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Attribution

Marketing teams struggle to build a centralized view of paid campaign performance because reporting is siloed in individual ad platforms. To build that cross-platform attribution, data teams are often required to manage complex, high-maintenance models in their data warehouse.

RudderStack’s Attribution Data App simplifies the generation of attribution data sets for first and last touch paid campaigns. As an extension of RudderStack Profiles, this feature provides you with an intuitive configuration for the attribution model, then generates and runs the complex SQL for you in your own warehouse.

Key features

• Unified campaign performance data: Consolidates campaign performance data from digital campaigns across ad platforms and ties it to user activity from your apps and websites.
• Cross-platform tracking: Uses the Profiles ID stitcher, which reconciles individual entities from events across devices, sessions, and browsers.
• Complex entity support for accounts and households: Reports on advertising ROI for complex business entities like accounts and households that often have multiple individual users participate in the customer journey.
• Multiple conversion goals: Supports tracking multiple conversion types and multiple conversions for each campaign (purchases, subscriptions, signups, etc.)
• Paid performance metrics: Calculates key metrics like Customer Acquisition Cost (CAC) and Return on Ad Spend (RoAS).
• Supports flexible, granular reporting: Provides daily reports that can be aggregated to different time granularities for any dashboard use case.

Prerequisites

• An active RudderStack Profiles project.
• Tables with the following data:
  • Event Stream Data: User activity touchpoints (usually the page and track call tables) to identify the first and last interaction points.
  • Conversion Data: Behavioral conversions you want to track, like track or identify tables for user signup or purchase events, or timestamped data points from other platforms, like an Opportunity Created Date from Salesforce.
    • Revenue data (optional): If you want to measure RoAS, your conversion data points must include a value like revenue, total cost, etc. This data does not necessarily have to be ingested via RudderStack but must have an associated timestamp.
  • Ad Campaign Data: ETL dataset (for example, cost, impressions, and engagement metrics) from platforms like Facebook, Google Ads, LinkedIn, etc.

Project setup

This section guides you on setting up an attribution model within an existing Profiles project.

Note that you will first need to add several components to your core Profiles project if they don’t exist yet. You will then use those components as a part of the attribution model configuration.

Add required components to your Profiles project

This section guide assumes you have already created a business entity (like user) and an ID stitcher model for that entity as part of an existing Profiles project.

Step 1: Define a campaign entity

In addition to your business entity, define a campaign entity in profiles.yaml. This entity will consolidate campaign data from multiple campaign performance tables.

Step 2: Define inputs

Make sure you have all of the relevant tables from the Prerequisites section as input data sources in your Profiles project in the inputs.yaml file.

There are three types of inputs:

• Paid performance data from ad platforms: This is the standard campaign performance data you would load from platforms like Google or Facebook using an ETL tool like Fivetran or Airbyte.

• Event data for entity touchpoints: To compute an entity’s first and last touch, the attribution model reads from event tables to construct a chronological list of touchpoints. Hence, you must add event tables that represent your customer journey (if these are not already defined as inputs in your existing project).

• Event data for conversions: The attribution model reports on conversions and uses the conversion’s timestamp to compute the end of the user journey. You must define these conversions as entity_vars in the profiles.yaml. Common examples of conversions are first_order_date or user_signup_date.

Step 3: Create a campaign ID Stitcher model

Add an ID Stitching model for your campaign entity in the profiles.yaml file and configure it with following specifications:

• Link various campaign identifiers like utm_campaign, campaign_id, etc.
• Include user-journey tables (for example, pages, tracks) in this stitcher.
• Ensure that the campaign identifier columns like utm_campaign contribute to the ID graph.

Configure attribution.yaml

RudderStack recommends creating a new file attribution.yaml to keep your Profiles project organized. You can copy the sample model config code to get started.

Step 1: Configure touchpoints

Add your inputs as touchpoints in the attribution.yaml file.

Note that each table must satisfy the following conditions to be used as a part of the user journey that the attribution model creates:

• It must be a part of both entities’ ID graph (user and campaign ID graph in this case).
• It must have a timestamp column denoting the time when the user saw/clicked the campaign. This is defined as the occured_at_col key.

A sample touchpoints configuration is shown:

touchpoints: 
    - from: inputs/pages
    - from: inputs/mobile_pages
      where: timestamp >= {{user.Var('signup_date')}} # optional

The where clause is optional and helps in filtering the parts of user journey that should not be considered for some conversions. For example, if you want to measure the efficiency of retargeting campaigns, you might not want to include the early parts of user journey. The where key takes a valid SQL where clause string, similar to the where clause in entity_vars. All the columns should be present in the same input table or can refer to the entity_vars of the model entity.

Step 2: Define conversions

Add the entity_vars that represent your conversions. Note that you can report on multiple conversions for each attribution model.

