Beyond heuristics: Algorithmic multi-channel attribution
Looking to improve your attribution modeling for better insights into your analytics? Columnist David Fothergill shares his method, which uses a Markov model to determine the relative value of each channel in your conversion path.
Marketing attribution continues to provoke many discussions, theories and debates these days. As stated in the intro to Christi Olson’s recent Search Engine Land column, “Proper attribution modeling is one of the biggest challenges facing marketers today.”
Along with difficulties caused by holes in data (such as connecting user journeys across devices), the oversimplification of traditional first- and last-click models is highlighted.
These models fall into a class of “heuristic” rules. A heuristic, by its nature, is a simplification of problem to more of a “rule of thumb,” removing complexity in favor of a quick analysis. In the case of attribution modeling, this means assigning values across positions in the chain, regardless of actual impact on the completion of a sale.
The step beyond this is to algorithmic attribution — complete analysis of the available data to determine the true impact of a given touch point on conversions. Rather than “shortcutting” and applying a blanket position or time-decay rule, algorithmic attribution involves having a custom model and weightings for each touch point based on your own user dynamics.
Alongside a truer picture of channel value, the deeper understanding provides a starting point in progressing away from descriptive analytics towards the realm of predictive and prescriptive analytics.
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