Last Updated: May 20, 2022

Attribution modelling (in the context of attribution projects) is the process of assigning credit for a conversion to one or more non-direct touchpoints on a conversion path. 

In the context of attribution projects, we do not assign conversion credit to direct touchpoints (direct visit) on a conversion path unless the conversion path is made up entirely of direct visits. 

Attribution modelling is carried out to understand the customers’ purchase journey and to determine the most effective marketing channels for investment. 

A quick recap of touchpoints

A touchpoint (also known as ‘interaction’ or ‘touch’ or ‘event’) is an exposure to a marketing channel. 

There are several types of touchpoints. For example:

• Touchpoints based on position – First touchpoint, middle touchpoint, and last touchpoint.
• Touchpoints based on users’ behaviour – click touchpoint (which is basically clicking on ads), impression touchpoint (which is basically viewing an ad), direct visit touchpoint, etc.
• Touchpoints based on campaign type or traffic source type – keyword touchpoint, campaign touchpoint, Facebook touchpoint, Google Ads touchpoint, Twitter touchpoint, etc.

Any touchpoint other than the first and last touchpoint is called the middle touchpoint.

Introduction to conversion credit models

A conversion credit model is a rule or set of rules or a data-driven algorithm that determines how credit for conversions should be attributed/distributed to one or more touchpoints on conversion paths. It is another name for an attribution model. 

Through the conversion credit models, you can evaluate the effectiveness of your marketing campaigns. 

You use the model output to increase or decrease investment in a marketing channel and then monitor how it affects your conversions and sales over time.

No conversion credit model is innately good or bad. 

We select a conversion credit model based on our business model, advertising objectives, and the hypothesis we want to test. Conversion credit models are used for testing. They are just a means to an end. 

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Classifications of conversion credit models

The conversion credit models (aka attribution models) in an attribution project can be broadly classified into the following two categories: 

• Rule-based attribution models (non-direct)
• Algorithmic attribution models (non-direct)

All attribution models in an attribution project do not give conversion credit to direct visits unless the conversion path is made up entirely of direct visits. 

The ‘(non-direct)’ denotes zero conversion credit to direct visits on conversion paths unless a conversion path is made up entirely of direct visits.

Rule-based attribution models (non-direct)

Rule-based attribution models assign conversion credit to touchpoints in a conversion path according to certain predefined rules.  

These rules are used to identify the position of an interaction on the conversion path and then assign conversion credit solely on the basis of the position. 

Not all touchpoints are equally valuable. Some touchpoints are more valuable than others regardless of their position on a conversion path. But rule-based attribution models do not take this into account while distributing conversion credit.

Also, the rule-based attribution models in an attribution project do not give conversion credit to direct visits unless the conversion path is made up entirely of direct visits. That’s why they are referred to as ‘rule-based attribution models (non-direct)’

The following are examples of rule-based attribution models available in an attribution project:  

• First click (non-direct)
• Last click (non-direct)
• Linear (non-direct)
• Position based (non-direct)
• Time decay (non-direct)

First click (non-direct) attribution model

The first click (non-direct) model assign 100% credit for a conversion to the first touchpoint on a conversion path. 

However, this model does not give conversion credit to direct visits unless the conversion path is made up entirely of direct visits. 

Use the first touch attribution model, if brand building/brand awareness is very important for you. If you are a new player in your niche then you may need more brand awareness than your established competitors.

Consequently, your advertising goals should be more ‘brand building’ centric. So you would need to assign more conversion credit to touchpoints that initiated the conversion process.

Last click (non-direct) attribution model 

The last click (non-direct) model assigns 100% credit for a conversion to the last touchpoint on a conversion path.

However, this model does not give conversion credit to direct visits unless the conversion path is made up entirely of direct visits. 

Use the last touch attribution model when the least amount of buying consideration is involved. For example, if you are an FMCG (fast-moving consumer goods) company (like Tesco or Walmart) and you sell products that involve the least amount of consideration by a buyer, then you can use the last touch attribution model.

That is because you then do not need to assign more conversion credit to the first and middle interactions in your conversion path.

Linear (non-direct) attribution model

The linear (non-direct) attribution model assigns equal credit for a conversion to each touchpoint on a conversion path.

