![]() There is no ‘one-size fits all’ response to the Multi Touch Attribution vs. For example, in Corvidae the issue is removed through the use of AI and Machine learning to assess incrementality and conversion impact. In manual implementations this is something to be aware of but it is possible to work around the issue. The inference being how do you decide on the weightings and the potential for bias. Applies arbitrary weightings to conversion impact – some of the criticism levelled at Multi-Touch Attribution centres around the use of arbitrary weightings which are decided upon and then applied to specific touchpoints. ![]() In fact, that is the approach that our own attribution solution, Corvidae, takes. However, as we alluded to above, a good solution is going to allow you to ingest data from offline sources and ‘Walled Gardens’ and stitch that into the user journey. This is a criticism which is often levelled at Multi-Touch Attribution approaches and it is worth listing here as it could be the case depending on the attribution solution you choose.
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