29 May '15, 6am

Predicting which customers may churn

David Dague is vice president of marketing at Gravy By David Dague For brands and online publishers, developing and deploying predictive modeling techniques to identify customers at-risk to churn is not rocket science. But it is pretty close. One of the simplest methods is known as “RFM” or recency, frequency and monetary. As the very terms imply, this method uses data mining to analyze and profile customers who have churned based on how recently they had made a purchase, how frequently they had purchased and their overall purchase value over time prior to defecting. These churn-risk profiles are then applied to current-customer records. Other more sophisticated methods employ multi-dimensional logistical regression and Bayesian probabilistic modeling, adding geo-demographics, psychographic panel data and feature engineering, which comprises characterizations of customer i...

Full article: http://www.luxurydaily.com/predicting-which-customers-may...

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