Abstract: Techniques are disclosed for identifying, assessing, and presenting differences between segments of customers. The techniques identify differences in categorical features of the customers in two segments. The techniques use observed data to identify differences in a categorical feature. The techniques then assess whether the observed difference is a real difference applicable to the entire customer population or the result of random chance. The categorical features with the most significant differences (i.e., unlikely due to random chance) are presented, for example, to allow a marketer to easily appreciate the most significant segment differences. Certain techniques account for segment overlap (i.e., customers being in both segments) in assessing whether differences are due to random chance. Certain techniques limit the presented categorical features to account for common knowledge and/or false testing issues.

Abstract: Techniques are disclosed for identifying, assessing, and presenting differences between segments of customers. The techniques identify differences in categorical features of the customers in two segments. The techniques use observed data to identify differences in a categorical feature. The techniques then assess whether the observed difference is a real difference applicable to the entire customer population or the result of random chance. The categorical features with the most significant differences (i.e., unlikely due to random chance) are presented, for example, to allow a marketer to easily appreciate the most significant segment differences. Certain techniques account for segment overlap (i.e., customers being in both segments) in assessing whether differences are due to random chance. Certain techniques limit the presented categorical features to account for common knowledge and/or false testing issues.