The startup I'm part of uses ML to predict which end users are likely to churn for our customers. We work with B2B and B2C SAAS, mobile apps and games, and e-c

The startup I'm part of uses ML to predict which end users are likely to churn for our customers. We work with B2B and B2C SAAS, mobile apps and games, and e-commerce. For each of them, it is a generalized solution customized to allow them to know which end users are most at risk of churning. The amount of time range varies depending on their customer lifecycles, but for longest lifecycles we can, with high precision, predict churn more than 6 months ahead of actual attrition. Even more important than "who is at risk?" is "why are they at risk?". To answer this we highlight patterns and sets of behavior that are positively and negatively associated with churn, so that our customers have a reason to reach out, and are armed with specific behaviors they want to encourage, discourage, or modify. This enables our customers to try to save their accounts / users. This can work through a variety of means, campaigns being the most common. For our B2B customers, the account managers have high confidence about whom they need to contact and why. All of this includes regular model retraining, to take into account new user events and behaviors, new product updates, etc. We are confident in our solution and offer our customers a free trial to allow us to prove ourselves. I can't share details, but we just signed our biggest contract yet, as of this morning. :) For more http://appuri.com/ http://appuri.com/ A recent whitepaper "Predicting User Churn with Machine Learning" http://resources.appuri.com/predicting_user_churn_ml/ http://resources.appuri.com/predicting_user_churn_ml/