Data Mining

Data Mining

June 1st, 2012

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.

Associations Discovery and Customer Purchases

Association analysis is useful for discovering interesting relationships hidden in large amounts of data. There are two things to remember when using association analysis with regard to market data: discovering patterns from a large transaction data set can be computationally expensive and some of the discovered patterns are potentially spurious because they may happen simply by chance.

Association discovery finds rules about items that appear together in an event such as a purchase transaction. Market-basket analysis is a well-known example of association...