Case study
Customer churn
Finding the right treatment for the right customer at the right time.
Challenge
Solution
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.
Impact
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.