Finding differences among two or more groups is an important data-mining task. For example, a retailer might want to know what the different is in customer purchasing behaviors during a sale compared to a normal trading day. With this information, the retailer may gain insight into the effects of holding a sale and may factor that into future campaigns. Another possibility would be to investigate what is different about customers who have a loyalty card compared to those who don’t. This could allow the retailer to better understand loyalty cardholders, to increase loyalty revenue, or to attempt to make the loyalty program more appealing to non-cardholders.