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Case study

Customer churn

Finding the right treatment for the right customer at the right time.



  • Ensuring high returns on marketing investments is essential; however, retention campaigns can be costly and carry inherent risk. A poorly executed campaign can even itself cause customer churn.

  • Randomized A/B tests don’t understand confounding factors and offer limited customization needed to target distinct customer segments effectively.


  • A Causal AI is trained to identify the factors affecting churn of each customer. 

  • Based on this model, campaign simulations are run on subsets of customers instead of a “one-size-fits all” intervention. This level of personalization can improve effectiveness and significantly reduce risk.

  • Explainable recommendations are given to marketing teams to plan individual promotional strategies. Cost-efficiency is ensured by directing resources towards customers who are genuinely at risk of churning.

  • Reduction in customer churn, leading to an increased CRR.

  • Effective distribution of marketing resources, lowering CRC.

  • Insights into customer behaviors and promotional campaign efficacy.

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