A fun technique is correlation maps. I often am looking at correlations between a large set of attributes. Typically, this is done with a big X by X table with correlation coefficients in every cell. While detailed and accurate, that make a lot of numbers for someone to look through. So these large tables full of numbers are hard for clients to get the “big picture” of how thing interact with each other.
A solution is to put the correlations in a “Correlation Map”. All they are is a x,y grid with the distance between points being proportional to the correlation. If 2 dots are close together, those two factors are highly correlated. But if 2 dots are far apart then there is no correlation between those elements.
Since correlations are not a linear, the maps are not 100% accurate. You can’t take a ruler and tell exactly what the correlations coefficient is. But they are built to be generally correct, and so even a person with no statistical background can tell what factors are closely correlated, which ones are not, and relatively how they interrelate. See the example below:
I have a python program (that I created) that helps me make up these maps, but a person can just place the dots by hand based on a correlation coefficient table.
Often when studying a large group of factors and how they are correlated with each other, the question comes up what correlations are direct relationships (a “driver”) and which ones are just side effects of other correlations. The above graph shows that ads that are “Fresh” are also often “Up Beat” and “Informative”. But what an ad wasn’t “Up Beat” would “Fresh” and “Informative” still go together? If “yes”, then “Fresh” and “Informative” have a direct relationship independent of “Up Beat”. If “no”, then “Fresh” and “Informative” are only close (correlated) with each other because they are both close to “Up Beat”. In the second case, we call the relationship with “Up Beat” as the main driver.
Looking for direct relationships (drivers) is important to making use of the correlations you see in the maps. If your goal is to get a “Purchase Action” you want to find the direct relationships with “purchase action” to build better ads. We call this a “Driver analysis”. And one benefit of these correlations maps is you can also use them to see the results of the a “driver analysis”. Below are the results of a driver analysis I did for a client who wanted to know which of the attributes are the “drivers” of “Purchase Action”. The four drivers identified can then be used to build better ads.
The science of doing a driver analysis is not that difficult. In fact, there are many methods all that work equally. You could build a regression equation, and the attributes that enter the model are the drivers. Or you can use a tree method, identify the main driver (highest correlation), then look at where that main driver is present and where it isn’t, and find the next driver by finding what other attribute is highly correlated in both cases. Or Bayesian models to remove the influence of each driver found and then look for the next one. Lots of methods.
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