Methods and systems for analyzing entity performance

Abstract

Approaches for analyzing entity performance are disclosed. A first set of data and a second set of data can be stored in a data structure. This data can be associated with a plurality of interactions, and can be modified to include additional interactions. These interactions can involve consuming entities and provisioning entities. The modified data structure can be queried to retrieve information associated with one or more entities. After information is retrieved, it can be provided to a user.

1 . A system comprising:
 one or more computer processors;   one or more computer memories;   a set of instructions incorporated into the one or more computer memories, the set of instructions configuring the one or more computer processors to perform operations for displaying an aggregate in at least near-real-time in a user interface, the facilitating including reducing an amount of processing time required to perform the displaying of the aggregate by incrementally updating the aggregate, the operations comprising:   producing the aggregate by running a query on a data structure storing a first set of data relating to interactions performed by a plurality of entities;   creating a modified data structure based on the data structure, the modified data structure including a second set of data relating to the interactions performed by the plurality of entities into the aggregate; and   based on the incremental updating of the aggregate, running the query on the modified data structure and modifying the user interface in at least near-real time to include at least some of the second set of data.   
     
     
         2 . The system of  claim 1 , wherein the data structure is stored in random access memory of a cluster computing framework configured to process data within the data structure in at least near real-time. 
     
     
         3 . The system of  claim 1 , wherein the first set of data is segmented by new customers and returning customers by determining whether a consuming entity identified in the first set of data or the second set of data has made multiple purchases within a predetermined amount of time. 
     
     
         4 . The system of  claim 1 , wherein the first set of data and the second set of data are segmented by local customers and non-local customers by comparing a home address of a consuming entity with the first set of data or the second set of data. 
     
     
         5 . The system of  claim 1 , wherein the second set of data corresponds to a consuming entity and at least one of a set of consuming entity categories, the consuming entity categories including at least one of identification, location, loyalty, credit card type, age, gender, income, children, product information, and service information. 
     
     
         6 . The system of  claim 1 , where the second set of data is used to determine a cohort and the modifying of the user interface includes updating a comparison of entities in the cohort. 
     
     
         7 . The system of  claim 1 , wherein the incremental updating of the aggregate includes running the query on the second set of data as it is acquired from a data stream after the producing of the aggregate. 
     
     
         8 . A method comprising:
 performing operations for displaying an aggregate in at least near-real-time in a user interface, the facilitating including reducing an amount of processing time required to perform the displaying of the aggregate by incrementally updating the aggregate, the operations comprising:   producing the aggregate by running a query on a data structure storing a first set of data relating to interactions performed by a plurality of entities;   creating a modified data structure based on the data structure, the modified data structure including a second set of data relating to the interactions performed by the plurality of entities into the aggregate; and   based on the incremental updating of the aggregate, running the query on the modified data structure and modifying the user interface in at least near-real time to include at least some of the second set of data.   
     
     
         9 . The method of  claim 8 , wherein the data structure is stored in random access memory of a cluster computing framework configured to process data within the data structure in at least near real-time. 
     
     
         10 . The method of  claim 8 , wherein the first set of data is segmented by new customers and returning customers by determining whether a consuming entity identified in the first set of data or the second set of data has made multiple purchases within a predetermined amount of time. 
     
     
         11 . The method of  claim 8 , wherein the first set of data and the second set of data are segmented by local customers and non-local customers by comparing a home address of a consuming entity with the first set of data or the second set of data. 
     
     
         12 . The method of  claim 8 , wherein the second set of data corresponds to a consuming entity and at least one of a set of consuming entity categories, the consuming entity categories including at least one of identification, location, loyalty, credit card type, age, gender, income, children, product information, and service information. 
     
     
         13 . The method of  claim 8 , where the second set of data is used to determine a cohort and the modifying of the user interface includes updating a comparison of entities in the cohort. 
     
     
         14 . The method of  claim 8 , wherein the incremental updating of the aggregate includes running the query on the second set of data as it is acquired from a data stream after the producing of the aggregate. 
     
     
         15 . A non-transitory computer-readable medium storing a set of instructions that are executable by one or more processors of an apparatus to cause the apparatus to perform operations for displaying an aggregate in at least near-real-time in a user interface, the facilitating including reducing an amount of processing time required to perform the displaying of the aggregate by incrementally updating the aggregate, the operations comprising
 producing the aggregate by running a query on a data structure storing a first set of data relating to interactions performed by a plurality of entities;   creating a modified data structure based on the data structure, the modified data structure including a second set of data relating to the interactions performed by the plurality of entities into the aggregate; and   based on the incremental updating of the aggregate, running the query on the modified data structure and modifying the user interface in at least near-real time to include at least some of the second set of data.   
     
     
         16 . The non-transitory computer-readable medium of  claim 15 , wherein the data structure is stored in random access memory of a cluster computing framework configured to process data within the data structure in at least near real-time. 
     
     
         17 . The non-transitory computer-readable medium of  claim 15 , wherein the first set of data is segmented by new customers and returning customers by determining whether a consuming entity identified in the first set of data or the second set of data has made multiple purchases within a predetermined amount of time. 
     
     
         18 . The non-transitory computer-readable medium of  claim 15 , wherein the first set of data and the second set of data are segmented by local customers and non-local customers by comparing a home address of a consuming entity with the first set of data or the second set of data. 
     
     
         19 . The non-transitory computer-readable medium of  claim 15 , wherein the second set of data corresponds to a consuming entity and at least one of a set of consuming entity categories, the consuming entity categories including at least one of identification, location, loyalty, credit card type, age, gender, income, children, product information, and service information. 
     
     
         20 . The non-transitory computer-readable medium of  claim 15 , where the second set of data is used to determine a cohort and the modifying of the user interface includes updating a comparison of entities in the cohort.