Column settings
Column settings let you configure column formatting, some of which can be conditional.
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To access Column settings, select
You can edit settings for multiple columns at once. Select multiple columns and select
Conditional formatting conditional-formatting
Conditional formatting applies formatting to upper, midpoint, and lower limits that you can define. Applying conditional formatting within Freeform tables is also automatically enabled on breakdowns, unless Custom limits are selected.
Replacing a dimension in the table resets the conditional formatting limits. Replacing a metric recalculates the limits for that column (where a metric is on the X axis and a dimension is on the Y axis).
Use non-default attribution model use-non-default-attribution-model
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When using the component in a report with a single dimension: The component’s attribution ignores the allocation model when a non-default attribution model is used.
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When using the component in a report with multiple dimensions: The component’s attribution retains the allocation model when a non-default attribution model is used.
To use a non-default attribution model for a metric in Analysis Workspace:
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Select Use non-default attribution model. When already selected, use Edit to edit the attribution model. Or unselect to return to the default attribution model.
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In Column attribution model, select a Model and a Lookback window. The lookback window determines the window of data attribution that is applied for each conversion.
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.
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.
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.
Container
An attribution container defines the desired scope for the attribution. Possible options are:
- Visit: Looks at conversions from the scope of the visit container.
- Visitor: Looks at conversions from the scope of the visitor container.
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.
- 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.
Example
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 attribution model, container and channels receive different credit. See table below for examples:
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.
- Gap of zero days between display touch point and conversion.
2^(-0/7) = 1 - Gap of zero days between email touch point and conversion.
2^(-0/7) = 1 - Gap of six days between social touch point and conversion.
2^(-6/7) = 0.552 - Gap of nine days between paid search touch point and conversion.
2^(-9/7) = 0.41Normalizing 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
Conversion events that typically have whole numbers are divided if credit belongs to more than one channel. For example, if two channels contribute to an order using a Linear attribution model, both channels get 0.5 of that order. These partial metrics are summed across all people then rounded to the nearest integer for reporting.
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