Input and output in Attribution AI

The following document outlines the different input and outputs utilized in Attribution AI.

Attribution AI input data

Attribution AI works by analyzing one of the following datasets to calculate algorithmic scores:

IMPORTANT

The Adobe Analytics source connector can take up to four weeks to backfill data. If you recently set up a connector, you should verify that the dataset has the minimum length of data required for Attribution AI. Please review the historical data section to verify you have enough data to calculate accurate algorithmic scores.

For more details on setting up the Consumer Experience Event (CEE) schema, please refer to the Intelligent Services data preparation guide. For more information on mapping Adobe Analytics data, visit the Analytics field mappings documentation.

Not all the columns in the Consumer Experience Event (CEE) schema are mandatory for Attribution AI.

NOTE

The following 9 columns are mandatory, additional columns are optional but recommended/necessary if you want to use the same data for other Adobe solutions such as Customer AI and Journey AI.

Mandatory columns Needed for
Primary Identity Field Touchpoint / Conversion
Timestamp Touchpoint / Conversion
Channel._type Touchpoint
Channel.mediaAction Touchpoint
Channel.mediaType Touchpoint
Marketing.trackingCode Touchpoint
Marketing.campaignname Touchpoint
Marketing.campaigngroup Touchpoint
Commerce Conversion

Typically, attribution is run on conversion columns such as order, purchases, and checkouts under “commerce”. The columns for “channel” and “marketing” are used to define touchpoints for Attribution AI (for example, channel._type = 'https://ns.adobe.com/xdm/channel-types/email'). For optimal results and insights, it is highly recommended that you include as many conversion and touchpoint columns as possible. Additionally, you are not limited to just the above columns. You can include any other recommended or custom columns as a conversion or touchpoint definition.

TIP

If you are using Adobe Analytics data in your CEE schema, the touchpoint information for Analytics is typically stored in channel.typeAtSource (for example, channel.typeAtSource = 'email').

The columns below are not required but it is recommended that you include them in your CEE schema if you have the information available.

Additional recommended columns:

  • web.webReferer
  • web.webInteraction
  • web.webPageDetails
  • xdm:productListItems

Historical data

IMPORTANT

The minimum amount of data that is needed for Attribution AI to function is as follows:

  • You need to provide at least 3 months (90 days) of data to run a good model.
  • You need at least 1000 conversions.

Attribution AI requires historical data as input for model training. The data duration required is mainly determined by two key factors: training window and look-back window. Input with shorter training windows are more sensitive to recent trends, while longer training windows help produce more stable and accurate models. It’s important to model the objective with historical data that best represents your business goals.

The training window configuration filters conversion events set to be included for model training based on occurrence time. Currently, the minimum training window is 1 quarter (90 days). The lookback window provides a time frame indicating how many days prior to the conversion event touchpoints related to this conversion event should be included. These two concepts together determine the amount of input data (measured by days) that is required for an application.

By default, Attribution AI defines the training window as the most recent 2 quarters (6 months) and lookback window as 56 days. In other words, the model will take into consideration all of the defined conversion event(s) that have occurred in the past 2 quarters and look for all the touchpoints that have occurred within 56 days prior to the associated conversion event(s).

Formula:

Minimum length of data required = training window + lookback window

TIP

The minimum length of data required for an application with default configurations is: 2 quarters (180 days) + 56 days = 236 days.

Example :

  • You want to attribute conversion events that have happened within the last 90 days (3 months) and track all the touchpoints that have happened within 4 weeks prior the conversion event. The input data duration should span over the past 90 days + 28 days (4 weeks). The training window is 90 days and the lookback window is 28 days totaling 118 days.

Attribution AI output data

Attribution AI outputs the following:

Example output schema:

Raw granular scores

Attribution AI outputs attribution scores in the most granular level possible so that you can slice and dice the scores by any score column. To view these scores in the UI, read the section on viewing raw score paths. To download the scores using the API visit the downloading scores in Attribution AI document.

NOTE

You are able to see any desired reporting column from the input dataset in the score output dataset only if either of the following are true:

  • The reporting column is included in the configuration page either as part of touchpoint or conversion definition configuration.
  • The reporting column is included in additional score dataset columns.

