Method and system for recommendation engine otimization

A method for a process performed on a computer for constructing recommendation-based predictive models, the method comprising:

• accessing a collection of data records comprising a composite numerical representation of customer satisfaction indices, wherein the customer satisfaction index comprises a data point having a calculated ordinal data level;
• determining at least two optimization goals for improving the customer satisfaction index, wherein the optimization goals can be used to compute an angle for comparison purposes;
• determining a set of at least two key drivers that influence the customer satisfaction index, wherein each of the key drivers comprises a data point having a calculated ordinal data level;
• utilizing an ordinal logistical regression to determine the key driver that has the highest probability of improving the customer satisfaction index, wherein each of the members of the set of key drivers are independently increased by a single ordinal data level;
• calculating the key driver performance of each key driver within each key driver metric;
• and
• determining which key driver, if improved, most likely results in a value closest to the target angle.

A computer implemented system for optimizing recommendation engine output, the system comprising:

• means for accessing a collection of data records comprising a composite numerical representation of customer satisfaction index, wherein the customer satisfaction index comprises a data point having a calculated ordinal data level;
• means for accessing a set of at least two key drivers, wherein the at least two key drivers are determined to have an impact on the customer satisfaction index and wherein each of the key drivers comprises an ordinal data point having a calculated ordinal data level;
• means for utilizing an ordinal logistical regression to determine the key driver that has the highest probability of improving the customer satisfaction index wherein each of the members of the set of key drivers are independently increased by a single ordinal data level;
• means for calculating a target level comprising a user-determined numerical combination of at least two key driver metrics;
• means for calculating the key driver performance of each key driver within each key driver metric;
• and
• means for determining which key driver, if implemented, most likely results in a value closest to the target value.