A/B tests are one of the best ways to optimize your website. However, it is not a magical cure for websites that are:
In this post I am going to go through the code for a relatively simple R Shiny app. It will predict Google Ads transactions and CPA (cost per acquisition). This will be for three months plus the current month. I will add a graph and a table of values, as well as four sliders. These are to adjust spend for the current month and the following three months. You’ll have to experiment with the app to get it to run the way you would like.
It can be a challenge to identify poor performing Google Ads. R can help by using statistics to find ads that should be replaced.
Excel is great for spreadsheet use. R is great for data analysis, reporting, data visualization and modeling. I can remember taking accounting in college, when I was not aware of Excel (it was a long time ago). We used sheets of paper that we had to adjust manually (erasers were invaluable). Some of the figures were adjusted so many times, that the paper would wear through.
Let’s say that you want to know if the number of your visitors are related to how much you spend on media. The number of visitors and media spend are both data attributes. If we want to understand the relationship between these two data attributes (beyond just eyeballing them), we have to understand if they covary. That means that when one variable moves from its mean, we would expect that a related variables would change in a similar way. This is the correlation between data attributes.
In this post I will walk through what covariance is, how it is calculated and use R to test for it. This is a very valuable technique to determine relationships between variables and it is not difficult to apply.