Attribution is crucial for understanding the effectiveness of your advertising efforts. AdRoll's Attribution Settings allow you to define how credit is assigned to different touchpoints that lead to a conversion, providing valuable insights into your campaign performance. This article will guide you through the various settings available in AdRoll's Attribution Dashboard.
Accessing Attribution Settings
You can find AdRoll Attribution Settings under the "Analytics" section in your AdRoll dashboard, specifically by navigating to Analytics > AdRoll Attribution > Settings.
Choosing an Attribution Model Template
AdRoll offers several pre-defined attribution model templates to help you quickly establish rules for assigning conversion credit. Each model has unique rules for attributing credit to AdRoll products that contribute to a conversion, allowing you to see how performance changes as you adjust the model to align with your goals.
Here are the available templates:
Blended model that gives 100% of credit to the last touch with a priority on clicks over views. In this model, a conversion will be attributed to AdRoll as a click-through-conversion (CTC) if the conversion happened after a click within the lookback window selected in our dashboard’s attribution settings or as a view-through-conversion (VTC) if the conversion happened following an impression.
The default lookback windows (which are fully customizable) are:
- Retargeting: Click last 30 days, View last 7 days.
- Brand Awareness: Click last 30 days, View last 30 days.
AdRoll’s AI and Machine Learning models are optimized to drive incremental revenue versus clicks. Just because someone clicks on an ad does not mean they are going to convert. Focusing on reaching your target audience at each key moment of influence throughout their journey leads to incremental revenue versus optimizing towards clicks leads to conversions from consumers who were already likely to convert.
| Pros | Cons | Best used for |
|---|---|---|
| A blended attribution model provides the performance data needed to judge the true impact of display campaigns. Focusing solely on clicks ignores the influence of other channels. With this model, you take into consideration the value of previous touchpoints in the customer journey. | AdRoll's default model can be more complex to assess than single attribution models. It is not ideal for direct response campaigns or very short sales cycles. | More granular reporting with views and clicks; allows you to test new creatives and placements to ensure your strategy is built on a foundation of understanding your customers’ needs. |
Last Click Attributes 100% of credit to the most recent click prior to a visitor converting.
| Pros | Cons | Best used for |
|---|---|---|
|
Simple to understand. Useful when one single touchpoint drives the conversion. |
Ignores all other touchpoints. Biased towards bottom-of-funnel efforts. Won't represent cross-channel strategies with non-click oriented platforms, like CTV or audio. Provides limited ad optimization opportunities because insights are solely based on the final touchpoint which prevents us from understanding the full impact of the campaigns. |
Situations with very direct conversion paths. |
Attributes 100% of credit to the most recent click or view prior to a visitor converting.
| Pros | Cons | Best used for |
|---|---|---|
|
Simple to understand. Captures the final interaction, even if it's a view. |
Ignores all other touchpoints. Biased towards bottom-of-funnel efforts. Provides limited ad optimization opportunities because insights are solely based on the final touchpoint which prevents us from understanding the full impact of the campaigns. |
Situations with very direct conversion paths, including views. |
Attributes 100% of credit to the first touchpoint that brought a visitor into your purchase funnel.
| Pros | Cons | Best used for |
|---|---|---|
| Highlights the initial acquisition source; Good for demand generation. |
Doesn't show the full journey limiting the view of a customer path to conversion. Undervalues nurturing efforts. |
Understanding initial customer acquisition channels for Demand Generation campaigns. |
Attributes equal credit to every touchpoint in the customer purchase funnel.
| Pros | Cons | Best used for |
|---|---|---|
| Easy to understand basic multi-touch attribution approach. |
Doesn't reflect the varying impact of different touchpoints. Can make it difficult to see opportunities for optimizing your campaigns and product use. |
If you want a simple introduction to understanding multiple touchpoints. |
Note
With AdRoll attribution, only two products (Web and CTV Retargeting and Web and CTV Brand Awareness) currently contribute touchpoints. Because at most one touch per product is considered, the Linear and Positional models produce identical results in practice today, granting 100% credit if only one product touched the customer, or a 50/50 split if both did. Examples showing three or more touchpoints are for illustrative purposes to demonstrate how the model will behave as our product offerings evolve. Read more here.
Attributes 40% credit to the first and last touch, the remaining 20% credit is split evenly among the ones in between.
| Pros | Cons | Best used for |
|---|---|---|
| Recognizes the importance of the initial and final interactions while still attributing credit to all other influencing touchpoints. | Might overemphasize the first and last touch, which could result in undervaluing middle-funnel nurturing touchpoints | Recognizing both acquisition and conversion drivers. |
Note
With AdRoll attribution, only two products (Web and CTV Retargeting and Web and CTV Brand Awareness) currently contribute touchpoints. Because at most one touch per product is considered, the Positional and Linear models produce identical results in practice today, granting 100% credit if only one product touched the customer, or a 50/50 split if both did. Examples showing three or more touchpoints are for illustrative purposes to demonstrate how the model will behave as our product offerings evolve. Read more here.
