MTA har been many marketers dream for a long time, but is it just a dream?

Multi touch attribution is a technique for measuring marketing effectiveness. The idea is to track all touchpoints that leads to a desired outcome, i.e. following the complete customer journey. And thereafter assign a credit to each of the touchpoints.

Last click has been used by many marketers for attributing value. Everyone can probably understand why; it is easy and tangible. A customer clicked on my Facebook ad and purchased an item on my site, all credit for this purchase should be attributed to Facebook. Or not.. A bit more advanced marketers early on understood that even digital channels influence each other and that a customer probably has gone through a customer journey with multiple touchpoints. From that insight, and the possibility to aggressively track users behavior around the web, MTA was born.

There are different types of MTA models, with different degrees of sophistication. Generally you can classify them as either rule based or algorithmic. Rule based means that you manually define the rules for how the attribution should be allocated to the touchpoints. For example that could be even weighting or a time decay rule. Algorithmic MTA instead use statistical models and machine learning for attributing credit to a touchpoint

From the insights you can continuously take decisions on where to allocate marketing budget. In theory this is awesome. But in reality there are some significant problems with MTA. The biggest issue is of course that it is not possible to actually track all touchpoints even if you only focus on digital channels. The proponents argue that some data are better than zero data, but there is a big risk in taking decisions based on limited data. With the ever increasing limitations on tracking users in digital channels our conclusion is that on a short to a medium time horizon marketers should find other ways to measure their marketing effectiveness.

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