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7. Modeling with Decision Trees > Predicting Signups

Predicting Signups

Sometimes when a high-traffic site links to a new application that offers free accounts and subscription accounts, the application will get thousands of new users. Many of these users are driven by curiosity and are not really looking for that particular type of application, so there is a very low likelihood that they will become paying customers. This makes it difficult to distinguish and follow up with likely customers, so many sites resort to mass-emailing everyone who has signed up, rather than using a more targeted approach.

To help with this problem, it would be useful to be able to predict the likelihood that a user will become a paying customer. You know by now that you can use a Bayesian classifier or neural network to do this. However, clarity is very important in this case—if you know the factors that indicate a user will become a customer, you can use that information to guide an advertising strategy, to make certain aspects of the site more accessible, or to use other strategies that will help increase the number of paying customers.


  

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