Users complain about inbox overload. Email marketing evangelists on the other hand pride themselves with new record heights every year. So advertisers are to “blame”, aren’t they? Well, not so fast. Continue reading
I thought I’d share some more material with you with regards to the 250 Email Experts on Twitter blog. This material is more or less a byproduct that fell off while extracting the email influencers. That means the numbers are quick & dirty – mainly because the geo data append lead to inaccurate results. Yet, they are still good enough to end our working week: Let’s have a look. Continue reading
Twitter is growing. Although I myself haven’t posted very much, I do check tweets regularly. If you follow the right people, Twitter is a great way to be the first to know about important news & updates in email marketing.
But who are the right ones? I’m not sure if I know them all. But the machine knows – by doing some nifty calculations. Thus, it’s time to look closer at all the email experts: “Who’s hot, and who’s not?”
Here is what I found: Continue reading
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Are you looking for your industry averages? Then have a look at MailChimp’s Email Marketing Benchmarks. They just updated their data.
The company is probably one of the most popular email service providers, serving more than two million users which range from startups to Fortune 500 companies. So the numbers are somewhat significant. A former study dates from 2010 – I mentioned it a while ago. The new one again pulls open rates, clicks, bounces, complaints, and unsubscribe rates. It includes 669 million sends, that’s 65,000 campaigns, which went at least to 1000 subscribers each.
However, if the table looks like a data cemetery to you, check out the following two figures. I condensed the numbers and put them into an interactive graph using the wonderful rgl. This way, you can better compare all the different industries.
RFM stands for Recency, Frequency, Monetary Value. It’s a simple and yet effective customer scoring algorithm that has been used for about a hundred years, now. Especially in catalogue marketing, because RFM saves costs: The method discriminates possible responders from non-responders before the send out. RFM scores correlate with the likelihoods of responding to the next offer.
Email marketers do also use RFM analysis. Less, to save costs (email is “for free”), but more to predict subscriber values and to: Continue reading
Multivariate testing is a bit underrated. Marketing weblogs mostly focus on A/B or A/B/n tests. Those are quickly set up. But they often provide only incremental gains. MVT are more promising with regards to the outcome. Let’s look at how they work. Continue reading
When testing, it’s a good idea to have some formulas to hand. For instance in split A/B/n test scenarios, you may want to inspect the relationship between sample size, level of significance, and power. Also when renting lists, no one likes to buy a pig in a poke. Instead, the campaign has to be tested on a small segment first. Only if the test turns out to provide a good return on investment, the full run will be booked.
However, the question is, how many recipients should one book for the test? Including too many recipients would only cost in case the list proves to be unprofitable. Renting too few subscribers on the other hand bears the risk that the test results are due to chance. Here’s a hands-on solution. Continue reading
In a discussion on emailmarketersclub.com I just stumbled over some interesting benchmarks that have been published by MailChimp a while ago. Email senders have always been keen on comparing themselves to others in order know where they stand. The reported data, although from 2010, might be a good reference for small enterprises. From a consultant perspective, it would also be interesting to know if there are industry groups that perform similarly good or bad. Well, have a look at my fancy little email marketing benchmark cube… Continue reading
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.