Method and System for Recommendation Engine Optimization

1 . A method for a process performed on a computer, the method comprising:

receiving a first user input on a surface of a user interface, the first user input identifying a primary performance indicator;

receiving a second user input on the surface of a user interface, the second user input identifying a set of key drivers;

accessing a collection of data records comprising a composite numerical representation of a primary performance indicator, wherein the primary performance indicator comprises a data point having a calculated ordinal data level;

using the first and second user inputs to determine a set of key drivers having an influence on the primary performance indicator, wherein each of the key drivers comprises an ordinal data point having a calculated ordinal data level;

using the first and second user inputs to determine the key driver that has the highest probability of increasing the primary performance indicator by utilizing an algorithm based on the results of an ordinal logistical regression; and

providing a recommended action from a user customizable candidate set of recommendations corresponding to the key driver having the highest probability of increasing the primary performance indicator;

the aforementioned steps carried out solely by the computer.

2 . The method of claim 1 , wherein the primary performance indicator comprises a customer satisfaction rating.

3 . The method of claim 2 , wherein the key drivers with greatest significance for improving the primary performance are determined for separate operational units according to ranking criteria selected from one of customer geography, customer age, or customer income level.

4 . The method of claim 1 , wherein the step of determining a set of key drivers comprises accessing a collection of data records comprising a composite numerical representation of factors influencing the primary performance indicator.

5 . The method of claim 1 , wherein the ordinal data level of the primary performance indicator is calculated by discretion of continuous data points into ordinal data points.

6 . The method of claim 1 , wherein each member of the key set of drivers further comprises a subset of discrete nominal data points corresponding to different actions related to the key driver.

7 . The method of claim 1 , wherein each member of the key set of drivers further comprises a subset of discrete ordinal data points corresponding to different actions related to the primary performance indicator.

8 . The method of claim 7 , further comprising the step of utilizing an ordinal logistical regression to calculate which member of the subset of key drivers has the highest probability of improving the primary performance indicator wherein each of the members of the subset of key drivers are independently increased by a single ordinal data level.

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

receiving a first user input on a surface of a user interface, the first user input identifying a set of key drivers;

receiving a second user input on a surface of a user interface, the second user input identifying a set of optimization goals;

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 angel 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.

10 . The method of claim 9 , wherein the method further comprises the step of allocating a numerical value to each of the one or more optimization goals such that the sum of the numerical allocation equals a whole number.

11 . The method of claim 9 , wherein the step of calculating a target level comprises allocating a predetermined number points between two or more optimization goals and calculating an angle resulting from the allocation by computing the arctangent of the first goal divided by the second goal.

12 . The method of claim 9 , further comprising accessing a user customizable candidate set of recommendations corresponding to the key driver and recommending an action having the greatest likelihood of improving the key drivers.

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

graphic user interface means for selecting a set of key drivers;

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 at least two key drivers from the set of selected 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.

14 . The system of claim 13 , further comprising means for characterizing a subset of key drivers for separate operational units according to ranking criteria selected from one of customer geography, customer age, or customer income level.

15 . The system of claim 13 , wherein each member of the key set of drivers further comprises a subset of discrete ordinal numerical data points corresponding to different actions related to the primary performance indicator.

16 . The method of claim 15 , further comprising means for utilizing an ordinal logistical regression to calculate the probability of increasing the customer satisfaction index if each of the members of the subset of key drivers are independently increased by a single ordinal data level.

17 . The method of claim 16 , wherein the means for calculating a target level comprises allocating points between the two key driver metrics and calculating an angle resulting from the allocation by computing the arctangent of the first key driver metric divided by the second key driver metric.

18 . The method of claim 16 , further comprising accessing a user customizable candidate set of recommendations corresponding to the key driver and recommending an action having the greatest likelihood of improving the key drivers.

19 . The method of claim 18 , wherein the recommendations made to the end user are based on a hierarchal lookup keyed first on the key driver and second on one of business brand, business geography, or business operating unit.