Lift is the improvement in response from mailing due to modeling and segmentation. Divide the response from a segment by the overall response, subtract 1 and multiply by 100.
In data mining lift is a measure of the performance of a targeting model(association rule) at predicting or classifying cases as having an enhanced response (with respect to the population as a whole), measured against a random choice targeting model. A targeting model is doing a good job if the response within the target is much better than the average for the population as a whole. Lift is simply the ratio of these values: target response divided by average response.
For example, suppose a population has an average response rate of 5%, but a certain model (or rule) has identified a segment with a response rate of 20%. Then that segment would have a lift of 4.0 (20%/5%).Typically, the modeller seeks to divide the population into quantiles, and rank the quantiles by lift. Organizations can then consider each quantile, and by weighing the predicted response rate (and associated financial benefit) against the cost, they can decide whether to market to that quantile or not.Lift is analogous to information retrieval’s average precision metric, if one treats the precision (fraction of the positives that are true positives) as the target response probability.