Using your own dataset - Predictive Segmentation Using Amazon Pinpoint and Amazon SageMaker

Using your own dataset

The Predictive Segmentation Using Amazon Pinpoint and Amazon SageMaker solution includes a simple, example dataset that contains example customer data, engagement data, and endpoint export data.

If you want to use this solution using your data, we recommend following AWS best practices for uploading data in Amazon Simple Storage Service (Amazon S3).

Use the following steps to modify the solution to use your dataset.

  1. With the help of an experienced data scientist, train the ML model using your dataset and features from Amazon Pinpoint behavioral data.

    For a list of Amazon Pinpoint events, refer to Events in the Amazon Pinpoint REST API Reference.

  2. Create a new Amazon Athena query that pulls the applicable ML model features from your dataset. For more information, refer to Getting Started in the Amazon Athena User Guide.

  3. Update the QueryAugmentStart AWS Lambda function's NAMED_QUERY environment variable with the identifier for the query you created in the previous step.