By: Martin Faith
Roivenue uses collaborative game theory to help marketers invest correctly. You don’t need to be a mathematician to use it, but a general understanding of the main principles certainly doesn’t hurt.
The traditional ways of looking at attribution are first or last touch. But honestly, these do not describe reality. The customer journey is more complex than ever, and the idea that the first or last ad someone saw online deserves full credit for a purchase was never really valid.
There are also linear and time-decay models, which are perhaps a bit more fair, but, again, is this how things tend to work? We don’t think so. Football (soccer) provides a useful example.
Your favorite football team just won a tournament and got 1 million euros to divide amongst them. Who should get this money?
The various models of attribution would treat the reward like this:
In our opinion, none of these models are correct. Everybody contributed to the win in some way, and it’s no easy task to assess who played the biggest role in the win. The same is true for marketing.
The reason why we simply cannot agree with these models is that they distribute the success across respective touchpoints based on a human decision. What is this decision based on? It’s impossible to tell. This is why we use the models we do – we think they are correct. Rather than valuing performance based on feelings or trying to hit the jackpot with our eyes closed, we turn to science and data.
Typical marketing and ad spends include Facebook ads, AdWords, paid search, and any number of re-targeting networks. The customer journey can, and usually does, include any number of combinations of these channels – often in separate, unique instances – and, if you want to calculate ROI accurately, you need to account for each of these instances and the role they played. There could be up to 75% difference in reported results among those common approaches.
This is why we use the Shapley value, which is, interestingly, actually rooted in football. The Shapley value tells us about the impact of a channel on the overall performance of conversion path. In other words, does the whole marketing mix benefit from activity on this channel or not?
The Shapley Value erases several flaws in previously-mentioned attribution models. It is fair, efficient, and since it is data driven, it is consistently remarkably accurate.
The other two models Roivenue uses are based on Markov chains. These tell us the direct impact of a channel based on the following touchpoint in the conversion path. A channel with high attribution value according to a 1st order Markov model has a positive impact on conversion rate of the following model in the sequence. In the case of Markov 2nd order models, the impact is on the channel two steps ahead in the sequence.
It sounds complicated, we know. But, the fact is that it is complicated. This is exactly why we built Roivenue – to turn your increasingly complex marketing data into simple, actionable, and accurate insights.
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