Computing early adopters and potential influencers using transactional data and network analysis

Abstract

The early adopters and potential influencers (EAPI) system, method and computer-readable medium provide a way to identify early adopters and potential influencers. The EAPI system obtains a list of purchases for customers of merchants and/or subscriptions from a transaction tracking system. The EAPI system creates a time-based transaction network, and using a scoring function, the EAPI system determines an early adopter and/or potential influencer ranking among customers in the network. The EAPI system may use one or more customer attributes to determine a customer's influence with respect to different dimensions.

1 .- 32 . (canceled) 
     
     
         33 . A method comprising:
 receiving transactional data associated with a plurality of merchants and a plurality of customers, the transactional data comprising a plurality of element sets, each element set comprising data elements representative of a customer, a merchant, and a timestamp;   receiving social network data associated with the plurality of customers via an application programming interface;   for each customer of the plurality of customers, generating, via a processor, one or more networks,   wherein generation of each network of the one or more networks is performed by:   (1) determining transaction data, from the transactional data, for a selected customer of the plurality of customers;   (2) determining one or more transaction merchants with whom the selected customer has transacted based on the transaction data; and   (3) computing a set of additional customers, each of which having subsequently transacted with the one or more transaction merchants with whom the selected customer transacted with,   wherein each network of the one or more networks comprises one or more merchant nodes, a plurality of customer nodes, one or more merchant-customer edges between at least one of the one or more merchant nodes and at least one of the plurality of customer nodes, one or more customer-customer edges between two or more customer nodes of the plurality of customer nodes, and a plurality of weight values each associated with at least one of the merchant-customer edges or the one or more customer-customer edges, wherein the plurality of weight values are derived based at least in part on the social network data;   generating, via the processor, a network ranking of a particular customer node of the plurality of customer nodes based at least in part on a centrality of the particular customer node within at least one of the one or more networks, and wherein the centrality is determined at least in part based on the plurality of weight values; and   transmitting a promotion to a particular customer based on the network ranking of the particular customer node satisfying a predetermined threshold.   
     
     
         34 . The method of  claim 33  further comprising:
 for each network of the one or more networks,
 determining a link analysis score for each merchant; and 
 determining a score summary representative of a degree of influence that the particular customer has over the network based on an information fusion criteria associated with the link analysis score. 
 
 
     
     
         35 . The method of  claim 34  further comprising:
 generating the network ranking of the particular customer node based on an information fusion criteria associated with one or more score summaries determined for the one or more networks. 
 
     
     
         36 . The method of  claim 33  further comprising:
 determining an effective cost of the promotion based on the network ranking. 
 
     
     
         37 . The method of  claim 33 , wherein the plurality of weight values are derived based at least in part on an expected geographical distance between respective ones of the one or more merchant nodes and the plurality of customer nodes associated with the at least one of the merchant-customer edges or the one or more customer-customer edges. 
     
     
         38 . The method of  claim 33 , wherein the social network data comprises a number of check-ins using a social network associated with the plurality of customer nodes with respect to the one or more merchant nodes. 
     
     
         39 . The method of  claim 33 , further comprising:
 determining a plurality of customers influenced by a plurality of particular customers associated with network ranking score equal to or greater than the predetermined threshold;   determining a least number of the plurality of particular customers that provide a greatest number of the determined plurality of customers influenced by the plurality of particular customers; and   transmitting the promotion to selected ones of the plurality of particular customers based on the determination of the least number of the plurality of particular customers.   
     
     
         40 . The method of  claim 39 , wherein determining the least number of the plurality of particular customers further comprises determining a least number of the selected ones of plurality of particular customers that provide a greatest number of the determined plurality of customers influenced by the plurality of particular customers and are common to the selected ones of the plurality of particular customers. 
     
     
         41 . A computer program product comprising at least one non-transitory computer-readable storage medium having computer-executable program code instructions stored therein, the computer-executable program code instructions comprising program code instructions for:
 receiving transactional data associated with a plurality of merchants and a plurality of customers, the transactional data comprising a plurality of element sets, each element set comprising data elements representative of a customer, a merchant, and a timestamp;   receiving social network data associated with the plurality of customers via an application programming interface;   for each customer of the plurality of customers, generating one or more networks,   wherein generation of each network of the one or more networks is performed by:   (1) determining transaction data, from the transactional data, for a selected customer of the plurality of customers;   (2) determining one or more transaction merchants with whom the selected customer has transacted based on the transaction data; and   (3) computing a set of additional customers, each of which having subsequently transacted with the one or more transaction merchants with whom the selected customer transacted with,   wherein each network of the one or more networks comprises one or more merchant nodes, a plurality of customer nodes, one or more merchant-customer edges between at least one of the one or more merchant nodes and at least one of the plurality of customer nodes, one or more customer-customer edges between two or more customer nodes of the plurality of customer nodes, and a plurality of weight values each associated with at least one of the merchant-customer edges or the one or more customer-customer edges, wherein the plurality of weight values are derived based at least in part on the social network data;   generating a network ranking of a particular customer node of the plurality of customer nodes based at least in part on a centrality of the particular customer node within at least one of the one or more networks, and wherein the centrality is determined at least in part based on the plurality of weight values; and   transmitting a promotion to a particular customer based on the network ranking of the particular customer node satisfying a predetermined threshold.   
     
