Version:

Feature Development

Use unified profiles and enrich them with the required features/traits to drive targeted campaigns.

  •   2 minute read  

Once you have performed identity stitching to map the individual entities to known identifiers, you can enhance the unified profiles with additional data points and features.

To develop user features, you can use a feature table to define the features/traits in your warehouse tables. You can perform calculations over this data to devise meaningful outcomes, which can help marketing teams to run effective campaigns.

How to define features?

You can use RudderStack’s Feature Table model to write feature definitions. The Profiles project generates and runs SQL in the background and automatically adds the resulting features to a feature table.

You can produce a customer feature table or a feature table for specific projects like personalization, recommendations, or analytics.

success
You can combine the features to create even more features. You can also use custom SQL queries to enrich unified user profiles for advanced use cases.

A sample configuration file to create a feature table:

var_groups:
  - name: user_vars
    entity_key: user
    vars:
      - entity_var:
          name: first_seen
          select: min(timestamp::date)
          from: inputs/rsTracks
          is_feature: false
      - entity_var:
          name: last_seen
          select: max(timestamp::date)
          from: inputs/rsTracks
          is_feature: false
      - entity_var:
          name: user_lifespan
          select: '{{user.Var("last_seen")}} - {{user.Var("first_seen")}}'
          description: Life Time Value of a customer
      - entity_var:
          name: days_active
          select: count(distinct timestamp::date)
          from: inputs/rsTracks
          description: No. of days a customer was active
# ID stitcher
models:
  - name: domain_profile_id_stitcher
    model_type: id_stitcher
    model_spec:
      validity_time: 24h # 1 day
      entity_key: user
      materialization:
        run_type: incremental
      edge_sources:
        - from: inputs/rsIdentifies
        - from: inputs/rsTracks
# Feature table
  - name: domain_profile
    model_type: feature_table_model
    model_spec:
      validity_time: 24h # 1 day
      entity_key: user
    features:
        - user_lifespan
        - days_active

Benefits

  • You can use the output of the identity graph to define or compute features across multiple feature tables.
  • As the number of features/traits increases, Profiles makes the maintenance process much easier by using a configuration file (as opposed to large and complex SQL queries).
  • Profiles generates highly performant SQL to build feature tables, which helps mitigate computing costs and engineering resources when the data sets become large, dependencies become complex, and features require data from multiple sources.

Use-cases

  • Create analytics queries like demographic views, user activity views, etc.
  • Send data using a Reverse ETL pipeline to various cloud destinations.
  • Use RudderStack Audiences to send customer profiles to marketing tools (available for beta customers).

See also

Questions? Contact us by email or on Slack