Predict and drive customer retention and churn management

American Express Australia used machine learning to identify 24% of customer accounts that would close within four months allowing them to take preventative save actions

France Telecom''s Telekomunikacja Polksa realised that certain customers have a greater or lesser influences on networks of mobile phones users. If highly connected networkers churn then this is likely to cause a large ripple effect. To improve customer churn prediction and identification of who to retain they developed social graphs and analysis based on the transaction history and network connections of customers. This allowed them to improve prediction by 47%.

PayPal improves customer churn and retention metrics with machine learning

Equinix predicts customer churn with 90% accuracy using a machine learning neural network model

Neopost identifies customers at risk of churn with machine learning using PredicSis

T-mobile reduces churn by up to 50% by identifying and retaining highly-influential ''tribe leader'' customers with advanced predictive modelling