Are you still trying to prevent churn? It’s time to reframe how we are thinking about the churn problem. We suggest starting with: Who are the customers that are more likely to stay, and how can we find or foster more of them?
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.
Watch this webinar to learn:
- How we can identify those customers most likely to stay, and more importantly explain why.
- How Causal AI can deliver on the promise of precision recommendations that drive the desired behavior.
- How Causal AI can provide reactive interventions based on who the customer is and what is most likely to retain them.
Claudia Westby, Applied Data Science at casuaLens
Prior to joining causaLens Claudia focused on applying machine learning to online marketing strategies at ConsultMyApp and building features of fintech applications at Enova International. Here at causaLens she [ ]. Claudia holds a B.S. in Mathematics and Computer Science from Fordham University.
Daniel Crowe, Marketing Domain Expert at causaLens
Daniel has spent the better part of his career working with companies that seek to change the world by empowering the current and next generations to solve the worlds most complex problems. Most recently
Daniel led all marketing activities and a global community of over 10k engineers and data scientists for another revolutionary deep tech startup in London, Vaticle. Beyond leading marketing strategy and multi-channel execution, he has produced over 500 events live and online, including Vaticle’s first user conference in London. His written work has been featured in DZone, Hackernoon, and Towards Data Science. His dog Gece thinks he could spend a little more time in the park.
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The Next Chapter in AI for Marketing