     
         42 . The computer program product of  claim 41  further comprising program code instructions for:
 for each network of the one or more networks,
 determining a link analysis score for each merchant; and 
 determining a score summary representative of a degree of influence that the particular customer has over the network based on an information fusion criteria associated with the link analysis score. 
 
 
     
     
         43 . The computer program product of  claim 42  further comprising program code instructions for generating the network ranking of the particular customer node based on an information fusion criteria associated with one or more score summaries determined for the one or more networks. 
     
     
         44 . The computer program product of  claim 41 , wherein the plurality of weight values are derived based at least in part on an expected geographical distance between respective ones of the one or more merchant nodes and the plurality of customer nodes associated with the at least one of the merchant-customer edges or the one or more customer-customer edges. 
     
     
         45 . The computer program product of  claim 41 , wherein the social network data comprises a number of check-ins using a social network associated with the plurality of customer nodes with respect to the one or more merchant nodes. 
     
     
         46 . The computer program product of  claim 41  further comprising program code instructions for:
 determining a plurality of customers influenced by a plurality of particular customers associated with network ranking score equal to or greater than the predetermined threshold; 
 determining a least number of the plurality of particular customers that provide a greatest number of the determined plurality of customers influenced by the plurality of particular customers; and 
 transmitting the promotion to selected ones of the plurality of particular customers based on the determination of the least number of the plurality of particular customers. 
 
     
     
         47 . The computer program product of  claim 46 , further comprising program code instructions for determining a least number of the selected ones of plurality of particular customers that provide a greatest number of the determined plurality of customers influenced by the plurality of particular customers and are common to the selected ones of the plurality of particular customers. 
     
     
         48 . An apparatus comprising at least one processor and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the processor, cause the apparatus to at least:
 receive transactional data associated with a plurality of merchants and a plurality of customers, the transactional data comprising a plurality of element sets, each element set comprising data elements representative of a customer, a merchant, and a timestamp;   receive social network data associated with the plurality of customers via an application programming interface;   for each customer of the plurality of customers, generate one or more networks,   wherein generation of each network of the one or more networks is performed by:   (1) determining transaction data, from the transactional data, for a selected customer of the plurality of customers;   (2) determining one or more transaction merchants with whom the selected customer has transacted based on the transaction data; and   (3) computing a set of additional customers, each of which having subsequently transacted with the one or more transaction merchants with whom the selected customer transacted with,   wherein each network of the one or more networks comprises one or more merchant nodes, a plurality of customer nodes, one or more merchant-customer edges between at least one of the one or more merchant nodes and at least one of the plurality of customer nodes, one or more customer-customer edges between two or more customer nodes of the plurality of customer nodes, and a plurality of weight values each associated with at least one of the merchant-customer edges or the one or more customer-customer edges, wherein the plurality of weight values are derived based at least in part on the social network data;   generate a network ranking of a particular customer node of the plurality of customer nodes based at least in part on a centrality of the particular customer node within at least one of the one or more networks, and wherein the centrality is determined at least in part based on the plurality of weight values; and   transmit a promotion to a particular customer based on the network ranking of the particular customer node satisfying a predetermined threshold.   
     
     
         49 . The apparatus of  claim 48 , wherein the plurality of weight values are derived based at least in part on an expected geographical distance between respective ones of the one or more merchant nodes and the plurality of customer nodes associated with the at least one of the merchant-customer edges or the one or more customer-customer edges. 
     
     
         50 . The apparatus of  claim 48 , wherein the social network data comprises a number of check-ins using a social network associated with the plurality of customer nodes with respect to the one or more merchant nodes. 
     
     
         51 . The apparatus of  claim 48  wherein the at least one memory and the computer program code is further configured to, with the processor, cause the apparatus to at least:
 determine a plurality of customers influenced by a plurality of particular customers associated with network ranking score equal to or greater than the predetermined threshold; 
 determine a least number of the plurality of particular customers that provide a greatest number of the determined plurality of customers influenced by the plurality of particular customers; and 
 transmit the promotion to selected ones of the plurality of particular customers based on the determination of the least number of the plurality of particular customers. 
 
     
     
         52 . The apparatus of  claim 51  wherein the at least one memory and the computer program code is further configured to, with the processor, cause the apparatus to at least determine a least number of the selected ones of plurality of particular customers that provide a greatest number of the determined plurality of customers influenced by the plurality of particular customers and are common to the selected ones of the plurality of particular customers.