ThinkDeeply Marketplace

Summary:

American Express has over 100 million credit card customers globally representing over $1 trillion in annual charge volume. The Australian company used advanced data analytics and machine learning to analyse historical transactions along with 115 variables to forecast customer churn. They were able to identify 24% of accounts that would close within four months allowing them to take preventative save actions.

Problem:

With a database of over a 100 million credit cards globally, that account for over $1 trillion in charge volume every year, American Express deals with vast quantities of data. They are looking to better understand customer behaviour and improve customer retention and have built strong big data capabilities.

Solution:

With access to big data, machine learning models can produce superior discrimination and thus better understand customer behavior. Through sophisticated predictive models the company has been able to analyze historical transactions and 115 variables to forecast potential churn.

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

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