Bringing AI to Marketing
The evolution of AI, machine learning, and data science have been an increasingly integral part of the transformation of many industries, and marketing is no exception.
Learn MoreGiven that it costs 5-10 times more to acquire a new customer than to retain an existing one, it seems obvious that all businesses should engage in some level of churn prevention. Because of its business impact and its relative ease in execution, for many types of business, churn prediction is a great first project to tackle with machine learning and AI.
In a subscription-based business, even a small rate of monthly/quarterly churn will compound quickly over time. Just 1% monthly churn translates to almost 12% yearly churn. Given that it’s far more expensive to acquire a new customer than to retain an existing one, businesses with high churn rates will quickly find themselves in a financial hole as they have to devote more and more resources to new customer acquisition.”
Michael Redbord, General Manager of Service Hub at HubSpot, Customer Churn Prediction Using Machine Learning: Main Approaches and Models, KDnuggets, 2019
When building any machine learning-based model, but especially for churn, one has to be careful that the model is actually learning the right thing. For instance, one of the common pitfalls for a churn modeling project is to train the model on both past and future events. To avoid this common mistake, think about what the model will know once deployed into production:
Another common pitfall is to produce models that predict people that are obviously going to churn. This is not only a problem with the model, but also with the business implications: the output suggests action when it will be ineffective, since the customers have already decided their intent to churn and thus aren’t sensitive to marketing actions. Instead, models should catch the people who are evaluating leaving and trigger an early warning system.
Finally, the last pitfall is to see a churn model as a one-shot study. Multiple reviews and iterations create a successful strategy to retain and increase customer loyalty. This will likely improve the scalability and reproducibility of the project as well.
Dataiku is one of the world’s leading AI and machine learning platforms, supporting agility in organizations’ data efforts via collaborative, elastic, and responsible AI, all at enterprise scale. Building a churn prediction model is made easy with Dataiku’s advanced features:
Recommendation engines can be used across industries to provide value either to end customers or to employees of the organization itself.
Read moreThe evolution of AI, machine learning, and data science have been an increasingly integral part of the transformation of many industries, and marketing is no exception.
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Learn MoreCoyote uses IoT-based devices and mobile applications that enable their users to warn other drivers of traffic hazards. Having started out with predictive analytics for churn prevention, today Dataiku has become an integral part of Coyote’s predictive safety operations.
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