HAQM Personalize optimizer using HAQM Pinpoint events

Note: This post was written by Ryan Lowe, an AWS Solution Architect and Chelsea Graf, Senior Pinpoint PM.

This is the second of two posts in our series on personalization with HAQM Pinpoint. Today’s customer expects a high level of personalization in order to continue engaging with an enterprise.  Rather than a generalized marketing campaign, enterprises use curated messaging to lower churn rates, increase consumer interaction, and drive higher conversion rates. Many enterprises are turning to machine learning to deliver personalized product recommendations or promotions at scale. In order to maintain an effective and relevant machine learning model, you need high volumes of recent behavioral data. Without an automated data pipeline, retraining is manual, inefficient and risks a model becoming outdated.

AWS offers the HAQM Personalize Optimizer Using HAQM Pinpoint Events solution to help you easily connect your HAQM Personalize campaigns and HAQM Pinpoint projects. This solution allows you to train and publish models quickly without support from an engineer. You can define the frequency and the type of data used to retrain your models. Using an automated retraining loop frees you to build new models and keep them relevant for your marketers.

The following diagram illustrates the flow of data in this solution.

The AWS CloudFormation template configures the event stream in an existing HAQM Pinpoint project to use HAQM Kinesis Data Firehose to export behavioral events such as email opens. It also configures the latter to store event data in HAQM Simple Storage Service (HAQM S3). The HAQM S3 data schema is stored in an AWS Glue Data Catalog enabling data queries.

There is a constant flow of real-time data moving from HAQM Pinpoint through Kinesis Data Firehose and being stored in HAQM S3. When you send a campaign, HAQM Pinpoint connects with HAQM Personalize to retrieve a personalized recommendation. This recommendation is based on the HAQM Pinpoint recommender model configuration for each user identified in the campaign.

The AWS CloudFormation template also deploys a daily batch process orchestrated by AWS Step Functions. The process begins when an HAQM CloudWatch time-based event triggers a series of AWS Lambda functions. The functions use an HAQM Athena query to query customer data stored in HAQM S3. The query result is then used to retrain HAQM Personalize by providing new interaction data from the HAQM Pinpoint event data.

To get started, visit the HAQM Personalize Optimizer Using HAQM Pinpoint Events solution page on the AWS Solutions site.