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Adspace Attribution Scenarios

Adspace Attribution Scenarios

What is it about?

The Adspace Attribution Scenarios Data View provides detailed insights into how individual adspaces contribute to conversion attribution across different models (e.g. first click, first touchpoint, assist, etc.). It enables advertisers and network managers to evaluate the true value of adspaces within multi-touch journeys.

Also it allows to judge, what the attribution results would have looked like, if the attribution was different, like first click instead of the defined model that is in most cases based on last click.

Dimensions

Field

Description

Field

Description

Advertiser

The advertiser

Partner

The partner (publisher) owning the adspace

Adspace

The specific adspace involved in the conversion funnel

Target

The type of conversion target (e.g. sale, lead)

Performed At

Date of the conversion

Filtering Options

The following filters are commonly used in this Data View:

  • Date Range (e.g. Last 30 days)

  • Conversion Target

  • Advertiser ID

  • Partner ID

 

Best Practice: Use Filters

To get the best results, we recommend to use these Filters:

  • Advertiser

  • Conversion Target

 

Metrics - Explanation

The metrics in this Data View are grouped into three logical categories, each serving a different purpose in understanding the role of an adspace in the user journey.

1. Simulation: First Click Attribution (Post-click only)

  • Winner by First Click
    This metric shows how many conversions the adspace would have won if the attribution model had been "first click" – meaning attribution goes to the first click that occurred within 30 days before the conversion.
    Only actual clicks are considered, and views are ignored.

    Use case: This helps evaluate how well the adspace performs when it initiates the click journey that ultimately leads to a conversion.

 

2. Simulation: First Touchpoint Attribution

This group of metrics simulates what would happen if attribution were based on the first touchpoint – which can be either a click or a view. It includes more granular breakdowns to compare the role of views and clicks at the beginning of the funnel.

  • Winner by First TP
    Number of conversions the adspace would have won under a first-touchpoint attribution model, considering both clicks and views as potential starting points.
    It identifies the first touchpoint within 30 days prior to conversion, regardless of type.

    Compared to Winner by First Click, this includes views and therefore captures all top-of-funnel activity.

  • Winner if View as First TP
    Simulates how many conversions the adspace would have received only if the first touchpoint was a view. Clicks are excluded.
    It shows how effective impressions are in initiating journeys that convert later.

  • Winner if Click as First TP
    Similar to the above, but limited to clicks as the first touchpoint. Views are excluded.
    This differs from Winner by First Click in that it focuses on first interaction being a click, not just the first click overall

The math looks like this: Winner by First TP = Winner if View as First TP + Winner if Click as First TP

These simulations are useful for understanding whether the adspace primarily adds value through impressions or clicks at the start of the user journey.

 

3. Actual Attribution Outcomes

These metrics reflect what actually happened under the currently configured attribution model in your platform (e.g. last click, linear, position-based).

  • Winner
    Total number of conversions that were actually attributed to the adspace under the live attribution model.

  • Assisted
    Number of conversions where the adspace was part of the user journey (i.e. had a touchpoint), but did not receive final credit for the conversion.

  • Participated
    Sum of all conversions where the adspace was involved – either as the winner or an assisting touchpoint.

Use case: This set of metrics provides a holistic view of actual attribution, revealing not just final impact but also support roles within the funnel.

 

Use Cases

  • Identify top-performing adspaces based on different attribution positions

  • Detect undervalued adspaces that assist but rarely win attributions (e.g. top of the funnel activities)

  • Support commission strategy optimization

  • do adspace-level performance reviews for publishers

 

Example Interpretation

Example Interpretation

  • If adspace A has 18 participations, 10 assists, and 8 wins – it plays a balanced role between assisting and converting.

  • If adspace B has 20 participations, but 0 wins and 20 assists – it might be supporting the journey but is never attributed as the winner under current rules.

  • If adspace C has 10 “Winner by First TP” but only 2 actual “Winner” - it means that it starts many User Journeys but rarely is the last touchpoint, a typical top of the funnel partner.

 

Attribution Logic Notes

  • Attribution logic is based on the touchpoint attribution rules configured in your platform.

  • Different priorities and attribution windows may influence the Winner metric.

  • View-based touchpoints are always included in the Simulation.

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