Optionally, you can define a value for each conversion, which the attribution model will use to compute total value generated and RoAS for each campaign.

A sample conversion_vars configuration is shown:

conversion_vars:    
  - name: payer_conversion
    timestamp: user.Var("first_paid_date")
    value: user.Var("first_order_amount") # Optional - adding this creates an extra column called <conversion>_<model>_value (ex: signup_first_touch_value)
    conversion_window: 30d # Optional - takes values in minutes, hours, and days, 30m, 4h, 7d, etc. If provided, RudderStack considers the conversion to have happened if it is within this range only.
  - name: mql
    timestamp: user.Var("user_mql_conversion_dt")

Step 3: Configure campaigns

To configure your campaigns, add the start date, end date, and any columns generated by the campaign ID stitcher that you want to include in the attribution model output as campaign entity_vars.

The following snippet highlights a sample campaign configuration:

campaign:
   entity_key: campaign
   campaign_start_date: campaign_start_date
   campaign_end_date: campaign_end_date
   campaign_vars: #These represent columns that will be repeated and are pulled from the campaign_var table
       - campaign_name
       - url
       - utm_source
       - utm_medium
       - utm_channel        

Step 4: Define campaign performance data

Lastly, define the campaign performance data points for cost, impressions, and clicks. RudderStack has standardized these for the campaign performance data loaded through Fivetran and Airbyte. Reach out to our team to request the templates.

A sample cost configuration is shown:

campaign_details: #These represent columns that will be computed daily
    - cost:
        - from: inputs/ga_campaign_stats
          date: date 
          select: sum(cost_micros / 1000000 )
        - from: inputs/lkdn_ad_analytic_campaign
          date: day 
          select: sum(cost_in_usd)
        - from: inputs/fb_basic_campaign
          date: date 
          select: sum(spend)

RudderStack will not be able to compute the RoAS and CAC fields if you do not provide the cost configuration.

Sample yaml for the attribution model

After completing the above steps, your attribution.yaml file will look similar to the following example:

  - name: campaign_performance_report
    model_type: attribution
    model_spec:
       entity_key: user
        conversion: 
            #entity_key: user
            touchpoints: 
                - from: inputs/pages
                - from: inputs/mobile_pages
            conversion_vars:    
                - name: payer_conversion
                  timestamp: user.Var("first_paid_date")
                  value: user.Var("first_order_amount") # Optional - adding this creates an extra column called <conversion>_<model>_value (ex: signup_first_touch_value)
                  conversion_window: 30d
                - name: mql
                  timestamp: user.Var("user_mql_conversion_dt")
                  # default lookback_value = 90 days
         campaign:
            entity_key: campaign
            campaign_start_date: campaign_start_date
            campaign_end_date: campaign_end_date
            campaign_vars: #These represent columns that will be repeated and are pulled from the campaign_var table
                - campaign_name
                - url
                - utm_source
                - utm_medium
                - utm_channel
             campaign_details: #These represent columns that will be computed daily
                  - cost:
                     - from: inputs/ga_campaign_stats
                       date: date 
                       select: sum(cost_micros / 1000000 )
                     - from: inputs/lkdn_ad_analytic_campaign
                       date: day 
                       select: sum(cost_in_usd)
                     - from: inputs/fb_basic_campaign
                       date: date 
                       select: sum(spend)
                  - impressions:
                     - from: inputs/ga_campaign_stats
                       date: date 
                       select: sum(impressions)
                     - from: inputs/lkdn_ad_analytic_campaign
                       date: day 
                       select: sum(total_impressions)
                     - from: inputs/fb_basic_campaign
                       date: date 
                       select: sum(imp)
                  - clicks:
                     - from: inputs/ga_campaign_stats
                       date: date 
                       select: sum(clicks)
                     - from: inputs/lkdn_ad_analytic_campaign
                       date: day 
                       select: sum(total_clicks)
                     - from: inputs/fb_basic_campaign
                       date: date 
                       select: sum(clicks)                  

Note that:

• The touchpoints section defines the user journey data sources.
• The conversion_vars specify the conversion types and their associated data.
• The campaign section outlines campaign-specific variables and daily performance metrics.
• Ensure that all the referenced inputs and variables are properly set up in your Profiles configuration.

Step 5: Run your project

After configuring your project, you can run it using one of the following methods:

Using Profile CLI

If you have created your Profiles project locally, run it using the pb run CLI command to generate output tables.

Using Profiles UI

Run your Profiles project by first uploading it to a Git repository and then importing it in the RudderStack dashboard.

Output

Once your project run is complete, Profiles generates an output table in your warehouse in the following format:

If you define multiple conversions, the [CONVERSION_TYPE]_ columns are repeated for each conversion, for example, signup_first_touch_count, subscribed_first_touch_count, etc.

Questions? Contact us by email or on Slack