Patent Application
20150332414
None.
The present invention relates to financial transaction data and systems and methods for using such data. More particularly, the invention relates to systems and methods for determining items purchased based on financial transaction data that omits direct itemization, and processing such data to provide financial information relating to a customer or merchant.
Corporations and government agencies have a keen interest in any available information regarding the flow of money as it relates to the potential prediction of future purchases and consumer actions. Merchants often want to monitor and review their company and/or business performance to obtain additional clients/customers and increase revenues for their particular business. Many merchants maintain extensive records of business transactions that identify itemized products and their associated invoices, and correlate this information with particular customers in order to obtain relevant business information that may assist a merchant's business decision-making. However, such information is limited to the particular merchant, and without significant knowledge of related customer transactions occurring with other merchants. Thus, a given merchant possesses limited detailed knowledge as to its customers' purchases of items and services associated with other (e.g. competitor or ancillary merchant) stores. Credit card companies, banks and other financial institutions may have knowledge of various payment card transactions, but do not necessarily receive specific records containing itemized invoicing and associated products purchased.
A system and method is provided for determining categories and/or types of items purchased based on financial transaction data that omits direct itemization, and processing such data to provide financial information relating to the customer and/or merchant.
In embodiments, systems and computer-implemented methods are provided to determine a category or type of item purchased as part of a given payment card transaction based on applying a predictive model analysis of previous payment card transactions associated with multiple customers and merchants to one or more data parameters of the given payment card transaction.
In embodiments, systems and methods for determining a type or category of product purchased as part of a payment card transaction between a customer and a merchant, comprises receiving at a computer processor, payment card transaction record data, where the transaction record omits direct product purchase itemization data. In one embodiment, the payment card transaction record data may include one or more of a customer identifier, a merchant identifier, and a transaction purchase amount corresponding to a product purchase transaction. A predictive model is used to determine a likelihood indicator that a given type or category of product sold by the merchant matches that of the actual product purchased in the payment card transaction. The transaction record data is analyzed in order to generate one or more score indicators that represent different possible product types or categories of product purchased via the payment card transaction. In one embodiment, the transaction record data analyzed includes one or more of the customer identifier, the transaction purchase amount, and a class of the merchant corresponding to the transaction record, and is compared with historical data of previous payment card transactions, in order to generate one or more score indicators that represent different possible product types or categories of product purchased via the payment card transaction. The system compares the one or more score indicators with a threshold value to generate a score index and selects the indicator having the highest score from the score index as representative of the type or category of product actually purchased in the transaction. The transactions data base may be processed to generate merchant spend profiles and price thresholds that correspond to average prices allocated to particular categories or types of items sold by the merchant. Terminal identifier information of a merchant that identifies the particular POS terminals for which purchase transactions are made may be stored in the database and purchase prices for each transaction analyzed statistically to determine statistical average purchase prices associated with each particular terminal, in order to allocate one or more categories or types of products tending to be purchased at each of said terminals. In one embodiment, the determined average may be calculated as the arithmetic average (mean). In other embodiments, the average may be calculated as the median, mode, geometric mean and/or weighted average. Temporal purchase sequencing of transactions of the customer may be performed over a given time interval and correlated with the transaction record data to determine one or more trends of customer behavior indicative of the likelihood that a given category or type of product sold by the merchant is representative of the type or category of product purchased in the transaction. The system further may be configured to determine and utilize natural price breaks associated with a given merchant based on computerized analysis of aggregate purchase price transaction records associated with the particular merchant.
In another embodiment, a system for analyzing payment card transactions data comprises an input module for receiving a transaction record that may include one or more of a customer identifier, a merchant identifier, and a transaction amount, corresponding to a product purchase transaction, wherein the transaction record omits direct product purchase itemization data. A database is configured to store the transaction record received by the input module. A computerized predictive model is configured to determine a likelihood indicator that a given type or category of product was actually purchased as part of the transaction, based on one or more of the customer identifier, the transaction amount, a class of merchant, an amount of the transaction, and a terminal identifier, wherein the indicator is indicative of a likelihood of a correct product determination. One or more computer processors is configured for: executing the predictive models; and processing the transaction record based upon the indicator determined by the computerized predictive model. The computerized predictive model is constructed through an analytic process that identifies variables in a plurality of transaction records corresponding to purchases, and calculates at least one score based on analysis of the variables, wherein each score is indicative of a likelihood that the transaction record corresponds to a purchase of a particular product type or category. The processor is configured to aggregate transaction records to generate one or more merchant and customer profiles and associate the product categories of a given merchant with an average product price and average transaction amount. The processor may determine one or more variance thresholds for score differentials that exceed a score threshold, and select a highest score as indicative of the type or category of product purchased.
Disclosed herein are processor-executable methods, computing systems, and related processing for the administration, management and communication of data relating to the determination of a category or type of product or service purchased by a customer using a payment card in a given transaction derived from payment card transaction data from customers and merchants, wherein the given payment card transaction record omits itemization. Transaction data may include one or more of customer information, merchant information, and transaction amounts and are processed to identify purchasers of particular properties. Transaction data may be stored in a data base (e.g. a relational data base) and analyzed to link relevant fields within various records to one another in order to determine and establish relationships (e.g. cause and effect, associations and groupings) and links between and among categories of services, customers, merchants, geographic regions, frequencies of services, and the like.
