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Since then, we’ve continued our research and have constructed an improved system to identify undeliverable and risky email addresses so you can be confident in your sending, instead of relying on outdated and inaccurate checks.
Our data scientists discovered that not all hard bounces are equal – one hard bounce isn’t enough to discount an email address for everyone.
The %d macro is extracted from the supplied mail from address.
If an SPF record is supplied, it is used for the initial evaluation instead of any record published in DNS for the domain.
In fact, 95% senders who remove disposable addresses will see no change or an increase in their engagement rate while decreasing their overall traffic.
We know how important user experience is for your success (keep your eyes peeled for some exciting UX changes in the Spark Post app).
With it, we started with the industry knowledge and worked with our team of data scientists to validate best practices and create our solution.
With this release we have also added typo detection and suggestion, plugging a top source of lead loss for customers.They use an actual RFC 7208 compliant library (pyspf) for tests and will dynamically test for processing limit errors (no other testers I'm aware of do this).This site uses a caching DNS resolver, so for tests that use live DNS, results will be cached for the Time To Live of the DNS record.When Recipient Validation suspects an email address will hard bounce, we surface that so you can make the decision on whether you want to take on that risk.Recipient Validation helps you identify low-quality addresses.