Whether it\u2019s for outreach purposes or link removals, you won\u2019t get far without searching for email addresses, this of course can be time consuming but well worth it and a manual approach is always recommended, this way you\u2019re going to be able to contact the right person first time and have a more tailored approach when you\u2019re doing so – but of course there are ways to speed this process up.<\/p>\n
There are times though when you can\u2019t find contact details, even legitimate sites don\u2019t always display a clear way to contact them so there are a number of ways you can try and get the details, this might be checking Whois data, checking to see if they have social accounts, I\u2019ve had some good success in the past using services such as spyonweb.com to see if they have other properties with shared analytics that might be displaying the contact details or using an email validation service and having a guess at common email address.<\/p>\n
Recently I was doing a large link clean-up; removing all the poor quality links that have been built to a new client\u2019s site over the years and I was left with around 100 domains that had no visible contact details or contact forms, now I could just disavow the links and hopefully that will be enough but there\u2019s no fun in that and I prefer to remove links than just disavow to ensure they don\u2019t get caught in an algorithm update at some point in the future \u2013 The problem is, this is now a big task.<\/p>\n
To solve this problem and try to speed up the process I wrote a Python script that basically tests a list of URLs against common email names such as \u2018admin@example.com\u2019, \u2018info@example.com\u2019, \u2018support@example.com\u2019 etc. etc.<\/p>\n
Here it is in action\u2026(play it in full screen)<\/strong><\/p>\n