Attribution panel overview
The Attribution panel is available for all customers on the Adobe Analytics Ultimate, Prime, Select, and Foundation SKUs.
The attribution panel is an Attribution IQ feature that lets you add many new types of attribution models to freeform tables, visualizations, and calculated metrics. All attribution models have two components:
- Attribution model: The model describes the distribution of conversions to the hits in a group. For example, first touch or last touch.
- Attribution lookback window: The lookback window describes which groupings of hits are considered for each model. For example, visit or visitor.
When to use
Gives 100% credit to the touch point occurring most recently before conversion.
The most basic and common attribution model. It is frequently used for conversions with a short consideration cycle. Last Touch is commonly used by teams managing search marketing or analyzing internal search keywords.
Gives 100% credit to the touch point first seen in the attribution lookback window.
Another common attribution model useful for analyzing marketing channels intended to drive brand awareness or customer acquisition. It is frequently used by display or social marketing teams, but is also great for assessing onsite product recommendation effectiveness.
Gives 100% credit to the very hit where the conversion occurred. If a touch point does not happen on the same hit as a conversion, It is bucketed under "None".
A helpful model when evaluating the content or user experience that was presented immediately at the time of conversion. Product or design teams often use this model to assess the effectiveness of a page where conversion happens.
Gives equal credit to every touch point seen leading up to a conversion.
Useful for conversions with longer consideration cycles or user experiences that need more frequent customer engagement. It is often used by teams measuring mobile app notification effectiveness or with subscription-based products.
Gives 40% credit to the first interaction, 40% credit to the last interaction, and divides the remaining 20% to any touch points in between. For conversions with a single touch point, 100% credit is given. For conversions with two touch points, 50% credit is given to both.
A great model for those who value interactions that introduced or closed a conversion, but still want to recognize assisting interactions. U-Shaped attribution is commonly used by teams who take a more balanced approach but want to give more credit to channels that found or closed a conversion.
Gives 60% credit to the last interaction, 20% credit to the first interaction, and divides the remaining 20% to any touch points in between. For conversions with a single touch point, 100% credit is given. For conversions with two touch points, 75% credit is given to the last interaction, and 25% credit is given to the first.
This model is great for those who prioritize finders and closers, but want to focus on closing interactions. J-Shaped attribution is frequently used by teams who take a more balanced approach and want to give more credit to channels that closed a conversion.
Gives 60% credit to the first touch point, 20% credit to the last touch point, and divides the remaining 20% to any touch points in between. For conversions with a single touch point, 100% credit is given. For conversions with two touch points, 75% credit is given to the first interaction, and 25% credit is given to the last.
This model is ideal for those who prioritize finders and closers, but want to focus on finding interactions. Inverse J attribution is used by teams who take a more balanced approach and want to give more credit to channels that initiated a conversion.
Allows you to specify the weights you want to give to first touch points, last touch points, and any touch points in between. Values specified are normalized to 100% even if the custom numbers entered do not add to 100. For conversions with a single touch point, 100% credit is given. For interactions with two touch points, the middle parameter is ignored. The first and last touch points are then normalized to 100%, and credit is assigned accordingly.
This model is perfect for those who want full control over their attribution model and have specific needs that other attribution models do not fulfill.
Follows and exponential decay with a custom half-life parameter, where the default is 7 days. The weight of each channel depends on the amount of time that passed between the touch point initiation and the eventual conversion. The formula used to determine credit is 2^(-t/halflife) , where t is the amount of time between a touch point and a conversion. All touch points are then normalized to 100%.
Great for teams who regularly run video advertising or market against events with a predetermined date. The longer a conversion happens after a marketing event, the less credit is given.
Gives 100% credit to all unique touch points. The total number of conversions is inflated compared to other attribution models. Participation deduplicates channels that are seen multiple times.
Excellent for understanding who often customers are exposed to a given interaction. Media organizations frequently use this model to calculate content velocity. Retail organizations often use this model to understand which parts of their site are critical to conversion.
The following algorithmic attribution model is currently available in Adobe Analytics Labs and will eventually be part of a general release.
When to use
Uses statistical techniques to dynamically determine the optimal allocation of credit for the selected metric.
