Analysis: Decision Trees Boost Web Site Performance - InformationWeek

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Software // Information Management

Analysis: Decision Trees Boost Web Site Performance

Too much data to make sense of the customer? Slow, one-dimensional analytics won’t cut it when Web site visitors need to see cross-sell offers in milliseconds. Decision trees help clear the path to buyer--and seller--satisfaction.

Trees Of Insight

A decision tree is, to quote Wikipedia, "an idea-generation tool that generally refers to a graph or model of decisions and their possible consequences." Firms in financial services, insurance, telecommunications and other industries have employed decision trees to segment large databases so they can determine customer lifetime value, detect fraud and engage in other forms of business intelligence and analysis. Often associated with machine learning, decision trees are typically built for perform segmentation and classification purposes. Decision trees are excellent for modeling consumer behavior because they let you see what combinations of data attributes can best predict who will buy what.

Decision trees let you measure the "information gain" in the classification of a dependent variable, such as "will buy" versus "will not buy," through comparison of hundreds of independent variables, such as age, income, number of Web site visits and total sales. You can automate data segmentation by using machine-learning algorithms, which work with decision trees to split and test for independent variables that increase the information gain to be found between "will buy" and "will not buy" customers.

Automated decision trees perform classification through recursive partitioning. That is, they split and measure the amount of information a single data variable provides for determining a dependent variable. Let's say you are trying to classify fruit, and you have three data attributes: weight, shape and color. A decision tree quickly eliminates weight, which provides little information because the three fruits weigh about the same. Shape offers a higher information gain; "round" segregates bananas from apples and oranges. However, the decision tree would rank color as the variable offering the most information gain because with this attribute you can discriminate easily between oranges, apples and bananas.

The same process works for determining which variables are most valuable in forecasting who will buy or not buy. Decision trees also are helpful in segmenting one-time buyers from repeat buyers, multiple-product buyers from single-item buyers, and so on. In addition, decision trees can be constructed to predict the success of cross sales, as well as the lifetime value of online visitors and shoppers.

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