How selection bias will skew your activity mailing results

Here’s an interesting thing that I came across when evaluating a mailing for a client that I need to share.

Fallacy01

The observation

So what we’re looking at here is the revenue for a given group of customers from 06/2014 to 12/2014. (Note that the data presented here is completely fictional recreated in a spreadsheet, but follows patterns similar to the original data. See below for more details.)

This group of customers had been selected due to its inactivity as defined by the revenue in 06/2014 being smaller then a certain threshold (again, the actual selection was a lot more refined that is described in this model).…

read more

How to tell if your results are significant – a practical guide

Marketers frequently face a situation like this: In a survey it is found that 57% of women prefer product A, while 60% of men prefer product B.

In this article I will show how marketers – using only simple statistical analysis tools available in Microsoft Excel – can quickly and easily decide whether or not they can draw meaningful conclusions from such a result, or whether they may be making fatal mistakes by interpreting random noise as valid data.

stats mofo

Marketers frequently face a situation like this: In a survey it is found that 57% of women prefer product A, while 60% of men prefer product B.…

read more