With companies getting aggressive and utilizing several digital marketing channels to reach their target audience, the customer’s journey through the funnel has evolved and has become complicated to track accurately. We all have experienced instances where we click on a PPC or display ad to check out a new product, next we receive an email advertising the product features and finally we make a purchase organically or through a direct visit to the product page. It is important to understand the value and contribution of each channel in the overall conversion process.

Marketing attribution modeling refers to tying the performance of an on-going or past campaign to different marketing channels with an aim to understand individual channel performance at different stages in the customer journey and shift budget for maximum ROI. Not only does it help with optimizing the marketing spend but also helps to understand the effective content that ideal customers would most likely appreciate via different channels throughout their journey. However, building an attribution model from scratch can sometimes be intimidating, overwhelming and also not feasible for certain teams. It requires a good amount of technical knowledge, accurate data collection infrastructure from all sources, resourcing and budgetary considerations. Thankfully, the free version of Google Analytics offers seven types of baseline attribution models and a custom model that can be easily applied to the existing analytics data.

In this article, I will talk about the 7 types of baseline attribution models available in Google Analytics and under what circumstances should each model be used. It is important to keep the following two points in mind while working with attribution models in Google Analytics:

• The default attribution model used in reports is the Last Non-Direct Click Model which has been covered later in the article.
• The custom model only captures data for click based channels and do not include the impression values.

Before we get into the different types of attribution models it is necessary to understand how the attribution models categorize data and calculate weight for different channels based on their interaction, it is also necessary to manage the 3 boxes above the data tables as shown below:

Conversion: Google Analytics allows the user to select the conversion goals where the attribution models need to be applied. The user can select one specific goal, a combination of goals or all goals for the modelling.

Type: Allows the user the either apply the models on all the data from different sources or just the traffic that interacted with the Google Ads at any point during their conversion journey.

Lookback Window: Google Analytics allows the user to select the number of days between 1 to 90 days prior to the goal conversion. Based on the user’s selection, the attribution model would compute the results using the impressions and clicks data for those days.

Now let’s get to know more about the 7 types of attribution models available to us:

Last Interaction Attribution Model assigns 100% of the conversion credit to the last interaction that the satisfies the selected condition. For example: a form submission on the website is one of the main conversion goals for B2B companies. Let’s say this is selected as the condition for the last interaction attribution model. We assume that the user visited the website blog through a referral, found the services offered by the company helpful and ends the session. Next, the user tries to look up more information about the company by searching organically and visits several pages on the website. Finally, the user makes up his mind to connect with the sales team and directly goes to the website to fill the form.

In this case, even though REFERRAL was the first interaction, ORGANIC was second and both channels played an important role in the conversion process, 100% credit will be attributed to DIRECT channel in Google Analytics

First Interaction Attribution Model assigns 100% of the conversion credit to the last interaction that satisfies the selected condition. In the above example, REFERRAL was the first interaction in the user’s journey to submit the contact form. Based on this model, all the credit will be assigned to REFERRAL.

If we were to map a funnel for the customer journey to fill out a contact form, this model is most efficient method to analyze the top of the funnel audience- who are not yet aware of the brand or the product/service offered by the company. This model helps to understand which channels are exclusively performing well for that top stage of the funnel which further helps to build a better content strategy and target audience accordingly.

Linear Attribution Model assigns equal conversion credit to all touch points in the conversion path. In our example, REFERRAL, ORGANIC & DIRECT will be assigned and equal weightage for each form submission using this model.

This model is preferred by advertisers who view all marketing initiatives as equal contributors throughout the conversion path. It is also a great introduction to multi-touch attribution.

Time Decay Attribution Model assigns incrementally more conversion credit to the touchpoints that are closest in time to the conversion. Applying this model to our example, DIRECT would receive the highest credit, followed by ORGANIC and REFERRAL in a decreasing order.

This model is best used by advertisers who want to measure performance for bottom funnel-heavy initiatives.

Position Based Attribution Model assigns 40% of conversion credit to the first interaction, 40% of conversion credit to the last interaction, and distributes the remaining 20% of conversion credit across all middle interaction. Using this model, 40% credit would be assigned to each, DIRECT & REFERRAL channels and ORGANIC would contribute 20%. Had there been another interaction between the first and last touchpoints, 20% would be equally distributed

This is a beneficial model for advertisers who want to measure the synergistic impact of top and bottom funnel initiatives and align their strategies to make the most out of it.

Last Non-Direct Click Model assigns 100% of conversion credit to the last non–direct touchpoint in a conversion path. In other words, it is similar to the Last Interaction Attribution Model minus the DIRECT channel. However, if the conversion path only includes DIRECT, it will be given 100% credit for the conversion. In our example, 100% attribution would be assigned to ORGANIC channel as it is the second last step before form submission through a DIRECT visit.

For advertisers who want to measure bottom funnel paid and organic campaigns, while deprioritizing direct as a channel, this is the model to use.

Last Google Ads Click assigns 100% of conversion credit to the last Google Ads click in a conversion path. This model prioritizes the Google Ads Clicks and assigns all credit for conversion to PAID SEARCH if the user clicked on the ad at any point throughout the conversion process.

This model is useful for advertisers who want to measure performance for bottom funnel Google Ads campaigns.

To better understand the difference between channel performances using different models, Google Analytics allows us to compare a maximum of 3 different models at the same time. This helps the marketer understand how much each channel contributes at different stages in the customer journey using different models available. For example, in most cases direct channel is not a high contributor if the modeling is done using the First Touch Attribution Model as the user is not aware of the brand as yet. But as the user comes across ads and emails and is aware of the product, they would visit the website directly to make the purchase. As a result, the contribution for the direct channel would be much higher if the last touch attribution model is used. Below is an image showing the comparison of data using First Interaction, Last Interaction and Linear Models:

You now have all the information needed to set up attribution models for your own website. Google Analytics uses the default channel grouping for the categorizing the contribution of the channels unless user-based channel grouping (custom) is created and selected. These are the baseline attribution models offered by the free version of Google Analytics and might not always fit your needs. In that case, you might want to explore the Custom Attribution Modeling in Google Analytics (Free) or the Data Driven Attribution Modeling in Google Analytics 360.