Let’s clear the air right out of the gate: We know exactly what you guys do when you hit a registration wall on SD Times.
You want to read that deep-dive whitepaper, grab an architecture cheat sheet, or watch a Kubernetes webinar, but you don’t want a sales rep calling you on a Tuesday. So, you type [email protected], [email protected], or spin up a temporary inbox on 10MinuteMail, click download, and ghost us.
Look, we get it. We’re developers and tech editors; we are just as paranoid about our inboxes as you are. But here is the hard truth about the publishing business: SD Times doesn’t have a paywall. We don’t charge you a subscription fee to read our curated stories, architectural breakdowns, or daily news.
We keep the lights on, pay our writers, and keep the content free because enterprise tech companies sponsor those webinars and whitepapers. When you use a burner email, our sponsors get a database full of bouncing, unreachable leads. If sponsors don’t get a return on their investment, they stop sponsoring. And if they stop sponsoring, we can’t keep delivering the high-quality content you come here to read.
It’s a value exchange. But asking nicely doesn’t work in software engineering. So, we decided to eat our own dog food and engineer a programmatic solution to stop the burner emails at the edge.
Originally, we looked at custom edge functions, but we needed something that played nice with our front-end lead-gen stack without adding codebase maintenance overhead. Here is how we automated our CRM hygiene using OptinMonster, Make (formerly Integromat), and Melissa, solving our CRM bloat for roughly a penny per lead.
The Economics of Catching a Developer
You can’t catch a tech-savvy audience with a simple RegEx check. If we just look for an @ symbol and a .com, you’ll bypass it in two seconds. To actually validate an email, you have to ping the mail server to see if the inbox is real, active, and not hosted on a known burner domain.
We integrated the Melissa Global Email Verification API because they run on a transparent, pay-as-you-go system rather than hiding behind a “Contact Sales” button. Melissa provides deep-dive SMTP checks alongside a rich data payload.
Here is the exact math on what it costs us to validate a form submission:
- Melissa charges roughly $30 for a bucket of 10,000 credits.
- A thorough verification check costs 4 credits.
- Therefore, $30 buys us 2,500 rigorous validations.
That works out to exactly 1.2 cents per lead. Spending a single penny to keep a toxic, bouncing lead out of our expensive marketing automation database yields an immediate, hard-dollar return on investment, and keeps our platform sponsors incredibly happy.
Architecting the “Anti-Burner” Low-Code Pipeline
Instead of burning engineering hours maintaining a custom serverless proxy, we built our real-time validation step directly into our automation layer using Make.com. This acts as our gatekeeper between subscriber acquisition and our core marketing database.
The Logic Flow
Our subscriber flow starts with OptinMonster capturing newsletter signups on sdtimes.com. When a visitor submits a popup form, OptinMonster fires a POST webhook to a Make scenario.
Because OptinMonster wraps its payload in a JSON array, the first step in Make is an Iterator module. This unwraps the array so each submission can be processed individually.
Calling the Melissa API
Once the email is extracted from the payload, Make hits Melissa’s Global Email Verification API using an HTTP GET request to the V4 endpoint:
https://globalemail.melissadata.net/v4/WEB/GlobalEmail/doGlobalEmail?id=YOUR_LICENSE_KEY&[email protected]&format=json
Melissa’s API returns a rich response for each email, including a DeliverabilityConfidenceScore (0 to 100), domain age, MX server information, and structural result codes. A typical response payload looks like this:
{
"Records": [{
"DeliverabilityConfidenceScore": "59",
"Results": "ES01,ES07,ES21",
"EmailAddress": "[email protected]",
"DomainName": "example",
"DomainAuthenticationStatus": "SPF,DMARC",
"ActivityLevel": "medium"
}]
}
Filtering on Result Codes
The magic happens in the Results string field. Melissa uses deterministic status codes to tell you exactly what’s going on.
- ES01: Confirms the email address is completely valid and deliverable.
- ES07: Flags that the domain uses catch-all routing.
- EE04 / EE03: Flags disposable burner domains or straight-up invalid mailboxes.
In Make, we position a Router module immediately after the Melissa HTTP call. We set up a strict condition: the workflow only continues down the path to our marketing automation system if the Results string contains ES01.
If you use a burner email or type a fake domain, you fail the router check. The workflow silently drops the submission before it ever touches our contact database.
Technical Gotchas: Battle-Testing Make.com
Moving this logic to an integration platform wasn’t without its implementation quirks. If you are building a similar workflow, keep these three platform behaviors in mind:
1. The Empty Records Array Bug (Content Compression)
During initial testing, Make’s HTTP module successfully returned a 200 OK status code, but the Records array from Melissa was completely empty. The data was being silently lost.
The Fix: Make’s HTTP module has a setting called Request compressed content enabled by default. While fine for most APIs, it breaks Melissa’s response parsing. Disabling Request compressed content and ensuring Parse response is set to True completely resolves the issue, allowing the full JSON object to populate.
2. The “Catch-22” Field Mapping
Make requires at least one live execution with parsed data before it knows what fields exist in downstream modules. When you first drop a Router into your scenario, you won’t see Records[]: Results as a selectable option.
The Fix: You must run the scenario manually at least once using a real, valid email address. Once Make observes a live successful payload, it registers the data structure, unlocking the Melissa fields for visual mapping in your Router conditions.
3. Array Unwrapping
Because OptinMonster bundles its webhook payloads inside a JSON array, a standard webhook receiver module in Make will appear empty or unmappable. Dropping an Iterator module directly after the webhook webhook is non-negotiable to expose fields like email, names, and IP addresses.
The Ultimate Result
The entire workflow runs completely asynchronously in the background. The user experience remains seamless: the subscriber never experiences loading delays on the frontend, while bad data is blocked at the gate. The Melissa API is fast enough that the entire loop—from OptinMonster submission to filtered contact insertion—takes less than two seconds.
By spending 1.2 cents to validate a record, we have drastically reduced our CRM bloat, protected our domain sender reputation, and ensured our sponsors get the high-quality data they pay for.
We promise to keep delivering the best software development news, tutorials, and architectural deep-dives on the web, completely free of charge. In return, all we ask is that you give us a real email address so we can keep the lights on.
(And hey, if you really don’t want the sponsor to email you, just hit “unsubscribe” on their first message. We promise they’ll honor it).
EDITOR’S NOTE: The code in this article was generated by AI and has been validated.
