Announcing RudderStack Predictions: Automate churn and conversion scores in your warehouse | RudderStack

By neub9
3 Min Read

Machine learning has become increasingly crucial in providing personalized customer experiences and is now seen as a necessary component for maintaining a competitive edge in today’s market. Despite the widespread push for companies to leverage machine learning to its full potential, many are struggling to deliver on this front. In fact, KDnuggets reported that 80% of machine learning projects fail before deployment.

To address this issue, we are introducing RudderStack Predictions, a solution designed to streamline the creation and deployment of machine learning models for churn and conversion scores. By automating these processes, Predictions enables businesses to anticipate customer behavior and take proactive measures to reduce churn and increase conversion rates. This capability significantly reduces the time required to train and deploy machine learning models.

Predictions seamlessly integrates into existing workflows and leverages existing data platforms’ ML compute infrastructure, such as Snowflake’s Snowpark ML platform. It pushes the results directly into your warehouse, making the machine learning data immediately accessible through our Reverse ETL pipeline or Activation API. This warehouse-native approach ensures that Predictions is fully auditable, allowing for a thorough audit of models and fit metrics, runs, and outputs.

RudderStack Predictions also provides the flexibility to use any data in your warehouse, and for advanced use cases, it allows you to migrate to a version controlled, code-based workflow to create custom predictive features.

For RudderStack Enterprise customers and prospects interested in testing the product, Predictions is now available. We have partnered with Snowflake to provide a detailed Quickstart Guide, complete with a sample data set and an explanation of how Predictions utilizes Snowpark for machine learning. Additionally, we are continuously adding new models for more use cases and working on enabling the migration of existing machine learning models into Predictions for scoring and activation.

To learn more about Predictions and to get started, you can check out the documentation and reach out to our team for a personalized demo. We are also excited to share our Shopify churn model project on GitHub, which demonstrates how Predictions builds a churn score from Shopify features. Stay tuned for more updates on the future enhancements of Predictions.

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