In order to understand Non-Linear Attribution we should first look at standard Attribution modeling:
An attribution model is the rule, or set of rules, that determines how credit for sales and conversions is assigned to touch-points in conversion paths. For example, the Last Interaction model in Analytics assigns 100% credit to the final touch-points (i.e., clicks) that immediately precede sales or conversions. As we can now see more of the actual interactions a buyer makes on the path to purchase, attribution modeling has become more sophisticated and precise. Some of the other attribution models include: Last Non-Direct Click, Last AdWords Click, First Interaction/First Click, Linear, Non-Linear (we feel this is the most effective model), Time Decay and Position Based.
The B2B buying process typically involves a buying team comprised of a number of influencers and decision makers who research purchase decisions online over a fairly extended amount of time. As they move through the sales funnel the members of buying team are consuming numerous pieces of content to inform them on the best vendor or product for their needs. It is clearly not a linear process, so, a non-linear model is required to measure attribution.
Sirius Decisions reported in 2015 that in an average buying cycle, B2B buyers have 12 to 18 non-human and human interactions, each of which needs to be accounted for in the buyer’s journey map. Through the use of Buyer Intent Data and an Account Based Marketing process we have the ability to track content consumption and how each contact moves the buyer through the funnel. While still not an exact science we believe that the non-linear attribution model gets to a much more accurate result.