This case study breaks down how we doubled the cold email results for a business broker (and longtime MailShake customer) after a single A\/B test.<\/p>\n
You\u2019re going to see how we helped Robert Allen of Acme Advisors & Brokers<\/a> turn a few \u201cnegative replies\u201d into a campaign that landed multiple appointments per day thanks to a new A\/B testing strategy.<\/p>\n Plus, I\u2019ll show you why our lead generation agency<\/a> started running \u201cqualitative A\/B tests<\/strong>\u201d and how it\u2019s helped us take a single email from a 9.8% to 18% reply rate after writing just one feedback-guided<\/em> variation.<\/p>\n Campaign Stats: You\u2019ve probably been told that you should A\/B test your cold emails.<\/p>\n But after talking with data-scientists and marketing gurus like Brian Massey at Conversion Sciences<\/a>\u2026<\/p>\n …It turns out most of us (myself included) have been A\/B testing cold emails wrong!<\/p>\n Gasp!<\/em><\/p>\n Here\u2019s the biggest mistake cold emailers make when A\/B testing:<\/p>\n \u201cWe look at <\/em>reply rates<\/em><\/strong> instead of the actual <\/em>replies<\/em><\/strong>.\u201d<\/em><\/p>\n Yep, now, when I run A\/B tests, I don\u2019t care about reply rates. At least, not at first.<\/p>\n Why? According to data-scientists, reply rates are not a reliable metric until you get 100 replies <\/em>per cold email variation. (Learn more on statistical significance here<\/a>.)<\/p>\n
\n<\/strong> 4 emails
\n206 prospects
\nOpen rate: 65%
\n30% reply rate
\n64 replies total
\n30+ meetings generated <\/strong><\/p>\nThe New Cold Email A\/B Test<\/strong><\/h2>\n
How To Run Cold Email A\/B Tests Like a P.h.D<\/strong><\/h2>\n