Lift Chart

The Lift Chart tab displays a graphical representation of the change in lift that a mining model causes. For example, the marketing department at Adventure Works Cycles wants to create a targeted mailing campaign. From past campaigns, they know that a 10 percent response rate is typical. They have a list of 10,000 potential customers stored in a table in the database. Therefore, based on the typical response rate, they can expect 1,000 of the potential customers to respond.

However, the money budgeted for the project is not enough to reach all 10,000 customers in the database. Based on the budget, they can afford to mail an advertisement to only 5,000 customers. The marketing department has two choices:

* Randomly select 5,000 customers to target
* Use a mining model to target the 5,000 customers who are most likely to respond

If the company randomly selects 5,000 customers, they can expect to receive only 500 responses, based on the typical response rate. This scenario is what the random line in the lift chart represents. However, if the marketing department uses a mining model to target their mailing, they can expect a larger response rate because they can target those customers who are most likely to respond. If the model is perfect, it means that the model creates predictions that are never wrong, and the company could expect to receive 1,000 responses by mailing to the 1,000 potential customers recommended by the model. This scenario is what the ideal line in the lift chart represents. The reality is that the mining model most likely falls between these two extremes; between a random guess and a perfect prediction. Any improvement from the random guess is considered to be lift.

http://technet.microsoft.com/en-us/library/ms175428.aspx