Attribution modeling is the practice of mapping touchpoints to monetarily relevant events within a customer journey, which is directly related to the return on investment of e-commerce websites, campaigns, or rankings in the organic search results. Attribution refers to the assignment of a value to specific interactions made by users or customers in different channels. Each interaction is to a certain extent responsible for the results of a digital asset and receives a corresponding numerical value, with which the results can be relatively accurately quantified afterwards.
The aim of attribution modeling is to analyze the role of individual touchpoints and channels for the customer journey. Customers come to a website through a variety of media such as ads, a hyperlink, or an article in the SERPs. Attribution modeling is designed to identify these different touchpoints and their causal role with regard to the ROI or conversion rate, thereby helping to weight the touchpoints and channels in order to be able to utilize multichannel marketing more efficiently and make more informed budget decisions.
Attribution or assignment generally refers to the attribution of psychological effects and motivations to their causes for actions. The attribution theory was formulated by Fritz Heider at the end of the 1990s. It forms the theoretical foundation for application in marketing, as it is customary today. An important point was the fact that, for example, a impressions of ads cannot be quantified because their effect on an actual purchase decision was not measurable. In addition, models such as cost per click (CPC), first click or last click sometimes did not provide correct attributions because they cannot show clearly which click was the decisive factor for a purchase decision which also caused a problem for distributing commissions in affiliate marketing.
Attribution is meant solve these problems by examining the causal connections between individual channels and purchases made and defining them within a general framework. Marketers are supposed to be able to look at how different channels and campaigns influence purchase decisions to be able to utilize the marketing budget as effectively as possible. An inventory of all channels and touchpoints between medium and user is therefore paramount to attribution modeling. Since conversion paths can be very different and sometimes individual channels overlap, these digital assets and their relationships to each other are quite important. Thus, multichannel tracking or tagging of the channels is essential.
Attribution modeling initially comprises several channels, which can be selected depending on marketing objectives:
There are generally interactions at different touchpoints before a purchase takes place. For example, a user sees an AdWords ad after entering a search term. He clicks the ad and is directed to a website where he signs up for a newsletter to receive a discount voucher. A few days later he returns to the website and makes a purchase after completing a product and price comparison.
Google Analytics has different attribution models in order to assign these channels and user interactions in and between these channels to a later purchase:
There are of course numerous factors that would make one or the other model preferable. Google offers a tool that highlights the effects on the ROI for a comparison of different models. It is also possible to create a custom attribution model to take into account business-related estimates and factors.
The correct association of channels and touchpoints can have many advantages for marketers. Usually, customer journeys are anything but linear. A customer journey usually involves several channels, devices and sessions. Attribution modeling helps to map these different customer journeys and thereby make them quantifiable. Attribution can answer questions such as what channels, campaigns, and touchpoints generate or are responsible for the highest turnover.
Marketers are thus given a detailed picture of the effectiveness of their activities and can identify cross-connections between channels, for example to identify ROPO effects, second screen usage, and cross-channel activities. At the same time, attribution allows the optimization of the activities so that the effectiveness of existing assets can be further increased. For example, content marketing and content with added value that initially generate traffic and then increase the reach of the digital assets. Marketers receive much better data when attribution modeling accurately depicts the touchpoints than with monitoring models that only consider the last click.