Patent Application
20150371240
1. Field of the Disclosure
The present disclosure relates to electronic transaction processing. More specifically, the present disclosure is directed to method and system for data analysis of buying patterns in support of marketing cashless transaction services to commercial entities.
2. Brief Discussion of Related Art
The use of payment devices for a broad spectrum of cashless transactions has become ubiquitous in the current economy, according to some estimates accounting for hundreds of billions or even trillions of dollars in transaction volume annually. While a layman might typically consider the cashless transaction payment scenario as it is applied in retail transactions of common experience, it is further becoming the case that the use of cashless payment devices is becoming more prevalent to facilitate commercial transactions.
Those of ordinary skill in the art will be acquainted with a purchase order system of commercial buying. A commercial buying entity delegates purchasing power, for example to one of its employees, and will have a system in place to issue a purchase order, having a unique purchase order number, for each authorized transaction. The purchase order will often specifying goods and price, among other terms defining the purchase authority. Each purchase order can be associated with a particular vendor, and for a particular amount of transaction. The respective vendor will then cite the purchase order number to request payment on a subsequent invoice for the transaction.
This purchase order system is cumbersome, however. At least for the buyer, there is conservable administrative overhead. On the other hand, the seller must typically still wait for payment according to the terms of the sale. In recent years, the purchase order system has been increasing supplanted by use of cashless transaction devices, e.g., payment cards, etc. In this way, a payment card may be issued in the name of an authorized officer on behalf of the commercial entity. The transaction device may have limitations on its authority corresponding to the authorized cardholder. The use of a transaction device in the ordinary stream of commerce also offers the benefit to the vendor of instant and available payment for invoices, among many other benefits.
The process and parties typically involved in consummating a cashless payment transaction can be visualized for example as presented in
In cases where the merchant 16 has an established merchant account with an acquiring bank (also called the acquirer) 20, the merchant 16 communicates with the acquirer to secure payment on the transaction. An acquirer 20 is a party or entity, typically a bank, which is authorized by the network operator 22 to acquire network transactions on behalf of customers of the acquirer 20 (e.g., merchant 16). Occasionally, the merchant 16 does not have an established merchant account with an acquirer 20, but may secure payment on a transaction through a third-party payment provider 18. The third party payment provider 18 does have a merchant account with an acquirer 20, and is further authorized by the acquirer 20 and the network operator 22 to acquire payments on network transactions on behalf of sub-merchants. In this way, the merchant 16 can be authorized and able to accept the payment device 14 from a device holder 12, despite not having a merchant account with an acquirer 20.
The acquirer 20 routes the transaction request to the network operator 22. The data included in the transaction request will identify the source of funds for the transaction. With this information, the network operator 22 routes the transaction to the issuer 24. An issuer 24 is a party or entity, typically a bank, which is authorized by the network operator 22 to issue payment devices 14 on behalf of its customers (e.g., device holder 12) for use in transactions to be completed on the network. The issuer 24 also provides the funding of the transaction to the network provider 22 for transactions that it approves in the process described. The issuer 24 may approve or authorize the transaction request based on criteria such as a device holder's credit limit, account balance, or in certain instances, more detailed and particularized criteria including transaction amount, merchant classification, etc., which may optionally be determined in advance in consultation with the device holder and/or a party having financial ownership or responsibility for the account(s) funding the payment device 14, if not solely the device holder 12.
The decision by the issuer 24 to authorize or decline the transaction is routed through the network operator 22 and acquirer 20, ultimately to the merchant 16 at the point of sale. In a one-message based transaction system, the transaction is thus consummated, with payment routed between issuer 24 and acquirer 20 via the network operator. Alternately, in a two-message system, the approval of the transaction by the issuer 24 is subsequently settled or paid to the acquirer 20, who then reconciles with the merchant.
The issuer 24 may then look to its customer, e.g., device holder 12 or other party having financial ownership or responsibility for the account(s) funding the payment device 14, for payment on approved transactions, for example and without limitation, through an existing line of credit where the payment device 14 is a credit card, or from funds on deposit where the payment device 14 is a debit card. Generally, a statement document 26 provides information on the account of a device holder 12, including merchant data as provided by the acquirer 20 via the network operator 22.
The network operator 22 can further build and maintain a data warehouse that stores and augments transaction data for use in marketing, macroeconomic reporting, etc. This data warehouse includes the transaction records of cardholders and merchants, from which information may be gleaned concerning their respective buying and selling patterns, etc. The data warehouse can be advantageously supplemented by third party provided data, among these and without limitation credit reporting agency data sources (e.g., Dunn & Bradstreet, Hoover's or the like), industry intelligence data (Standard & Poor's, etc.).
