Custom Data Science for Profile Enrichment Blueprint

Custom Data Science for Profile Enrichment Blueprint illustrates how data in Adobe Experience Platform can be used in Data Science Workspace to train, deploy, and score models to provide machine learning insights. These models can directly output to a dataset enabled for Real-time Customer Profile to further enrich customer profiles. These insights can then be actioned for personalization. Examples of machine learning insights include lifetime value scoring, product and category affinity, propensity to convert, or propensity to churn.

Use Cases

  • Extract insight and discover patterns from customer data in Experience Platform. Train and score models from this data.
  • Enrich the Real-time Customer Profile with model driven insights and attributes for more granular personalization and optimized journeys.
  • Train and Score models to determine customer insights such as customer lifetime value, propensity to convert or churn, product and content affinities, and engagement scores.

Architecture

Reference Architecture for the Custom Data Science for Profile Enrichment Blueprint

Implementation Steps

  1. Create schemas for data to be ingested.
  2. Create datasets for data to be ingested.
  3. Ingest data into Experience Platform.
  4. Create a DSW notebook.
  5. Choose a language. Python and PySpark are supported.
  6. Author model in notebook.
  7. Train the model.
  8. Score the model to generate predictions with the target data.
  9. Enable the model results dataset for profile, if pushing model results to the Real-time Customer Profile.

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