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As you might know, Return Path acquired OtherInbox some months ago. One result out of this merger is… a new dataset for us, 🙂 which contains some really interesting international email performance measures. It has been published yesterday. Return Path said, they
“analyzed over 3 million email campaigns sent to users of OtherInbox and calculated an engagement score for the most popular TLDs”.
I just had some quick fun playing around with it. Let’s have a look. Continue reading
In one of the last posts, we addressed the chi-squared test for independence. With this test, we wanted to calculate, if e.g. two subject lines have a significantly different impact on the absolute number of email opens. I provided you with a “flexible” solution. “Flexible” means, it can now easily be extended to your needs. One extension would be to determine the required sample size for each of your test cells a priori. There’s no question that a split A/B test, which only incorporates 2 x 100 recipients delivers a different reliability than one, which includes 2 x 1500 recipients. So here’s a solution for choosing the right sample size — again using the R package.
Continue reading
Quite often, I come across the question: how can I determine if my email test results are statistically significant? Recently someone asked this on EmailMarketersclub.com again. I won’t dive into the discussion, if (or when) this is necessary at all or if it wouldn’t be better to just rely on brainpower and gut feelings when evaluating tests in internet marketing. Instead, I’ll just provide you with a quick, yet well-founded and flexible solution. Continue reading
Let’s get back to the study from my last blog post for a minute. Not only had the outcome on subject line lengths caught my attention. (Remember: subject lines containing less than 10 characters are supposed to perform best.) Another thing I found intriguing was that the day of week, on which a mailing is delivered, would have no effect on response rates. Can this be true? Let’s have a practical look into some email data. Continue reading
Response modeling in most cases means mapping past reactions of your subscribers using statistical algorithms in a way that the outcome for each recipient (e.g. click or no click, open or no open, …) is explained by several explanatory attributes (e.g. age, email’s remote part, salutation, …). Such a view may help marketers, among other things, to …
Of course, open, click, and bounce rates are good and valuable performance indicators. However, they don’t tell you at once for example what groups of recipients showed more clicks and opens than others. And this would be a really interesting insight that holds great potential for optimizations. Decision tree models provide one easily interpretable representation of such mapped response behaviour. Let’s look at a practical example and explore how we could possibly make more of our data. Continue reading
On emailmarketing.de I recently mined about 550 job offers from TheEmailGuide.com to extract the common denominator. From the numerous results I pretty much liked this one: Continue reading
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