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Data Mining
Predictive Analytics and Customer Behavior
“Predictive analysis is the decision science that removes guesswork out of the decision-making process and applies proven scientific guidelines to find right solution in the shortest time possible.” (Kaith, 2011) There are seven steps to Predictive Analytics: spot the business problem, explore various data sources, extract patterns from data, build a sample model using data and problem, Clarify data – find valuable factors – generate new variables, construct a predictive model using sampling and validate and deploy the model. By using this method, businesses can make fast decisions using vast amounts of data. There are three main benefits of predictive analytics: minimizing risk, indentifying fraud, and pursuing new sources of revenue. Being able to predict the risks involved with loan and credit origination, fraudulent insurance claims, and making predictions with regard to promotional offers and coupons are all examples of these benefits. It basically reduces the cost of making mistakes. This type of algorithm allows businesses to test all sorts of situations and scenarios it could take years to test in the real world. Studying customer behavior gives businesses a competitive advantage and allows them to stay ahead of the competition in their market place.
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