First of all, definition from Wikipedia.
In statistics, fractional factorial designs are experimental designs consisting of a carefully chosen subset (fraction) of the experimental runs of a full factorial design. The subset is chosen so as to exploit the sparsity-of-effects principle to expose information about the most important features of the problem studied, while using a fraction of the effort of a full factorial design in terms of experimental runs and resources.There are key terms here, "fraction" and "full factorial design".
"full factorial design" is the way to list all the combination of change in each factors to take all factors and combination and factors into consideration.
For an experiment with 2 factors, source of errors/difference in observation comes from X1, X2 and X1X2
For an experiment with 3 factors, source of errors/difference in observation comes from X1,X2,X3,X1X2,X1X3,X2X3,and X1X2X3
It's simple. But when number of factors increases, runs of experiment increase exponentially. it's 2n. In some cases, it's extremely expensive to conduct the full factorial design.
That's where "factual" comes. Number of runs does not increase when new variable comes in, but some combination of variable need to be compromised. Here is the example.
Conclusion of fractal factorial design, full factorial design is the dumb but expensive method. fractal factorial design is the cheap method with the trade off of cost.
P.S. I need to related it to the context of marketing. The technique can be used in design of marketing campaign test. For traditional marketing campaign, fractal factorial design might be necessary because it's expensive. But for online marketing campaign test, it seems full factorial design is possible.
P.S.2 Here is a youtube video that might be useful to explain fractal factorial design.



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