However, this model does not give conversion credit to direct visits unless the conversion path is made up entirely of direct visits. 

Use this attribution model, if you have a business model where each interaction with your customers is equally important for your conversions. For example, if you provide customer support service then each interaction with your customers is equally important for you. In that case, use the linear attribution model.

Position-based (non-direct) attribution model

The position-based (non-direct) attribution model assigns 40% conversion credit to the first touchpoint, 20% conversion credit to the middle touchpoints and 40% conversion credit to the last touchpoint. 

However, this model does not give conversion credit to direct visits unless the conversion path is made up entirely of direct visits.

If you have a business model or advertising objectives that value first and last touchpoints more than the middle touchpoints, then use the position-based model.

To be honest, I have never found any good use of this model. 

Time decay (non-direct) attribution model

The time decay (non-direct) model assigns more conversion credit to the touchpoints which are closest in time to the conversion.

However, this model does not give conversion credit to direct visits unless the conversion path is made up entirely of direct visits. 

Use the time decay model, if you are running time-sensitive promotional campaigns.

If you want to understand the buying behaviour of your customers during a promotional campaign, then you would want to assign more credits to the interactions which occurred closest in time to conversions, as they are more relevant than the interactions which occurred further in the past.

This is my second favourite model after the data-driven attribution model.

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Algorithmic attribution models (non-direct)

Algorithmic attribution models assign conversion credit to touchpoints on a conversion path according to an algorithm. 

These attribution models use data modelling in which predictive algorithms find and analyze statistically significant data from one or more data sources and then assign conversion credit to various touchpoints on a conversion path. 

Since the conversion credit is distributed algorithmically to various touches on a conversion path, the touchpoint which assisted the most (or had the most influence on the purchase behaviour) is likely to get the maximum credit for conversion regardless of it being the first touch, last touch or middle touch. All other touches get conversion credit in proportion to their contribution to the conversion. 

An example of an algorithmic attribution model is the data-driven attribution model (DDA model).

Data-driven (non-direct) attribution model

The data-driven (non-direct) attribution model assigns conversion credits to touchpoints that are most likely to drive conversions.

However, this model does not give conversion credit to direct visits unless the conversion path is made up entirely of direct visits. 

The DDA (data-driven attribution) model uses machine learning algorithms to analyses both conversion and non-conversion path data. Conversion path data is the data from users who converted on your website. Non-conversion path data is the data from users who did not convert on your website.

A DDA model is generated separately for each conversion type.

Therefore, there is always a possibility that a DDA model is generated for some particular conversions and not for others.

Whenever you are using the DDA model in your attribution project and the model is not generated for a particular conversion then Google Analytics will display the following warning message at the top of the attribution reports: “The data-driven model is not available for one or more of the selected conversion types”:

DDA Model eligibility requirement

Not every business can benefit from the DDA model.

That is because there is not just one, but several stringent requirements that need to be met as well as maintained before a business can use and benefit from data-driven attribution:

Requirement #1

Meet the minimum conversion threshold for each conversion type for which you want to use the DDA model.

Your attribution project must have recorded at least 600 conversions in the last 30 days for each conversion type for which you want to use the DDA model.

Since the DDA model is generated separately for each conversion type, there is always a possibility that a DDA model is generated for some particular conversions and not for others.

Whenever you are using the DDA model in your attribution project and the model is not available for a particular conversion, then it means that conversion did not meet the minimum conversion threshold in the last 30 days.

Requirement #2

Maintain the minimum conversion threshold for each conversion type for which you want to use the DDA model.

Just because your attribution project met the minimum conversion threshold requirement once, does not automatically make you eligible for ongoing data-driven attribution analysis.

The DDA model for a particular conversion type is available to you only as long as your attribution project maintains the minimum conversion threshold of 600 conversions in the last 30 days.

If your conversion volume drops below the minimum conversion threshold in the last 30 days, the DDA model will no longer be available to you. Therefore you would need to maintain the minimum conversion threshold for each conversion type for which you want to use the DDA model.

Requirement #3

Immediate access to a high volume of high-quality data

Your DDA model is only as good as the data you feed to it. If you feed it garbage, it will produce garbage. Garbage in, garbage out. 

You need to make sure that your Google Analytics data (especially goal conversion and ecommerce data) is as accurate as possible.

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