What are variable interactions? Simply put, it is when the setting for one variable in your test positively or negatively influences the setting of another variable. If they have no effect on each other, they are said to be independent.

Let's look at a simple example: We are selling cars and want to test two different headlines and two different pictures. So we have a total of four possible recipes based on our two variables.

Landing Page 1 - Headline "A", Picture "A"

Ferraris are Really Fast

Landing Page 2 - Headline "A", Picture "B"

Ferraris are Really Fast

Landing Page 3 - Headline "B", Picture "A"

Volvos are Very Safe

Landing Page 4 - Headline "B", Picture "B"

Volvos are Very Safe

If you believe that there are no interactions, then you must also believe that there is a "best" headline regardless of the accompanying picture, and that there is a "best" picture regardless of the headline used.

Clearly this is not the case. Each variable depends on the context in which it is seen. Recipe #1 has a very strong positive interaction (connecting the speed and power in the picture with the word "Fast" in the headline). #2 has a strongly negative interaction (making you think about the consequences of fast driving - "speed kills"). #3 has a mildly positive interaction (supporting the notion that you can go fast and still be safe). #4 has a positive interaction (playing on the fear of accidents and highlighting Volvo's safety record).

In online marketing we want interactions. We want the picture to reinforce the headline, and the sales copy, and the offer, and the call to action...

Tuning methods such as A-B Split Testing and Multivariate Analysis assume that there are absolutely no interactions between your variables (that they are completely independent of each other). Obviously for online marketing this is an absurd assumption. So while you may be able to get some positive results by ignoring interactions, you may not be getting the very best results.

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