Useful to help avoid guesswork or heuristics when choosing the right attribution model for your business.
A lookback window is the amount of time a conversion should look back to include touch points. Attribution models that give more credit to first interactions see larger differences when viewing different lookback windows.
- Visit lookback window: Looks back up to the beginning of a the visit where a conversion happened. Visit lookback windows are narrow, as they don't look beyond the visit. Visit lookback windows respect the modified visit definition in virtual report suites.
- Visitor lookback window: Looks at all visits back up to the 1st of the month of the current date range. Visitor lookback windows are wide, as they can span many visits. For example, if the report date range is September 15 - September 30, the visitor lookback date range includes September 1 - September 30.
Consider the following example:
- On September 15, a visitor arrives to your site through a paid search advertisement, then leaves.
- On September 18, the visitor arrives to your site again through a social media link they got from a friend. They add several items to their cart, but do not purchase anything.
- On September 24, your marketing team sends them an email with a coupon for some of the items in their cart. They apply the coupon, but visit several other sites to see if any other coupons are available. They find another through a display ad, then ultimately make a purchase for $50.
Depending on your lookback window and attribution model, channels receive different credit. The following are some notable examples:
- Using first touch and a visit lookback window , attribution looks at only the third visit. Between email and display, email was first, so email gets 100% credit for the $50 purchase.
- Using first touch and a visitor lookback window , attribution looks at all three visits. Paid search was first, so it gets 100% credit for the $50 purchase.
- Using linear and a visit lookback window , credit is divided between email and display. Both of these channels each get $25 credit.
- Using linear and a visitor lookback window , credit is divided between paid search, social, email, and display. Each channel gets $12.50 credit for this purchase.
- Using J-shaped and a visitor lookback window , credit is divided between paid search, social, email, and display.
- 60% credit is given to display, for $30.
- 20% credit is given to paid search, for $10.
- The remaining 20% is divided between social and email, giving $5 to each.
- Using Time Decay and a visitor lookback window , credit is divided between paid search, social, email, and display. Using the default 7-day half-life:
- Gap of 0 days between display touch point and conversion. 2^(-0/7) = 1
- Gap of 0 days between email touch point and conversion. 2^(-0/7) = 1
- Gap of 6 days between social touch point and conversion. 2^(-6/7) = 0.552
- Gap of 9 days between paid search touch point and conversion. 2^(-9/7) = 0.41
- Normalizing these values results in the following:
- Display: 33.8%, getting $16.88
- Email: 33.8% getting $16.88
- Social: 18.6%, getting $9.32
- Paid Search: 13.8%, getting $6.92
Other conversion events, such as orders or custom events, are also divided if credit belongs to more than one channel. For example, if two channels contribute to a custom event using a Linear attribution model, both channels get 0.5 of the custom event. These event fractions are summed across all visits, then rounded to the nearest integer for reporting.
Using attribution with marketing channels
When marketing channels were first introduced, they came with only first and last touch dimensions. With these additional attribution models, explicit first/last touch dimensions are no longer needed. Adobe provides generic Marketing Channel dimensions so they can be used with your attribution model of choice. These generic Marketing Channels dimensions behave identically to Last Touch Channel dimensions, but are labeled differently to prevent confusion when using marketing channels with a different attribution model.
Since marketing channel dimensions depend on a traditional visit definition (as defined by their processing rules), their visit definition cannot be changed using virtual report suites.
Using attribution with multi-value variables
Some dimensions in Analytics can contain multiple values on a single hit. Common examples include list vars and the products variable.
When attribution is applied to multi-value hits, all values in the same hit get the same credit. Since many values can receive this credit, the report total can be different than if you summed each individual line item. The report total is deduplicated, while each individual dimension value gets proper credit.
Using attribution with segmentation
Attribution always runs before segmentation, and segmentation runs before report filters are applied. This concept also applies to virtual report suites using segments.
For example, if you create a VRS with a "Display Hits" segment applied, you could see other channels in a table using some attribution models.
If a segment suppresses hits containing your metric, those metric instances will not be attributed to any dimension. However, a similar report filter will simply hide some dimension values, without any impact on metrics processed per the attribution model. As a result, a segment and filter with comparable definitions may sometimes return lower values for the segment.