How To Clean Your Email List (Kartra Email Sequence)
What Is Email Validation or Scrubbing? – Raj Domains
There are basically 2 main steps to really cleaning your email lists. The First is email Validation or Scrubbing but essentially what you do here is remove as much KNOWN crap as possible. Verification is much costlier part of your overall data hygiene process, so email validation removals as much as possible before you need to verify, which saves you time and money. There are many steps in email validation your list of harmful bad emails that you DO NOT want to mail to! Key Words & Profane – removing an email address with specific words like: spam, www, shit, admin etc.
Bad Domains – like it says, there are known bad domains out there that are associated with spam traps or honey pots, so you would want to scrub against this list and remove any emails you have with these domains. Domain Extensions – removing emails like.org,. Numerical emails – emails that start with ONLY numbers is typically a bad email and for the most part, most only numeric domains as well. Now understand the whole idea of email scrubbing your list is to remove as many bad known emails as possible, but it’s very possible you’ll end up removing some good ones! But if you really want to be safe then losing 1% -2% of your emails is worth not hitting a spam trap or losing your server.
On that same note, its a known fact that AOL for example puts out rewards of 500,000 honey pots a day from abandoned email accounts so its impossible for ANYONE to remove 100% spam traps or honey pots as there are way to many daily being added and impossible for us know that. Last thing, typically if your scraping list or buying lists and have no idea where they are coming from, then you’ll probably see all of these examples. If you have your own opt in list or they are generally business lists, then you will not see half of these things in your list, but it’s better to be safe than sorry and email validation is the way to go!
5 Simple Steps To Scrub Your Email List
In this post, I’ll teach you how to scrub your email list. After a few minutes, you’ll get an email with the exported list attached. Run your inactive subscriber list through an email list cleaning tool like EmailListVerify. After you’ve cleaned it, take the list of valid email addresses and segment them into a separate list in your ESP. To do this with Sumo, click Contacts, then go to the Groups tab.
We’re going to send a re-engagement campaign to these people to either clean them from our list permanently or get them back as active subscribers. You want to get them to open your email by using an interesting subject line so they can be put back on your active subscriber list. After your list is cleaned and you’ve discovered who your active and inactive subscribers are, you’ll want to completely stop sending emails to anyone who didn’t respond to the re-engagement campaign. If you really want a clean list, one final is removing any email subscribers who reply with canned-response emails. Once you do see it, remove those emails from your list – and you’re done!
If you successfully cleaned your list, you should see a dramatic increase in open rates since you’re no longer sending to inactive or bogus email addresses. Cleaning your list and keeping it clean are two different stories! It has all the steps on one page for you to quickly scrub your list any time you need to.
A Simple Email Crawler in Python
A queue of urls to be crawled new urls = deque( ). # a queue of urls to be crawled. A set of urls that we have already crawled processed urls = set(). Process urls one by one until we exhaust the queue while len(new urls): # move next url from the queue to the set of processed urls url = new urls. Process urls one by one until we exhaust the queue. After we have processed the current page, let’s find links to other pages and add them to our url queue.
Add the new url to the queue if it’s of HTTP protocol, not enqueued and not processed yet if link. Add the new url to the queue if it’s of HTTP protocol, not enqueued and not processed yet. Parse import urlsplit from collections import deque import re # a queue of urls to be crawled new urls = deque( ) # a set of urls that we have already crawled processed urls = set() # a set of crawled emails emails = set() # process urls one by one until we exhaust the queue while len(new urls): # move next url from the queue to the set of processed urls url = new urls. Startswith(‘/’): link = base url + link elif not link. Startswith(‘http’): link = path + link # add the new url to the queue if it was not enqueued nor processed yet if not link in new urls and not link in processed urls: new urls.
process urls one by one until we exhaust the queue. Add the new url to the queue if it was not enqueued nor processed yet.