The linear attribution model is a method used to calculate the credit for each touchpoint in a customer’s journey. It is a simple and straightforward way of attributing value to each touchpoint in the conversion process. The model assumes that the value of each touchpoint is equal and that the total value of the conversion can be divided equally among all touchpoints.
How Does the Linear Attribution Model Calculate Credit? The linear attribution model is based on the idea that every touchpoint in the customer journey contributes equally to the final conversion. This means that each touchpoint is credited with the same amount of value, regardless of whether it was the first touchpoint or the last. The model is often used when there is limited data available on the customer journey, as it is easy to implement and provides a basic understanding of the customer journey.
To calculate the credit for each touchpoint in the linear attribution model, the total value of the conversion is divided by the number of touchpoints in the journey. For example, if a customer made a purchase worth $100 and had five touchpoints in the journey, each touchpoint would be credited with $20.
The linear attribution model has some limitations, as it does not take into account the importance of each touchpoint in the customer journey. For example, if a touchpoint was the first interaction with the customer, it may have had a greater impact on the conversion than a touchpoint that occurred later in the journey. In this case, the linear attribution model would not accurately reflect the impact of each touchpoint.
To overcome these limitations, more advanced attribution models have been developed, such as the time decay model and the U-shaped model. These models take into account the importance of each touchpoint in the customer journey and provide a more accurate representation of the impact of each touchpoint on the conversion.
The time decay model credits touchpoints that occur closer to the conversion with more value. This means that the first touchpoint is credited with the highest value, and the value decreases as the touchpoints get further away from the conversion. The rate of decay can be adjusted to reflect the importance of each touchpoint in the customer journey.
The U-shaped model credits the first and last touchpoints with the highest value, and the value decreases for touchpoints in the middle of the journey. This model assumes that the first and last touchpoints have the greatest impact on the conversion and that touchpoints in the middle have a lesser impact.
In conclusion, the linear attribution model is a simple and straightforward way of attributing value to each touchpoint in the customer journey. While it has limitations, it provides a basic understanding of the customer journey and can be useful when there is limited data available. More advanced attribution models, such as the time decay model and the U-shaped model, provide a more accurate representation of the impact of each touchpoint on the conversion and should be used when more data is available.
