In the context of marketing and advertising, a linear attribution model is a method for assigning credit for conversions (such as a sale) to the various touchpoints (such as clicks on ads or interactions with emails) that a customer has with a company’s marketing efforts. This can help a company understand which marketing efforts are most effective in driving conversions and allow them to allocate their marketing budget more effectively.
In a linear attribution model, each touchpoint is given equal credit for the conversion. For example, if a customer clicks on three ads and then makes a purchase, each of the three ads would be given one-third of the credit for the conversion.
This is in contrast to other attribution models, such as the last-click model, in which only the last touchpoint is given credit, or the first-click model, in which only the first touchpoint is given credit.
To calculate credit using the linear attribution model, you would need to do the following:
- Identify all the input variables that contributed to the outcome you are interested in.
- Determine the total credit that should be assigned for that outcome.
- Calculate the credit assigned to each input variable by dividing the total credit by the number of input variables.
For example, let’s say you are trying to calculate credit for an increase in sales revenue. The input variables that contributed to the increase in sales revenue might include marketing efforts, product improvements, and changes in pricing. If the total credit for the increase in sales revenue is 100, then each input variable would receive 33.33 points of credit (100 / 3 = 33.33).
The linear attribution model is a simple method of credit assignment that can be useful in some situations, but it has some limitations. For example, it does not take into account the relative importance of the different input variables or their specific contribution to the outcome. In some cases, a more sophisticated attribution model may be more appropriate.
Role of Linear Attribution Model in Digital Marketing
Linear attribution models can be a valuable tool for digital marketers as they seek to understand the effectiveness of their marketing efforts and optimize their campaigns. In a digital marketing context, a linear attribution model can help a marketer understand the role that various digital touchpoints (such as ads, emails, website visits, and social media interactions) play in driving conversions. This can be especially useful for marketers who are trying to understand the effectiveness of multi-channel campaigns and how different channels contribute to the customer journey.
By using a linear attribution model, a digital marketer can:
- Allocate marketing budget: By understanding which marketing efforts are contributing to conversions, a marketer can allocate their budget more effectively, focusing on the channels and tactics that are most effective.
- Identify successful marketing channels: A linear attribution model can help a marketer identify which marketing channels are most effective at driving conversions.
- Optimize marketing campaigns: By understanding which touchpoints are contributing to conversions, a marketer can optimize their campaigns to focus on the most effective tactics and channels.
- Measure marketing ROI: A linear attribution model can help a marketer measure the return on investment (ROI) of their marketing efforts by attributing a value to each touchpoint and measuring the total cost of the marketing campaign against the resulting conversions.
- Understand customer behaviour: A linear attribution model can help a marketer understand the customer journey and how different touchpoints influence the decision to convert. This can be valuable information for optimizing marketing efforts and improving the customer experience.
In conclusion, the linear attribution model is a method of credit assignment that distributes credit evenly among all the input variables that contributed to a particular outcome. To calculate credit using the linear attribution model, you would need to identify all the input variables that contributed to the outcome, determine the total credit that should be assigned for that outcome, and then calculate the credit assigned to each input variable by dividing the total credit by the number of input variables. While the linear attribution model is a simple and straightforward method of credit assignment, it has some limitations and may not be suitable for all situations. In some cases, a more sophisticated attribution model may be more appropriate.