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Customer Churn Prediction to Improve Retention

The standard machine learning churn playbook is flawed. Causal AI goes beyond ML predictions, and is able to recommend optimal pricing decisions and resource allocation to minimize churn.

Stop predicting churn

Churn prediction is a common use-case for AI, but the results are typically underwhelming. Typical churn models fail to respect the business’ constraints and goals and struggle to perform in a dynamic business environment.

While conventional AI systems can predict likely churners, they don’t recommend a course of action to prevent churn. Causal AI identifies the true drivers of churn: it is uniquely capable of recommending a set of interventions to optimally allocate resources and budgets to increase retention, based on business goals and key metrics. 

Causal discovery with human domain knowledge and Causal AI

With humans-in-the-loop, domain knowledge isn’t left off the table. Marketers can interact with a visual representation of the model’s causal graph to see which factors affect their target KPI(s) in which ways, and actively add in the constraints and nuances of your domain and the world your customers live in. 

A 5% increase in retention can increase profits 25-95%

We’ve integrated world class Causal AI capabilities into our Retention Optimization solution. Our application draws on next generation explainability, machine imagination, and intervention design. Our solution typically reduces churn by an extra 4-9% above standard machine learning-based churn prevention models with triple digit ROI results. We’ve rewritten the customer retention playbook.

Transparent and explainable

Mapping the causal drivers of your domain also enables you to get explanations for recommendations and current retention rates.

Traditional ML solutions for retention struggle to avoid biases and as a marketer, you may want to assess the causal model to ensure that it is fair when it comes to age or gender. Get an easy to understand readout of whether or not the model shows any indication of bias, along with a more detailed graph that allows you to see how the model’s outcomes vary with different inputs for protected variables.

Customer retention for data scientists with Causal AI

decisionOS allows causal customer retention decision workflows to be implemented, using the latest causality research, and deployed seamlessly so that they can be used by business stakeholders to enhance enterprise decision-making.

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Learn more about how Causal AI is revolutionizing the modern marketing stack

Marketers are frustrated with current AI technology, which is limited to predicting customer behaviors. Causal AI goes beyond predictions, generating insights into what drives customer behavior. Industry leading marketing teams are leveraging these insights to make better decisions.

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