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Machine learning models overview

Overview​

Marketers require robust predictive insights to fuel growth and improve retention more than ever before. MessageGears effortlessly connects with your data warehouse, enabling ML-driven models for propensity, churn, lifetime value (LTV), and more.

Common ML model use cases​

Increase customer loyalty​

Predict key customer lifecycle events to drive repeat purchases and prompt existing customers to choose your company over a competitor.

Reduce churn​

Identify and engage with customers that are likely to churn and see rises in profits, consistency, and long-term stability.

Optimize paid media investment​

Help campaigns achieve higher conversion rates and avoid ineffective media campaigns.

Increase personalization​

Use information like product and channel preferences to deliver more relevant and valuable content to increase engagement.

ML models available​

To learn more about each model, how they work, their outputs, and the minimum requirements for use, follow the links below.

Using model outputs directly in the UI​

Once a model is scored, its output can be applied directly when configuring a campaign or journey, for example routing recipients with Optimal Channel, or segmenting a Blueprint or journey on a model's score. For details, see Using predictive models in campaigns and journeys and Reserved predictive model column names.

How to get started​

Each of our models require different data inputs to train the models and score the target populations. To learn more about these inputs please refer to the Data input schemas. If you are interested in activating MessageGears machine learning models on your account please speak to your Customer Success Manager.