Attribution panel

The Attribution panel is an easy way to build an analysis comparing various attribution models. The panel provides you with a dedicated workspace to use and compare attribution models.

Customer Journey Analytics enhances attribution by letting you:

• Define attribution beyond paid media: Any dimension, metric, channel or event can be applied to models (for example, internal search), not just marketing campaigns.
• Use unlimited attribution model comparison: dynamically compare as many models as you want.
• Avoid implementation changes: With report-time processing and context-aware sessions, customer journey context can be built in and applied at run time.
• Construct the session that best matches your attribution scenario.
• Break down attribution by filters: Easily compare the performance of your marketing channels across any important filter (for example, New vs. Repeat customers, Product X vs. Product Y, Loyalty level or CLV).
• Inspect channel cross-over and multi-touch analysis: Use Venn Diagrams and Histograms, and trend attribution results.
• Analyze key marketing sequences visually: explore paths that led to conversion visually with multi-node flow and fallout visualizations.
• Build calculated metrics: use any number of attribution allocation methods.

Use

To use an Attribution panel:

1. Create an Attribution panel. For information about how to create a panel, see Create a panel.

2. Specify the input for the panel.

3. Observe the output for the panel.

Panel input

You can configure the Attribution panel using these input settings:

1. Add a Success metric and a dimension from the Channel that you want to attribute against. Examples include Marketing Channels or custom dimensions, such as internal promotions.

   

2. Select one or more attribution models from Included models and a lookback window from the Lookback window that you want to use for comparison.

3. Select Build to build the visualizations in the panel.

Panel output

The Attribution panel returns a rich set of data and visualizations that compare attribution for the selected dimension and metric.

Attribution visualizations

The following visualization are part of the panel ouput.

• Total metric: The total number of conversions that occurred over the reporting time window, and are attributed to the dimension you selected.
• Attribution Comparison Bar: Visually compares the attributed conversions across each of the dimension items from your selected dimension. Each bar color represents a distinct attribution model.
• Attribution Comparison Table: Shows the same data as the bar chart, represented as a table. Selecting different columns or rows in this table filters the bar chart as well as several of the other visualizations in the panel. This table acts similar to any other Freeform table in Workspace - allowing you to add components such as metrics, filters, or breakdowns.
• Overlap Diagram: A Venn visualization showing the top three dimension items and how often they participate jointly in a conversion. For example, the size of the bubble overlap indicates how often conversions occurred when a person was exposed to both dimension items. Selecting other rows in the adjacent Freeform table updates the visualization to reflect your selection.
• Performance Detail: A scatter visualization to compare up to three attribution models visually.
• Trended Performance: Shows the trend of attributed conversions for the top dimension item. Selecting other rows in the adjacent Freeform table updates the visualization to reflect your selection.
• Flow: Lets you see which channels are interacted with most commonly, and in what order across a person’s journey.

Attribution models

An attribution model determines which dimension items get credit for a metric when multiple values are seen within a metric’s lookback window. Attribution models only apply when there are multiple dimension items set within the lookback window. If only a single dimension item is set, that dimension item gets 100% credit regardless of attribution model used.

Icon	Attribution model	Definition
	Last Touch	Gives 100% credit to the touch point occurring most recently before conversion. This attribution model is typically the default value for any metric where an attribution model is not otherwise specified. Organizations typically use this model where the time to conversion is relatively short, such as with analyzing internal search keywords.
	First Touch	Gives 100% credit to the touch point first seen within the attribution lookback window. Organizations typically use this model to understand brand awareness or customer acquisition.
	Linear	Gives equal credit to every touch point seen leading up to a conversion. It is useful where conversion cycles are longer or require more frequent customer engagement. Organizations typically use this attribution model measuring mobile app notification effectiveness or with subscription-based products.
	Participation	Gives 100% credit to all unique touch points. Since every touch point receives 100% credit, metric data typically adds up to more than 100%. If a dimension item appears multiple separate times leading up to a conversion, values are deduplicated to 100%. This attribution model is ideal in situations where you want to understand which touch points customers are exposed to the most. Media organizations typically use this model to calculate content velocity. Retail organizations typically use this model to understand which parts of their site are critical to conversion.
	Same Touch	Gives 100% credit to the same event where the conversion occurred. If a touch point does not happen on the same event as a conversion, It is bucketed under “None”. This attribution model is sometimes equated to having no attribution model at all. It is valuable in scenarios where you do not want values from other events affecting how a metric gives credit to dimension items. Product or design teams can use this model to assess the effectiveness of a page where conversion happens.
	U Shaped	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. This attribution model is best used in scenarios where you value the first and last interactions the most, but don’t want to entirely dismiss additional interactions in between.
	J Curve	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. Similar to U-Shaped, this attribution model favors the first and last interactions, but more heavily favors the last interaction.
	Inverse J	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. Similar to J-Shaped, this attribution model favors the first and last interactions, but more heavily favors the first interaction.
	Time Decay	Follows an 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%. Ideal for scenarios where you want to measure attribution against a specific and significant event. The longer a conversion happens after this event, the less credit is given.
	Custom	Allows you to specify the weights that you want to give to first touch point, last touch point, 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 ideal for analysts who want full control over their attribution model and have specific needs that other attribution models do not fulfill.
	Algorithmic	Uses statistical techniques to dynamically determine the optimal allocation of credit for the selected metric. The algorithm used for attribution is based on the Harsanyi Dividend from cooperative game theory. The Harsanyi dividend is a generalization of the Shapley value solution (named after Lloyd Shapley, a Nobel Laureate economist) to distributing credit among players in a game with unequal contributions to the outcome. At a high level, attribution is calculated as a coalition of players to which a surplus must be equitably distributed. Each coalition’s surplus distribution is determined according to the surplus that was previously created by each subcoalition (or previously participating dimension items) recursively. For more details, see John Harsanyi’s and Lloyd Shapley’s original papers: Shapley, Lloyd S. (1953). A value for n-person games. Contributions to the Theory of Games, 2(28), 307-317. Harsanyi, John C. (1963). A simplified bargaining model for the n-person cooperative game. International Economic Review 4(2), 194-220.

Lookback window

A lookback window is the amount of time a conversion should look back to include touch points. If a dimension item is set outside of the lookback window, the value is not included in any attribution calculations.

• 14 Days: Looks back up to 14 days from when the conversion happened.
• 30 Days: Looks back up to 30 days from when the conversion happened.
• 60 Days: Looks back up to 60 days from when the conversion happened.
• 90 Days: Looks back up to 90 days from when the conversion happened.
• Session: Looks back up to the beginning of the session where a conversion happened. Session lookback windows respect the modified Session timeout in a data view.
• Person (Reporting Window): Looks at all visits back up to the first of the month of the current date range. For example, if the report date range is September 15 - September 30, the person lookback date range includes September 1 - September 30. If you use this lookback window, you can occasionally see that dimension items are attributed to dates outside of your reporting window.
• Custom Time: Allows you to set a custom lookback window from when a conversion happened. You can specify the number of minutes, hours, days, weeks, months, or quarters. For example, if a conversion happened on February 20, a lookback window of five days would evaluate all dimension touchpoints from February 15 to February 20 in the attribution model.