Both the network operator 22, and the issuer 24, inter alia, have an interest in growing their market for commercial payment services facilitated by the cashless transaction cycle described above. To this extent, the issuer 24 can look to the highest performing of its clients, in order to use their characteristics as models of other potential high-volume users. The instant disclosure proposes a method of user analysis that will identify characteristics of commercial cashless payment users to serve as models to drive expansion of usage.
Therefore, provide according to the instant disclosure is a method of targeting commercial entities for transaction-card usage revenue enhancement. The presently disclosed method includes functionally combining a first plurality of electronically searchable data sources concerning a second plurality of actual or potential commercial transaction-card using entities, where the first plurality of electronically searchable data sources including data concerning respective ones of the second plurality of commercial transaction card-using entities, relating to its relationship with a transaction-card issuer, firmographic data concerning the respective ones of the second plurality of commercial transaction card-using entities, and transaction record data concerning transaction card usage by the respective one of the second plurality of commercial transaction card-using entities. The first plurality of electronically searchable data sources is electronically searched to identify one or more model-performance ones of the second plurality of actual or potential commercial transaction-card using entities. A set of key metric categories is identified among the first plurality of electronically searchable data sources, in which the model-performance card-using entities exceed their peers. A list derived from the second plurality of actual or potential commercial transaction-card using entities is prepared, the list including of those actual or potential commercial transaction-card using entities whose measurements in one or more key metric categories exceed their peers.
In a further embodiment of the disclosed method, the data concerning respective ones of the second plurality of commercial transaction card-using entities relating to its relationship with a transaction-card issuer comprises one or more of a number of transaction cards held by the customer, the tenure of business of the transaction card-using entity with the issuer, the market segment a particular commercial card entity represents to the issuer, the amount of credit line advanced to the card user by the issuer, credit risk data concerning the transaction card-using entities, and whether the transaction card-using entity's account is actively managed by the issuer.
In a further embodiment of the disclosed method, the firmographic data concerning the respective ones of the second plurality of commercial transaction card-using entities comprises one or more of industry segment data, revenue data, issuer profitability data, creditworthiness data, forecast or historical data as to any of these.
In a further embodiment of the disclosed method, transaction record data comprises one or more of total spend data, spend category data, share of spending data among categories, and top merchants patronized.
In a further embodiment of the disclosed method, a model-performance commercial transaction card-using entity is measured according to one or more of total spend volume, and relative share of spending across multiple merchant categories.
In a further embodiment of the disclosed method, the list comprises those actual or potential commercial transaction-card using entities whose measurements in a plurality of the key metric categories exceed their peers.
In another aspect of the present disclosure, a non-transitory machine readable recording medium stores thereon a program of instructions which, when executed by a computer processor, cause the processor to execute a method of targeting commercial entities for transaction-card usage revenue enhancement, including the features and aspects described above and hereinafter.
In another aspect of the present disclosure, a system for targeting commercial entities for transaction-card usage revenue enhancement, includes a processor, and a non-transitory machine readable recording medium stores thereon a program of instructions which, when executed by the processor, cause the processor to execute the method, including the features and aspects described above and hereinafter.
These and other purposes, goals and advantages of the present disclosure will become apparent from the following detailed description of example embodiments read in connection with the accompanying drawings.
Some embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings, in which like reference numerals refer to like structures across the several views, and wherein:
With reference to
The process according to the instant disclosure combines several types and sources of data. For example, issuer data 102 is known to the issuer based upon its relationship with a given commercial card user. This information may be inherent to establishing and building the cardholder relationship and already known to the issuer 24. For example, issuer data 102 may include which cards may be attributed as a group to which commercial card user, the number of transaction cards held by the customer, their tenure of business with the issuer 24, the market segment a particular commercial card entity represents to the issuer 24, the amount of credit line advanced to the card user by the issuer 24, credit risk data concerning the commercial card user, and whether the commercial card entity's account is actively managed by the issuer 24.
A further layer of information comprises firmographic data 104 concerning corporate card users, particularly those that are current or prospective clients of the issuer 24. Generally, this firmographic data 104 is sourced from free or paid commercial sources, for example and without limitation credit reporting agency data sources, industry intelligence data sources, including without limitation, Dunn & Bradstreet, Manta, Hoover's, or the like. Firmographic data 104 may include industry segment, annual sales, number of employees, history and projection of company size.
A still further layer of information comprises transaction data 106 collected by the network operator 22 in their daily operations. The transaction data 106 is a fertile source of information from which spending patterns can be identified and analyzed. For example, data metrics such as aggregate spending amount, and category of spending can be readily discerned from the transaction record.
