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Last Updated: May 26, 2022

The linear attribution model assigns equal credit for a conversion to each interaction on a conversion path

If you have a business model where each interaction is equally important for your conversions then you can use the linear interaction model.

For example, if you run a customer support service then each interaction with your customers is equally important for you. In that case, you can use the linear model.

The following examples will help you to understand how the conversion credit is calculated in the case of the linear attribution model:

Consider the following conversion path with a path length of four:

This conversion path can also be represented by the following data table:

Under the linear attribution model, the conversion credit for each interaction would be calculated as 1 / conversion path length.

Since the length of our conversion path is 4, the conversion credit for each interaction would be calculated as 1 / 4 = 25%:

• Campaign A gets 25% credit for the conversion.
• Campaign B gets 50% (25% + 25%) credit for the conversion.
• Campaign C gets 25% credit for the conversion.

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Consider the second conversion path with a path length of three:

This conversion path can also be represented by the following data table:

Under the linear attribution model, the conversion credit for each interaction would be calculated as 1 / conversion path length.

Since the length of our conversion path is 3, the conversion credit for each interaction would be calculated as 1 / 3 = 33.33%

• Campaign A gets 66.66% (33.33%+33.33%) credit for the conversion.
• Campaign B gets 33.33% credit for the conversion.
• Campaign C gets 0% credit for the conversion (as campaign C is not on the conversion path).

The total conversion credit for campaign A so far

= (conversion credit for campaign A on the first conversion path + conversion credit for campaign A on the second conversion path) / 2

= (25% + 66.66%) / 2

= 45.83%

Total conversion credit for campaign B so far

= (conversion credit for campaign B on the first conversion path + conversion credit for campaign B on the second conversion path) / 2

= (50% + 33.33%) / 2

= 41.67%

Total conversion credit for campaign C so far

= (conversion credit for campaign C on the first conversion path + conversion credit for campaign C on the second conversion path) / 2

= (25% + 0%) / 2

= 12.5%

Consider the third conversion path with a path length of two:

This conversion path can also be represented by the following data table:

Since the length of our conversion path is two, the conversion credit for each interaction would be calculated as 1 / 2 = 50%

• Campaign A gets 50% credit for the conversion.
• Campaign B gets 50% credit for the conversion.
• Campaign C gets 0% credit for the conversion (as campaign C is not on the conversion path).

The total conversion credit for campaign A so far

= (conversion credit for campaign A on the first conversion path + conversion credit for campaign A on the second conversion path + conversion credit for campaign A on the third conversion path) / 3

= (25% + 66.66% + 50%) / 3

= 47.22%

The total conversion credit for campaign B so far

= (conversion credit for campaign B on the first conversion path + conversion credit for campaign B on the second conversion path + conversion credit for campaign B on the third conversion path) / 3

= (50% + 33.33% + 50%) / 3

= 44.44%

The total conversion credit for campaign C so far

= (conversion credit for campaign C on the first conversion path + conversion credit for campaign C on the second conversion path + conversion credit for campaign C on the third conversion path) / 3

= (25% + 0% + 0%) / 3

= 8.33%

If we assume that there are only three conversion paths in our selected time period, then the sum of conversion credit for campaigns A, B and C under the linear attribution model would be:  47.22% + 44.44% + 8.33% = 99.99% (rounded off to 100%)

Since the sum of the conversion credit for three campaigns is 100%, this proves that our conversion credit calculations are all correct.

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