A decision tree methodology based uplift modeling approach, wherein one decision tree is developed simultaneously on test and control groups with the objective of maximizing the mean-difference in identified subsets within the data. The incrementality modeling approach discussed may be applied in any situation where an incrementality model is required to determine who among a group of users should be exposed to a treatment with the goal of receiving a positive response.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system comprising: a processor; a data collection module configured to collect test group data representing users who have received a treatment, wherein the test group data includes a test output value, and one or more identification variables; the data collection module being further configured to collect control group data representing users who have not received the treatment, wherein the control group data includes a control output value, and the one or more identification variables; a microsegment module configured to separate the test group data and the control group data into one or more microsegments, wherein the microsegments are defined based on the one or more identification variables; an analysis module configured to compare the control group data with the test group data; an identification module configured to identify a microsegment from among the one or more microsegments having a greatest difference in the corresponding test output value and the control output value; a list generation module configured to create a list of the one or more identification variables corresponding to the microsegment; a user selection module configured to select one or more users having the one or more identification variables corresponding to the microsegment having the greatest difference between the corresponding test output value and the control output value; and a communication module configured to deliver new content to the one or more users. 2. The system of claim 1 wherein the microsegment module is further configured to determine a maximum number of the identification variables corresponding to the one or more microsegments; 3. The system of claim 1 wherein the treatment is an email. 4. The system of claim 1 wherein the new content delivered by the communication module is a new email to the one or more users. 5. The system of claim 1 wherein the test output value and the control output value are continuous values. 6. The system of claim 1, wherein the one or more identification values identified by the data collection module include site activity, demographics, product affinity, and previous communications. 7. A method comprising: obtaining test group data representing users who have received a treatment, wherein the test group data includes a test output value, and one or more identification variables; obtaining control group data representing users who have not received the treatment, wherein the control group data includes a control output value, and the one or more identification variables; separating the test group data and the control group data into one or more microsegments, wherein the microsegments are defined based on the one or more identification variables; comparing the control group data with the test group data; identifying a microsegment from among the one or more microsegments having a greatest mean-difference between the corresponding test output value and the control output value; and generating a list of the one or more identification variables corresponding to the microsegment having the greatest mean-difference between the corresponding test output value and the control output value. 8. The method of claim 7 wherein the comparison of the control group data with the test group data comprises: calculating the mean values of the corresponding microsegments to the test group data and the control group data; and determining the mean-difference between the microsegment of the test group data and the control group data. 9. The method of claim 7, further comprising: selecting one or more users having the one or more identification variables corresponding to the microsegment having the greatest mean-difference between the corresponding test output value and the control output value; and delivering new content to the one or more users. 10. The method of claim 7 wherein the one or more microsegments are defined based on a predetermined maximum number of the one or more identification variables; wherein the maximum number is determined based on a statistically significant difference in mean between the corresponding microsegments of the test group data and the control group data. 11. The method of claim 7 wherein the treatment is an email containing an incentive. 12. The method of claim 9 wherein the new content is a new email. 13. The method of claim 7 wherein the test output value and the control output value are continuous values. 14. The method of claim 7 wherein the test output value and the control output value represent a gross merchandise bought by the users represented by the test group data and the control group data. 15. The method of claim 7 wherein the one or more identification variables include site activity, demographics, product affinity, and previous communications. 16. A computer-readable medium embodying instructions that, when executed by a processor, perform operations comprising: obtaining test group data representing users who have received a treatment, wherein the test group data includes a test output value, and one or more identification variables; obtaining control group data representing users who have not received the treatment, wherein the control group data includes a control output value, and the one or more identification variables; separating the test group data and the control group data into one or more microsegments, wherein the microsegments are defined based on the one or more identification variables; comparing the control group data with the test group data; identifying a microsegment from among the one or more microsegments having a greatest difference between the corresponding test output value and the control output value; and generating a list of the one or more identification variables corresponding to the microsegment having a greatest mean-difference between the corresponding test output value and the control output value. 17. The computer-readable medium of claim 16, wherein the operations further comprise: selecting one or more users having the one or more identification variables corresponding to the microsegment having the greatest difference between the corresponding test output value and the control output value; and delivering content to the one or more users. 18. The computer-readable medium of claim 11, wherein the operations farther comprise: calculating the mean values of the corresponding microsegments to the test group data and the control group data; and determining the mean-difference between the microsegment of the test group data and the control group data 19. The computer-readable medium of claim 16, wherein the operations further comprise: selecting one or more users having the one or more identification variables corresponding to the microsegment having the greatest mean-difference between the corresponding test output value and the control output value; and delivering new content to the one or more users. 20. The computer-readable medium of claim 16, wherein the test output value and the control output value are continuous values.