Having functionally combined at least issuer data 102, firmographic data 104, and transaction data 106, a first level of automated benchmarking 108 is added to form a combined data set 110. Using the automated benchmarking 108 in combination with issuer data 12, firmographic data 104 and transaction data 106 can identify a limited set of likely targets for card usage and revenue growth. Highest-volume entities are identified from transactional data. These high-volume entities are then compared to their peers in a number of categories to identify any characteristics in which they exceed their peers. Accordingly, other commercial card-using entities having similar characteristics, but lower spending on cards, are identified as likely candidates for revenue enhancement and growth. Moreover, the process lends itself readily to automation.
Turning now to
Additional issuer-specific segmentations may include a separation between issuer clients whose accounts are actively managed, and unmanaged accounts. Among managed accounts, the account manager can be considered. Certain customers can be identified by an issuer 24 as a strategic customer, and analysis can be conducted among the strategic customers only, for example. The foregoing will be considered, without limitation, among a group of issuer-specific segmentation 202.
A further exemplary data category may be based upon geographic and firm details 204. That is to say, certain characteristics of the companies per se, for example location, industry, revenue, etc. may form the basis for a first threshold screening to identify likely candidates for card usage growth to target marketing efforts.
A next exemplary data category may be based upon the categories of spending 206, which is to say categories of merchant patronized, using the transaction devices. For example, merchants are routinely classified by their line of work. For purposes of commercial card use analysis, merchants can also be grouped according to their function with respect to the purchasing entity. For example, certain merchants, such as hotels and restaurants, fall into a broader “Travel and Entertainment” category. In particular, these serve generally the same purpose to a business client as they would to a leisure client. On the other hand, trade merchants or the like would fall under a Business-to-Business (B2B) category. A particular commercial card user making use of the card in one category may be a good candidate to introduce expansion of use into others. Moreover, experience has shown that the two different classes of user represent a different type of use of the card. In particular, adoption of card use in B2B transactions represents a greater level of commitment to card use, and also greater spend volume potential relative to business revenue. In a related aspect, a further data category may be a relative share of spending 208, i.e., one or more ratios or other comparisons of spending by a commercial card user between the various spend categories, e.g., travel & entertainment vs. B2B categories, among others.
A further exemplary data category may be based upon Trended Spending Metrics 210. Trended spending metrics can include the length of tenure a particular commercial card user has with the issuer 24. It may include a record of spending volume over time. Another exemplary Trended Spending Metric may be a number of cards or payment devices issued to a given commercial card user.
A further exemplary data category is considered Optimization Data 212. Optimization data may include industry benchmarking data, such as those published by market research organizations. Industry benchmarking data can include ranking of a business entity among its peers in one or more relevant metrics. In addition to an entity's own metrics, its comparative ranking can be used to forecast targets of card spending potential.
Referring now to
Of course the categories listed in
The system and method according to the present disclosure presents multiple benefits for both the issuer 24 and network operator 22 from a revenue growth perspective. In the first instance, by combining the multiple data sources which were previously maintained separately for separate purposes, it is possible to discern characteristics of high-volume commercial card users that were not apparent from any component data source separately. Accordingly, these characteristics may be used to identify likely candidates to implement commercial card usage or to grow current usage.
Furthermore, the combination of data sources allows the user to impute missing data from one commercial card user entity to another commercial card user entity, particularly in the case of related business entities. In particular, the instant assignee has developed and disclosed techniques for partial and approximate matching of entity data from disparate sources and formats, as well as for merchant data aggregation. See, e.g., U.S. Pat. No. 8,458,071, and any related applications, or U.S. patent application Ser. No. 13/791,078, filed 8 Mar. 2013, and any related applications. The foregoing applications are commonly assigned with the instant application, and the complete disclosures of both, and any related applications, are hereby incorporated by reference for all purposes.
Moreover, the data mining based on the combined database may be automated in order to identify top-performing users; identify the characteristics of those top-performing users according to one or more predetermined categories; compare the characteristics of those top-performing users to industry peers in order to identify one or more key measurements; and set threshold levels for benchmarking likely opportunities for portfolio acquisition and enhancement; and return a list of the most likely prospective commercial card users based upon their metrics in one or more key categories.
It will be appreciated by those skilled in the art that the methods as described above may be operated by a machine operator having a suitable interface mechanism, and/or more typically in an automated manner, for example by operation of a network-enabled computer system including a processor executing a system of instructions stored on a machine-readable medium, RAM, hard disk drive, or the like. The instructions will cause the processor to operate in accordance with the present disclosure. Moreover, the methods described herein may be performed by the network operator 22, given access to the issuer data 102 as noted. Alternately, the network operator 22 may provide the system or software for implementing the described method to the issuer 24 as a tool for their use.
Turning then to
Variants of the above-disclosed and other features and functions, or alternatives thereof, may be desirably combined into many other different systems or applications. Various presently unforeseen or unanticipated alternatives, modifications, variations, or improvements therein may be subsequently made by those skilled in the art which are also intended to be encompassed by the following claims.
