Several key factors contribute to the success of a predictive analytics implementation. Paramount among those factors is a comprehensive understanding of the company's business goals and business challenges. At the start of a predictive analytics implementation, there is always a tendency to rush forward and select a statistical technique or algorithm before developing a complete understanding of the company's business needs. This "rush to analytical nirvana" often obfuscates the real business challenge at hand. Before we break open the shrinkwrap and attempt to meander through the confusion of neural nets, decision trees and clustering, let us first determine the real business questions that need to be answered:

These questions represent only a small sample of the type of customer-related challenges that face most companies. Hence, there exists a need to categorize the business challenges within a consistent framework that can then provide a roadmap for selection of the appropriate analytical techniques. As a first step, we will focus on the key high-level business activities: Classification: The goal of classification analysis is to assign customers to previously defined groups by identifying the attributes which characterize that customer group. The model is developed from historical data by examining already-classified customers and inductively finding a predictive pattern. The identified model and its attributes are then used to classify the new customers. Classification deals with discrete outcomes. Examples of business applications include fraud detection, credit scoring, customer churn and retention.

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