Attributes the most credit to the final touchpoint, with decreasing credit for each touchpoint before.
| Pros | Cons | Best used for |
|---|---|---|
| Shows which touchpoints ultimately drives conversions. | Can undervalue early brand awareness efforts and top-of-funnel touchpoints. | Understanding what pushes customers to the final purchase. |
First, middle or last touchpoints are awarded as much credit as you wish.
| Pros | Cons | Best used for |
|---|---|---|
| Precise control over how you distribute credit for conversions based on the position of the platforms in the customer journey. | Developing and analyzing data with custom models is resource-intensive. | These can help to ensure that specific marketing efforts receive the credit they deserve and aren’t under or overvalued based on your unique business needs and funnel. |
Touchpoints are awarded credit within a timespan prior to the conversion.
| Pros | Cons | Best used for |
|---|---|---|
| Precise control over how you distribute credit for conversions based on the time of touchpoints. | Developing and analyzing data with custom models is resource-intensive | These can help to ensure that specific marketing efforts receive the credit they deserve and aren’t under or overvalued based on your unique business needs and funnel. |
Understanding Linear and Positional Attribution
The Linear model distributes conversion credit equally across all contributing touchpoints. The Positional model assigns 40% credit to the first touch, 40% to the last touch, and distributes the remaining 20% evenly across any touches in between.
Why do Linear and Positional produce the same results in AdRoll?
AdRoll attribution currently recognizes two products: Web and CTV Retargeting and Web and CTV Brand Awareness. When assigning credit, only the last touchpoint from each product is considered; earlier touches from the same product are not counted separately.
This means there are at most two relevant touchpoints for any given conversion:
- The last Retargeting touch (if any)
- The last Brand Awareness touch (if any)
With only one or two possible touchpoints, the Linear and Positional models always produce the same outcome:
| Active Product Touchpoints | Linear Model Credit Allocation | Positional Model Credit Allocation |
|---|---|---|
| Retargeting Only | 100% Retargeting | 100% Retargeting |
| Brand Awareness Only | 100% Brand Awareness | 100% Brand Awareness |
| Both | 50% Retargeting / 50% Brand Awareness | 50% Retargeting / 50% Brand Awareness |
The distinction between Linear and Positional only becomes meaningful when there are three or more touchpoints; for example, if a first touch, one or more middle touches, and a last touch all come from different products. With two products, that scenario cannot occur.
Why do the examples show three or more touchpoints?
The examples in the attribution settings are shown for illustrative purposes, to help you understand how each model would behave across a range of scenarios. As AdRoll's product offering evolves and additional touchpoints become available, these models will reflect that complexity accordingly — and the examples will remain accurate for those future cases.
Which one should I choose?
If you are using AdRoll attribution today, choosing between Linear and Positional will not affect your results. We recommend selecting the model that best reflects your attribution philosophy for the long term, so your settings remain meaningful as the product evolves.
- Choose Linear if you believe every distinct product interaction deserves equal credit.
- Choose Positional if you believe the first and last interactions are fundamentally more important than the ones in between.
Comparing Attribution Models
Note: If you have less than 30 days of data, the preview may show partial data.
The "Compare" section on the right side of the dashboard allows you to see how different attribution models impact your reported conversions and KPIs. You can compare your "Applied Model" against a "New Model" (e.g., "Custom Time-Based" vs. "AdRoll Default") to observe potential changes.
The comparison section visually distinguishes between different data views:
- No Model: This represents your data with no attribution model applied, reflecting an open 30-day lookback window for both click-through and view-through conversions.
- Applied Model: This shows data based on your current and/or previously applied attribution model(s) over the last 30 days.
- New Model: This displays data as it would appear with your newly selected model for comparison.
This comparison helps you understand the impact of switching to a new attribution model on metrics like:
- Attributed Conversions
- Attributed Revenue
- Avg. Order Value
- ROAS (Return on Ad Spend)
- CPA (Cost Per Acquisition)
- Spend
View-Through Credit Settings
View-through credit is assigned to products where ads were shown but not clicked before a conversion occurred. You can toggle "Allow credit for view-through conversions" on or off.
For each product (e.g., Web and CTV Retargeting, Web and CTV Brand Awareness), you can define:
- Credit: Typically set to "Auto" for automatic assignment.
- Lookback: The number of days within which a view of the ad will be considered for credit. For example, "Web and CTV Retargeting" has a default lookback of 1 day, while "Web and CTV Brand Awareness" has a 7-day lookback.
- Conversions within Lookback: This displays the number and percentage of conversions that occurred within the specified lookback window for that product.
Click-Through Credit Settings
Click-through credit is assigned to products where an ad was clicked before a conversion occurred. Similar to view-through credit, you can toggle "Allow credit for click-through conversions."
For each product (e.g., Web and CTV Retargeting, Web and CTV Brand Awareness):
- Credit: Typically set to "Auto."
- Lookback: The number of days within which a click on the ad will be considered for credit. For example, "Web and CTV Retargeting" has a 5-day lookback, and "Web and CTV Brand Awareness" has a 30-day lookback.
- Conversions within Lookback: This displays the number and percentage of conversions that occurred within the specified lookback window for that product.
Applying Your Attribution Model
Once you've configured your attribution settings, click the "Apply" button. A confirmation message will appear, stating:
"This new model will be applied to all future conversions on any AdRoll product. Conversions before this change will apply the old model."
This ensures that your historical data remains consistent while new conversions are attributed based on your updated model.