6 Common Attribution Models and When to Use Them

Attribution models are used to measure the effectiveness of marketing campaigns and to determine which channels are driving the most conversions. They are an important tool for marketers, as they allow them to understand which channels are most effective in driving conversions and how much each channel contributes to the overall success of a campaign.
The most common attribution models are Last-Click, First-Click, Linear, Time Decay, Position-Based, and Data-Driven. Each model has its own advantages and disadvantages, and below we’ll go over how to understand which model is best suited for a particular campaign.

Last-Click Attribution Model

The last-click model gives credit for a conversion to the last channel that was interacted with before the conversion took place. This model is often used by marketers who want to focus on optimizing their campaigns for the last touchpoint before a conversion takes place. It is also useful for understanding which channels are driving the most conversions at the end of a customer’s journey.

First-Click Attribution Model

When marketers use the first-click model, they’re choosing to assign credit for a conversion to the first channel that was interacted with before the conversion took place. This model is used to optimize campaigns for the first touchpoint in a buyer journey leading up to a conversion and is  useful for understanding which channels are driving initial interest in a product or service.

Linear Attribution Model

Some models assign credit to more than just one key touchpoint—one example being linear attribution. This model gives equal credit to all channels that were interacted with before a conversion took place. The linear attribution model is often used by marketers who want to give equal weighting to all touchpoints in a customer’s journey and understand how each channel contributes equally towards conversions.

Time Decay Attribution Model

Instead of assigning equal credit for multiple channels, there are also models like time decay attribution. This model gives more credit to channels that were interacted with closer in time to when a conversion took place than those that were interacted with further away in time. This model might be used by marketers who want to focus on optimizing their campaigns for recent touchpoints before a conversion takes place and understand how recent interactions can influence conversions more than older ones can.

Position-Based Attribution Model

The next model we’ll cover—position-based attribution—gives 40% of credit for a conversion to both the first and last channels that were interacted with before the conversion took place, while giving 20% of credit each to any other channels that were interacted with between those two points in time. This model is used to give more weighting towards both initial interest and final action taken before a conversion takes place while still giving some weighting towards any other touchpoints in between those two points in time as well.

Data-Driven Attribution Model

The last and most complex model we’ll mention is data-driven attribution. This model uses machine learning algorithms and data analysis techniques such as regression analysis or decision trees to determine which channels should be given credit for conversions based on past performance data from similar campaigns or customers’ journeys through different marketing channels over time. This model gives marketers an automated way of determining which channels should be given credit based on past performance data rather than relying solely on manual attribution models such as last click or first click models mentioned above.

Attribution: There’s No One Right Answer

In conclusion, there are many different attribution models available depending on what type of campaign you’re running and what type of insights you’re looking for from your marketing efforts. However, some models stand out due their ability provide valuable insights into how different marketing channels contribute towards overall campaign success or failure over time.