Fractional attribution is ad essentially effectiveness measurement beyond the last ad exposure. For those who are new to the field, the last-ad model has been dominating the digital measure for as long as we can remember. More recently, there has been a chorus of criticism on the last-ad approach, arguing quite correctly that even though last-ad exposure may possibly be more important than any other exposure, to completely ignore the latter does not make empirical and/or theoretical sense. Consequently conversion metrics based upon last-ad model are not as accurate as they are supposed to be.
Fractional attribution is a way to address the flaw of last-ad attribution by taking multiple prior exposures into account -- in practice, giving a percentage of attribution to each touch-point leading up to conversion.
In today's market there are two different types of products/services that claim to do exactly that. First, there are companies whose tools give users the option to assign their own weights/fractions to different exposure points. The problem with this approach is that most users have no clue what kind of weights they should be putting into the system. What makes it even worse is that the tool would not be able to tell us whether one weight schema is more valid than others. In fact, it is usually not even able to demonstrate empirically that fractional attribution is a better approach than the last-ad model. So to a certain extent, by using the tool we are making a leap of faith.
However, there is a more sophisticated attribution service in the market that would go beyond the user-defined-weight approach. Unlike the self-serve tool, the service usually includes an attribution engine that is able to create weights via statistical modeling based upon the campaign data that is being fed into it. As a consequence, the attribution is done in an automated fashion without any gut feeling guesswork from users. Moreover, attribution done this way is far more accurate and valid than what users can come up with from experience. It speaks directly to the campaign we are supposed to measure effectiveness on.
Unfortunately, at least at present, attribution modeling is not something that can be done cheaply. To crunch through log files is computationally intensive, which inevitably translates to high cost. And cost is one of the factors that prohibits its wider adoption.
Regardless of which fractional attribution approach we are using, the end point of an attribution process is no different in FORM from what comes out of last-ad model. We should expect to see the number of "conversions'" that are credited to each placement/creative. Unfortunately effectiveness measurement is never the end goal of optimization. Having proper measurement does not automatically lead to knowing how to make such media decisions as optimal media budget (re)allocation across different sites/placements.
To know precisely which sites/placements deserve additional spending and by how much is not a trivial thing. Many times such decisions are made with a lot of art but very little science, even with ad effectiveness measurement in hand. On the one hand, the industry definitely needs adequate performance metrics that are more accurate than those from the last-ad model. On the other hand, the industry would benefit even more if there were a decision engine that could take some of the art out of the media planning process.
There is no denying that tech firms that have been focusing upon fractional attribution have been doing a tremendous job in getting closer to a truer attribution model. However, to nail the attribution question without a good optimization engine/tool is putting the cart before the horse. I would safely predict that the demand for fractional attribution service would increase significantly if some of the results from fractional attribution could be fed into a media decision engine.
So for now, fractional attribution tools/services in their current form will remain something that are nice to have if there is extra budget -- but far from essential to the media decision-making process.