The following table outlines the schema fields in the raw scores example output:

Column Name (DataType) Nullable Description
timestamp (DateTime) False The time when an conversion event or observation occurred.
Example: 2020-06-09T00:01:51.000Z
identityMap (Map) True identityMap of the user similar to the CEE XDM format.
eventType (String) True The primary event type for this time-series record.
Example: “Order”, “Purchase”, “Visit”
eventMergeId (String) True An ID to correlate or merge multiple Experience Events together that are essentially the same event or should be merged. This is intended to be populated by the data producer prior to ingestion.
Example: 575525617716-0-edc2ed37-1aab-4750-a820-1c2b3844b8c4
_id (String) False A unique identifier for the time-series event.
Example: 4461-edc2ed37-1aab-4750-a820-1c2b3844b8c4
_tenantId (Object) False The top level object container corrisponding to your tentant ID.
Example: _atsdsnrmmsv2
your_schema_name (Object) False Score row with conversion event all the touchpoint events associated with it and their metadatas.
Example: Attribution AI Scores - Model Name__2020
segmentation (String) True Conversion segment such as geo segmentation which the model is built against. In case of the absence of segments, segment is same as conversionName.
Example: ORDER_US
conversionName (String) True Name of the conversion that was configured during setup.
Example: Order, Lead, Visit
conversion (Object) False Conversion metadata columns.
dataSource (String) True Globally unique identification of a data source.
Example: Adobe Analytics
eventSource (String) True The source when the actual event happened.
Example: Adobe.com
eventType (String) True The primary event type for this time-series record.
Example: Order
geo (String) True The geographic location where the conversion was delivered placeContext.geo.countryCode.
Example: US
priceTotal (Double) True Revenue obtained through the conversion
Example: 99.9
product (String) True The XDM identifier of the product itself.
Example: RX 1080 ti
productType (String) True The display name for the product as presented to the user for this product view.
Example: Gpus
quantity (Integer) True Quantity purchased during the conversion.
Example: 1 1080 ti
receivedTimestamp (DateTime) True Received timestamp of the conversion.
Example: 2020-06-09T00:01:51.000Z
skuId (String) True Stock keeping unit (SKU), the unique identifier for a product defined by the vendor.
Example: MJ-03-XS-Black
timestamp (DateTime) True Timestamp of the conversion.
Example: 2020-06-09T00:01:51.000Z
passThrough (Object) True Additional Score dataset Columns specified by user while configuring the model.
commerce_order_purchaseCity (String) True Additional Score dataset Column.
Example: city : San Jose
customerProfile (Object) False Identity details of the user used to build the model.
identity (Object) False Contains the details of the user used to build the model such as id and namespace.
id (String) True Identity ID of the user such as cookie ID or AAID or MCID etc.
Example: 17348762725408656344688320891369597404
namespace (String) True Identity namespace used to build the paths and thereby the model.
Example: aaid
touchpointsDetail (Object Array) True The list of touchpoint details leading to the conversion ordered by
touchpointName (String) True Name of the touchpoint that was configured during setup.
Example: PAID_SEARCH_CLICK
scores (Object) True Touchpoint contribution to this conversion as score. For more information on the scores produced within this object, see the aggregated attribution scores section.
touchPoint (Object) True Touchpoint Metadata. For more information on the scores produced within this object, see the aggregated scores section.

Viewing raw score paths (UI)

You can view the path to your raw scores in the UI. Start by selecting Schemas in the Platform UI then search for and select your attribution AI scores schema from within the Browse tab.

Pick your schema

Next, select a field within the Structure window of the UI, the Field properties tab opens. Within Field properties is the path field that maps to your raw scores.

Pick a Schema

Aggregated attribution scores

Aggregated scores can be downloaded in CSV format from the Platform UI if the date range is less than 30 days.

Attribution AI supports two categories of attribution scores, algorithmic and rule-based scores.

Attribution AI produces two different types of algorithmic scores, incremental and influenced. An influenced score is the fraction of the conversion that each marketing touchpoint is responsible for. An incremental score is the amount of marginal impact directly caused by the marketing touchpoint. The main difference between the incremental score and the influenced score is that the incremental score takes the baseline effect into account. It does not assume that a conversion is caused purely by the preceding marketing touchpoints.

Here is a quick look at an Attribution AI schema output example from the Adobe Experience Platform UI:

See the table below for more details about each of these attribution scores:

Attribution scores Description
Influenced (algorithmic) Influenced score is the fraction of the conversion that each marketing touchpoint is responsible for.
Incremental (algorithmic) Incremental score is the amount of marginal impact directly caused by a marketing touchpoint.
First Touch Rule-based attribution score that assigns all credits to the initial touchpoint on a conversion path.
Last Touch Rule-based attribution score that assigns all credit to the touchpoint closest to the conversion.
Linear Rule-based attribution score that assigns equal credit to each touchpoint on a conversion path.
U-Shaped Rule-based attribution score that assigns 40% of the credit to the first touchpoint and 40% of the credit to the last touchpoint, with the other touchpoints splitting the remaining 20% equally.
Time Decay Rule-based attribution score where touchpoints closer to the conversion receive more credit than touchpoints that are farther away in time from the conversion.

