Several of the presentations I attended at Predictive Analytics World, February 2010 were quite good. In addition to the sessions noted earlier, I heard an interesting analytics solution to a knotty business problem as well as an innovative application of analytics to business. I also had the pleasure of listening to a venerable analytics pioneer and saw a presentation that did an outstanding job bridging analytics theory and practice.
Mike Driscoll's: The Social Effect: Predicting Telecom Customer Churn with Call Data, was a good illustration of predictive analytics in a larger data warehousing and BI context. Driscoll and his team analyzed billions of calls, millions of records and thousands of defectors from a Greenplum DW looking for predictors of churn. Driscoll's a big proponent of the open source R Project for Statistical Computing to support his work flow of data munge, data model, and data visualize. And with a Ph.D. in Bioinformatics, he often thinks like an epidemiologist, in this case looking for indications of contagious churn behavior. Using several social network analysis packages available in R, Driscoll's team appears to have found that churn in an individual's social network of calling accounts in a given month is likely to lead to more churn in subsequent months, a clear indication of a network effect. That contagion is overwhelmingly the strongest signal the team found from the data. A next step is to work with marketing to intervene on early network churn with email campaigns to minimize losses from the affected networks. I'd love to see the results from those experiments.
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