Automatic Peer Group Formation for Benchmarking

1 . A method of automatically generating peer groups of entities, the method comprising:

receiving data for a plurality of characteristic parameters about a number of entities;

defining a number of peer groups, k, to be generated;

defining a minimum number of entities, m, to be assigned to each peer group;

defining k initial cluster values around which to group the entities according to the data for the entity's characteristic parameters;

assigning each entity to a peer group associated with a particular initial cluster center value; and

ensuring that the number of entities assigned to each peer group is greater than the minimum number, m.

2 . The method of claim 1 , wherein ensuring that the number of entities assigned to each peer group is greater than m comprises:

evaluating the number of entities in peer groups;

reassigning an entity from a neighboring peer group to a peer group having fewer than m entities, so long as the reassigned entity has not previously be assigned to the peer group having fewer than m entities; and

repeating the evaluating and the reassigning until all peer groups include at least m entities.

3 . The method of claim 2 , wherein no entity is reassigned more than once.

4 . The method of claim 2 , wherein the assignment of each entity to a peer group associated with an initial cluster value is based on the values of the entity's characteristic parameters and the value of the initial cluster value of the peer group.

5 . The method of claim 2 , further comprising:

modifying cluster center values for peer groups to reflect values of the characteristic parameters of the entities assigned to the peer groups;

reassigning entities to peer groups based upon the values of the entities' characteristic parameters and the cluster center values of the peer groups, including any modified cluster center values;

refining peer groups by reassigning entities to peer groups to ensure that the number of entities assigned to each peer group is greater than the minimum number, m; and

repeating the modification of the cluster values, the reassignment of the entities to the peer groups, and the refining of peer groups until the cluster center values change by less than a threshold value during subsequent iterations, and until the number of entities assigned to each peer group is greater than the minimum number, m.

6 . The method of claim 2 , wherein data for the characteristic parameters comprise key performance indicators (KPI) for the entities.

7 . The method of claim 2 , further comprising:

after a plurality of entities have been assigned to a number of peer groups, such that the number of entities assigned to each peer group is greater than m, receiving a new entity to be added to a peer group;

assigning the new entity to an existing peer group associated with a particular cluster center value based on the new entity's characteristic parameters and the value of the particular cluster center value;

when the number of entities assigned to the existing peer group exceeds a maximum size threshold, partitioning the existing peer group into two new peer groups and assigning subsets of the entities from the existing peer group to each new peer group; and

determining a cluster center value associated with each new peer group.

8 . The method of claim 2 , wherein the initial cluster values are assigned randomly within bounds defined by highest and lowest values of the characteristic parameters.

9 . The method of claim 2 , further comprising:

receiving KPI data for entities;

analyzing the KPI data to generate benchmark data for a peer group having at least m entities; and

providing the benchmark data to entities in the peer group.

10 . The method of claim 9 , wherein defining a minimum number of entities, m, to be assigned to each peer group comprises defining m to be sufficiently large such that a KPI data value for an entity in a peer group cannot be determined from an average of the KPI data values for all entities in the peer group.

11 . The method of claim 9 , wherein the number of entities assigned to each peer group is greater than 3.

12 . The method of claim 9 , wherein the KPI data is received anonymously.

13 . A system for automatically generating peer groups of entities, the apparatus comprising:

a communications agent adapted to receive characteristic parameter data about entities from remote clients;

a clustering engine adapted to generate cluster center values, assign entities to cluster centers to create peer groups of entities, and adjust cluster center values according to the characteristic parameters of the entities assigned to the cluster centers;

a thresholding filter engine adapted to identify peer groups that do not meet specified size thresholds;

a refining engine adapted to reassign an entity from a neighboring peer group to a peer group that does not satisfy a minimum size threshold if the reassigned entity has not previously been assigned to the peer group that does not satisfy the minimum size requirement.

14 . The system of claim 13 , wherein the communications agent comprises a secure anonymous gateway for the transfer of characteristic parameter data and key performance indicator data for an entity.

15 . The system of claim 13 , wherein the refining engine is further adapted to:

evaluate the number of entities in different peer groups;

reassign an entity from a neighboring peer group to a peer group that does not satisfy the minimum size threshold if the reassigned entity has not previously been assigned to the peer group that does not satisfy the minimum size requirement; and

repeat the evaluating and the reassigning until all peer groups satisfy the minimum size threshold, while not reassigning an entity back to a peer group from which the entity was already reassigned.

16 . The system of claim 16 , wherein the refining engine is further adapted to modify cluster center values after reassigning an entity from a neighboring peer group to a peer group that does not satisfy the minimum size threshold.

17 . The system of claim 13 , wherein the communications agent is further adapted to receive a new entity to be assigned to a peer group after a plurality of entities have been assigned to a number of peer groups, such each peer group satisfies the minimum size threshold;

wherein the clustering engine is further adapted to assign the new entity to an existing peer group associated with a particular cluster center value based on the new entity's characteristic parameters and the value of the particular cluster center value; and

wherein, when the number of entities assigned to the existing peer group exceeds a maximum size threshold, the refining engine is further adapted to partition the existing peer group into two new peer groups, assign subsets of the entities assigned to the existing peer group to each new peer group, and determine a cluster center value associated with each new peer group.

18 . The system of claim 13 , wherein the communications agent is further adapted to receive key performance indicator (KPI) data about the entities from the remote clients, and the system further comprising a benchmarking engine adapted to statistically analyze KPI data for entities in a peer group to generate benchmark information for the entities in the peer group.

19 . The system of claim 18 , further comprising an administration module adapted to set the minimum size threshold, such that the number of entities assigned to each peer group that satisfies the minimum size threshold is sufficiently large such that a KPI data value for an entity in a peer group cannot be determined from an average of the KPI data values for all entities in the peer group.

20 . The system of claim 18 , wherein the communications agent is adapted to receive the KPI data anonymously.