Raw Score reference (attribution scores)

The table below maps the attribution scores to the raw scores. If you wish to download your raw scores, visit the downloading scores in Attribution AI documentation.

Attribution scores Raw score reference column
Influenced (algorithmic) _tenantID.your_schema_name.element.touchpoint.algorithmicInfluenced
Incremental (algorithmic) _tenantID.your_schema_name.touchpointsDetail.element.touchpoint.algorithmicInfluenced
First Touch _tenantID.your_schema_name.touchpointsDetail.element.touchpoint.firstTouch
Last Touch _tenantID.your_schema_name.touchpointsDetail.element.touchpoint.lastTouch
Linear _tenantID.your_schema_name.touchpointsDetail.element.touchpoint.linear
U-Shaped _tenantID.your_schema_name.touchpointsDetail.element.touchpoint.uShape
Time Decay _tenantID.your_schema_name.touchpointsDetail.element.touchpoint.decayUnits

Aggregated Scores

Aggregated scores can be downloaded in CSV format from the Platform UI if the date range is less than 30 days. See the table below for more details about each of these aggregate columns.

Column Name Constraint Nullable Description
customerevents_date (DateTime) User defined & fixed format False Customer Event Date in YYYY-MM-DD format.
Example: 2016-05-02
mediatouchpoints_date (DateTime) User defined & fixed format True Media Touchpoint Date in YYYY-MM-DD format
Example: 2017-04-21
segment (String) Calculated False Conversion Segment such as geo segmentation which the model is built against. In case of absence of segments, segment is same as conversion_scope.
Example: ORDER_AMER
conversion_scope (String) User defined False Name of the Conversion as configured by the user.
Example: ORDER
touchpoint_scope (String) User defined True Name of the Touchpoint as configured by the user
Example: PAID_SEARCH_CLICK
product (String) User defined True The XDM identifier of the product.
Example: CC
product_type (String) User defined True The display name for the product as presented to the user for this product view.
Example: gpus, laptops
geo (String) User defined True The geographic location where the conversion was delivered (placeContext.geo.countryCode)
Example: US
event_type (String) User defined True The primary event type for this time-series record
Example: Paid Conversion
media_type (String) ENUM False Describes whether the media type is paid,owned or earned.
Example: PAID, OWNED
channel (String) ENUM False The channel._type property that is used to provide a rough classification of channels with similar properties in Consumer Experience Event XDM.
Example: SEARCH
action (String) ENUM False The mediaAction property is used to provide a type of experience event media action.
Example: CLICK
campaign_group (String) User defined True Name of the campaign group where multiple campaigns are grouped together like ‘50%_DISCOUNT’.
Example: COMMERCIAL
campaign_name (String) User defined True Name of the campaign used to identify marketing campaign like ‘50%_DISCOUNT_USA’ or ‘50%_DISCOUNT_ASIA’.
Example: Thanksgiving Sale

Raw Score reference (aggregated)

The table below maps the aggregated scores to the raw scores. If you wish to download your raw scores, visit the downloading scores in Attribution AI documentation. To view the raw score paths from within the UI, visit the section on viewing raw score paths within this document.

Column Name Raw Score reference column
customerevents_date timestamp
mediatouchpoints_date _tenantID.your_schema_name.touchpointsDetail.element.touchpoint.timestamp
segment _tenantID.your_schema_name.segmentation
conversion_scope _tenantID.your_schema_name.conversion.conversionName
touchpoint_scope _tenantID.your_schema_name.touchpointsDetail.element.touchpointName
product _tenantID.your_schema_name.conversion.product
product_type _tenantID.your_schema_name.conversion.product_type
geo _tenantID.your_schema_name.conversion.geo
event_type eventType
media_type _tenantID.your_schema_name.touchpointsDetail.element.touchpoint.mediaType
channel _tenantID.your_schema_name.touchpointsDetail.element.touchpoint.mediaChannel
action _tenantID.your_schema_name.touchpointsDetail.element.touchpoint.mediaAction
campaign_group _tenantID.your_schema_name.touchpointsDetail.element.touchpoint.campaignGroup
campaign_name _tenantID.your_schema_name.touchpointsDetail.element.touchpoint.campaignName

Next steps

Once you have prepared your data and have all your credentials and schemas in place, start by following the Attribution AI user guide. This guide walks you through creating an instance for Attribution AI.

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