliverability: RISKYAddress exists but has risk signals (catch-all domain, recently created)is_disposable_email: trueTemporary address; likely not a real employee
If you generate 100 email permutations for engineering roles at a target account and 40 return is_smtp_valid: true with DELIVERABLE status, you now have a verified floor: at minimum, 40 engineers in that function with active, reachable inboxes. That's more actionable than a LinkedIn headcount estimate, which may include contractors, alumni, and roles that were never backfilled.
Step 4: Run a catch-all control test first
Before interpreting your results, you need to know whether the target domain is a catch-all server — one that returns "valid" for every address, regardless of whether the inbox actually exists.
The test is simple: ping a nonsensical address at the target domain before running your real permutations.
asdfghjkl123@targetcompany.com
If that address comes back as DELIVERABLE, the server is a catch-all. Your verification results for that domain aren't conclusive — the server confirms everything. Flag those accounts separately and don't count them as verified floors in your TAM model.
If the nonsensical address comes back as UNDELIVERABLE, the server rejects unknown addresses. That means your positive results are real.
This one step separates useful intelligence from noise.
Step 5: Analyze and refine your market model
Once you have verified results across your account list, you can build a more precise SAM:
- Replace estimated headcounts with verified inbox floors
- Identify which accounts have active presence in the departments you're targeting
- Deprioritize accounts where most permutations return invalid (the function may not exist, or the company may use a different email format)
- Flag catch-all domains for manual follow-up rather than bulk outreach
The output isn't a clean list — it's a tiered account map based on verified organizational reality.
Mapping departments that don't appear in org charts
Some functions don't show up in LinkedIn or enrichment databases: sustainability teams, internal ventures, special projects, newly formed units. You can probe for their existence before investing in full outreach sequences.
Test functional aliases:
sustainability@company.com
esg@company.com
innovation@company.com
ventures@company.com
A valid SMTP response on sustainability@ tells you the function exists and is monitored. That's useful signal before you build a campaign around it. A bounce tells you not to waste the sequence.
You can also estimate department density by testing indexed formats (hr1@, hr2@, hr3@). The point at which the server starts returning invalid responses gives you a rough floor on team size within that function.
Scaling this with Abstract's Bulk Email Verification
Manual verification doesn't scale, and more importantly, it doesn't generate usable intelligence fast enough to act on.
Abstract's Bulk Email Verification processes thousands of permutations across entire account lists or industry segments in minutes. Because it hits mail servers directly via SMTP rather than querying a cached database, results reflect current organizational reality — not a snapshot from the last time the database was refreshed.
For RevOps teams using this for TAM/SAM/SOM refinement, that distinction matters: you're not cleaning a list, you're auditing a market.
How it fits into your existing stack:
- Export your target account list from your CRM or ABM tool
- Generate email permutations in a spreadsheet or via script
- Run the batch through Abstract's Bulk Email Verification endpoint
- Import verified results back into your CRM, segmented by deliverability status
- Use verified floors as inputs to your market sizing model
Practical use cases
| Use Case | Traditional Approach | With SMTP Verification |
|---|---|---|
| TAM Sizing | Estimate headcount from LinkedIn + enrichment databases | Verify which roles actually have active inboxes at target accounts |
| SAM Refinement | Filter by industry and company size | Add verified presence in target departments as a qualification layer |
| Competitive Intelligence | Rely on public job postings and org chart tools | Probe competitor departments to estimate team size and function mix |
| ABM Prioritization | Rank accounts by firmographic fit | Rank by verified organizational density in your target function |
| Department Discovery | Search LinkedIn for team members | Test functional aliases to confirm whether a department exists |
Conclusion: from estimation to verification
Most market sizing models are probabilistic by necessity — you estimate based on what you can observe. SMTP-level verification makes parts of that model deterministic. You stop guessing how many engineers a competitor has and start knowing which ones have active inboxes.
The catch-all control test takes 30 seconds. The permutation logic is straightforward. And the output — a tiered account map based on verified organizational reality — is more useful than any enrichment database snapshot, because it reflects what's true today.
That's the edge: not better data sources, but a different kind of verification that runs in real time.
Get started
Run your first existence test with Abstract's Bulk Email Verification. Free plan includes 100 requests per month — no credit card required.
For implementation details, see the Email Validation API documentation.
Frequently Asked Questions
What is bulk email validation?
Bulk email validation is the process of verifying a large list of email addresses at once to determine which are deliverable, invalid, or risky. It typically combines syntax checks, domain verification, and SMTP-level probing to confirm whether a real mailbox exists at each address. Running validation in bulk saves time compared to checking addresses one by one and helps clean lists before sending campaigns or building market analyses.
How does SMTP-level validation work when verifying emails in bulk?
SMTP validation contacts the recipient's mail server directly and simulates the start of an email delivery to check whether the mailbox exists, without actually sending a message. The server responds with codes that indicate whether the address is valid, unknown, or rejected. APIs like Abstract's Bulk Email Verification API expose these results as fields such as is_smtp_valid and deliverability, making it straightforward to filter your list programmatically.
What is a catch-all domain and why does it matter for bulk validation?
A catch-all domain is configured to accept email sent to any address at that domain, even addresses that don't correspond to real mailboxes. This means SMTP validation will return a deliverable result for made-up addresses like xyzrandom123@company.com, making the results inconclusive. Before trusting bulk validation results, test each domain with a clearly nonsensical address first — if it comes back as deliverable, treat that domain's results as unreliable.
When should I use bulk email validation instead of real-time single-address validation?
Use bulk validation when you're processing a large existing list, such as a CRM export, a purchased contact database, or a set of prospected addresses generated from email permutation patterns. Real-time single-address validation is better suited to form submissions where you need an immediate inline response. For market sizing research or pre-campaign list cleaning, bulk validation is more efficient and cost-effective.
How can bulk email validation improve TAM/SAM/SOM market sizing?
Contact databases decay at roughly 22% per year, meaning a significant portion of the roles and addresses in any static list no longer exist. By generating email permutations for target roles and running them through a bulk verification API, you can identify which positions actually exist at target accounts right now. This converts probabilistic market size estimates into deterministic, SMTP-verified counts that better reflect real addressable opportunity.
What fields should I look at in a bulk email validation API response?
The most actionable fields are is_smtp_valid (whether the mailbox responded positively to SMTP probing), deliverability (a summary status like DELIVERABLE, UNDELIVERABLE, or UNKNOWN), and is_disposable_email (whether the address belongs to a temporary inbox service). Filtering on these three fields lets you segment results into safe-to-contact, remove, and needs-review buckets before importing back into your CRM or outreach tool.
