I'd love to hear how I would analyze aggregated data from a email experiment.
My design is very straightforward. I wanted to test two versions of the subject message on the email opening rate, and four versions of content messages. But, I've not used and interaction between content and subject message.
For this, I've used two types of messages randomly assigning them (about 50% each) to 1600 email accounts. Then, with another batch of 1800 accounts I tested the content message by assigning four versions of the message at a rate of 25% each experimental message.
What would be the best strategy for acquiring some extra insight from data other than the simple percentage figures? Is there any test that could provide extra explanation?
data <- data.frame( subject=c("A","B","A","A","A","A"), content=c("A","A","B","C","D","E"), opens=c(475,488,321,312,299,300), clicks=c(158,168,189,174,159,165), total=c(798,802,449,452,448,451) )