Predictive analytics is becoming the successor to data mining. The differentiators among data warehousing, classic data mining, and predictive analysis are listed. For instance, predictive analytics can predict the future, while data warehousing described the present and past, and classic data mining predicts the past. An example of the advantages of predictive analysis is Xerox's use of Oracle Data Mining software for clustering defects and construction of predictive models for analysis of usage profile history, maintenance data, and representation of knowledge from field engineers to predict photocopy component failure. The Xerox copier then sends an e-mail to repair staff to schedule maintenance before the device breaks down. As for prediction of the future, KXEN is used to find the optimal point at which savings from catching a bad customer becomes the cost of turning away a well-paying customer (opportunity cost). Moreover, with predictive analytics hypotheses are invented, rather than tested, so that client predictive efforts can be directed by 'the methodological injunction that determining meaning is a business task, not a statistical one.' In that context, selection of a tool for predictive analytics can be optimized to the benefit of customer recommendations, cross-selling, up-selling, personalization, loyalty development, attrition and churn forecasting, demand planning, inventory (and cost) reductions, brand development, and expertise in market dynamics.