Free LIVE Webinar Cross-device attribution without cookies: accurate measurement for tomorrow’s marketing Register here





An Introduction to Marketing Attribution

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. The idea that the first or last ad someone interacted with online deserves full credit for a purchase was never really valid; especially not today.


There are also linear and time-decay models, which are perhaps a bit more fair. But, again, is this really how advertising – or even the world – works? We don’t think so. In fact, the worlds most popular sport provides a useful example.


Marketing Attribution Models

Your favorite football team just won a match one to nil. They received a 100 million euro prize to divide amongst themselves. How should it be done?


Naturally, emotions can be fickle. Nobody wants sore feelings spoiling the synergy of the team for future games. However, there happens to be someone on the team with a marketing background. He suggests leaving it up to some common attribution models. After comparing what the various models say, a selection will be made based on what seems the most fair.


Here’s how the split would go according to each:

  • Last touch would give the player who scored the goal the cash. All of it.
  • First touch would give the player who started the sequence that led to the goal the cash. All of it.
  • Linear would give every player who touched the ball in the scoring sequence the same amount of cash.
  • Time decay would still have the cash split between all the players involved in the scoring sequence, but would progressively give players towards the end of the sequence larger and larger percentages.
  • Position based would give the player who began the sequence and the player who ended the sequence each 40% of the prize. Every player involved in the sequence, in between these two, splits the remaining 20%.


Obviously, none of these methods make any sense. It’s no wonder that professional clubs don’t pay their players this way. It would be mayhem. All of these models completely dismiss some of the teams most crucial components. Especially, the keeper who just pulled off a clean sheet.


Of course: 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 touch points 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, Ad Words, 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.


Need an analytics or attribution consultation? Schedule a live demo with one of our product experts!


Receive the latest insights in your mailbox

Learn how we helped 100 top brands gain success