Using the attribution model for Multi-Channel Funnels (MCF) enables marketers to track the value of their channels. It assigns credit to the various touchpoints associated with their customers’ conversion paths. Some of these channels include paid search, organic search, social, direct and more.
In your Google Analytics, by default any value from eCommerce transactions is associated with the last channel that the user engaged with before they converted on the website. This is a straight-forward calculation, however, it doesn’t provide any insight into the performance that other touchpoints had in converting your customers.
In Google Analytics, you can use different attribution models to track the results for the value for each channel. There are 8 different sets of models with their own set of rules you can apply within analytics:
This model gives credits the full value of the transaction to the last channel the customer interacted with before their conversion. This means that no value is assigned to any other channels that the user engaged with, however it provides an overview into how the user first engaged with your brand.
This is the default model of Google Analytics. The last click model credits the full transaction value to the last channel the user clicked on, unless it is direct. This means that no value is assigned to any other channels that the user engaged with. However, it provides an overview into what channel ultimately influenced the user’s conversion.
This model gives credits the full value of the transaction to the last Google Ad that the user clicked on before they converted. Using this model helps to estimate individual Google Ad campaigns and their efficiency within the marketing mix.
This model gives full credit to the first channel which the user clicked on and interacted with the brand. Using this model helps to understand which channels create positive brand awareness, and ultimately result in a conversion.
This model splits the value of the transaction equally across all of the touchpoints involved in the lead up to a user’s conversion. Using this model has some limitations as it averages the credit across all of the channels, even if some had a greater contribution than others.
This model splits the credit across all of the channels, giving the most credit to the most recent touchpoint, and the least to the earlier interactions. Although the time decay model allows channels early in the conversion path to play a role, it has a limitation because it credits channels based on their position, rather than the influence they had on the end conversion.
This model gives credit to each channel based on it’s position within the user’s journey to conversion. By default, Google Analytics will credit 40% of the value to both the first and last touchpoint, and the remaining 20% is split evenly across the middle touchpoints. You can however, customise how you would like the credit to be split across each position.
Using a custom model means as a marketer, you need to have a deep understanding of your brand’s business model, which will allow you to modify other models based on your eCommerce needs.
You can view the customer example below to compare how the translation value is distributed across each touchpoint under each different model. Credit: Google
In your Google Analytics account, you can view your Top Conversion Paths to see the breakdown of value from your sales channels. For example, in this model you can see that the top conversion path is when users interact with an organic search channel, and a direct link before they converted.
In your Google Analytics account, you can view your assisted conversions. Assisted conversions are touchpoints that influence your consumer to convert but aren’t the main source or conversion point for the user. In this example below, you can see that 2,043 (33%) of the 6,273 conversions were assisted. Looking at Organic Search, you can see that for 1,014 conversions, the user engaged with an organic channel, however it wasn’t the last channel they engaged with before converting. On the other hand, organic search was at the end of the conversion path for 2,241 conversions. The assisted conversions highlight the importance of creating an omnichannel within your eCommerce strategy. Additionally, we can see that these channels don’t work in isolation, and have worked together to increase online conversions.
In Analytics, you can compare multiple attribution models and how they assign the value against each of your marketing channels.
In this example here, we are comparing the First Interaction and Last Interaction models. You can see that for 667 conversions, the user first engaged with a direct channel using the First Interaction model. Alternatively, for 1,210 conversions, the direct channel was the last interaction they had. This indicates that users are referred to the direct channel from another channel when they first engaged with the brand.
Using multiple models will provide a holistic view of all channels. Therefore, as a marketer, you can optimise your efforts efficiently, and increase your conversion rates. You can select the model(s) that will provide you with the most useful data relating to your marketing mix.