An analytics engine utilizing statistical analyses and techniques applied to the payment card transaction data is implemented to analyze the payment card transactions records to determine relationships, patterns, and trends between and among the various transaction records in order to predict the type and/or category of product (or service) purchased in a given transaction without the benefit of direct itemization of the given transaction. The engine is further configured to ascribe attributes or traits to purchasers based on the payment card transaction data. Based on the payment card transaction data, characteristic traits of the purchasers that relate to specific actions are linked with a particular property in order to provide insight as to the type or category of product (or service) purchased in the given transaction. Furthermore, profiles of purchasers may be generated and those purchasers exhibiting similar purchasing trends or tendencies, and/or geographic regions are grouped together, as are merchants who provide similar services, similar price purchasing, and/or similar geography. The transaction records may be processed and segmented into various categories in order to determine information relating to purchasers of a given property to be determined, purchasing frequencies, and drivers or factors and/or conditions affecting the determined purchased property or frequency of service, by way of non-limiting example.
The analysis engine may utilize independent variables as well as dependent variables representative of one or more purchasing events, customer types or profiles, merchant types or profiles, purchase amounts, and purchasing frequencies, by way of example only. The analysis engine may use models such as regression analysis, correlation, analysis of variances, time series analysis, determination of frequency distributions, segmentation and clustering applied to the transactions data in order to determine and predict the effect particular categories of data have on other categories, and thereby determine drivers of particular actions associated with a given property represented in the transactions data.
The system according to embodiments of the present invention leverages statistical techniques to group merchants and uncover logical breaks in the transaction data, indicative of a particular type or category of purchase, in order to predict the likelihood of a particular purchase. Temporal sequencing analysis of prior known purchases and/or events, and merchant associations determined from analyzing the payment card transaction data is utilized by the system to determine or predict that a given purchase is of a particular type (or category) of product without knowledge of the itemized invoice. In this manner, application of the logic developed using the above process enables customers, markets, and/or service providers to deliver information and meaningful insight relating to various commercial and consumer related applications.
In accordance with an exemplary embodiment, the system and method described herein provide a framework to utilize payment card transactions to provide data representative of actions taken and to predict types or categories of items purchased with respect to one or more properties identifiable from the payment card transaction data. In accordance with an aspect of the present disclosure, a predictive model is developed using the multiplicity of transactions data in the transactions database and application of the analysis engine to determine factors that may affect customer purchases by grouping merchants and determining logical breaks in the transactions data based on price and/or other factors. The system is configured to determine from the transaction data that a given transaction price from a particular terminal of a particular merchant may yield a high probability or likelihood of a certain product (e.g. product type or category) being the subject of the particular transaction. The system is further configured to determine from sequencing of purchases/events as well as the merchants associations and correlations, the likelihoods of related purchases (e.g.: Individuals who purchased a gas grill will most likely buy a gas tank in the near future). The predictive model may be enhanced with additional external data (e.g. merchant transactions information relating to specific purchases, and/or other external data relating to purchase transactions contained in the transactions database) so as to verify the quality of the predictive model and the associations, and/or adapt the predictions, whereby the payment card transactions data is used as predictive data and the particular merchant data (including itemized product(s) purchased in a given transaction) represents the target. Correlation may be accomplished using context sensitive analysis of the transaction data, using information from an entity operating a website or information of historical transactions associated with the user alone, combined, or even with the assistance of a predictive model. The predictive model(s) of the present invention may include one or more of neural networks, Bayesian networks (such as Hidden Markov models), expert systems, decision trees, collections of decision trees, support vector machines, or other systems known in the art for addressing problems with large numbers of variables. In embodiments, the predictive models are trained on prior data and outcomes using a historical database of prior transactions and resulting correlations relating to a same user, different users, or a combination of a same and different users. In embodiments of the present invention, the predictive model may be implemented as part of calculation module or tool.
For a given transaction record that omits direct itemization, analysis of the record is performed using the aforementioned factors and applied back into that particular payment card transaction data, in order to determine or forecast in real time what type or category of product was purchased in that particular transaction.
It is to be understood that a payment card is a card that can be presented by the cardholder (i.e., customer) to make a payment. By way of example, and without limiting the generality of the foregoing, a payment card can be a credit card, debit card, charge card, stored-value card, or prepaid card or nearly any other type of financial transaction card. It is noted that as used herein, the term “customer”, “cardholder,” “card user,” and/or “card recipient” can be used interchangeably and can include any user who holds a payment card for making purchases of goods and/or services. Further, as used herein in, the term “issuer” or “attribute provider” can include, for example, a financial institution (i.e., bank) issuing a card, a merchant issuing a merchant specific card, a stand-in processor configured to act on-behalf of the card-issuer, or any other suitable institution configured to issue a payment card. As used herein, the term “transaction acquirer” can include, for example, a merchant, a merchant terminal, an automated teller machine (ATM), or any other suitable institution or device configured to initiate a financial transaction per the request of the customer or cardholder.
A “payment card processing system” or “credit card processing network”, such as the MasterCard network exists, allowing consumers to use payment cards issued by a variety of issuers to shop at a variety of merchants. With this type of payment card, a card issuer or attribute provider, such as a bank, extends credit to a customer to purchase products or services. When a customer makes a purchase from an approved merchant, the card number and amount of the purchase, along with other relevant information, are transmitted via the processing network to a processing center, which verifies that the card has not been reported lost or stolen and that the card's credit limit has not been exceeded. In some cases, the customer's signature is also verified, a personal identification number is required or other user authentication mechanisms are imposed. The customer is required to repay the bank for the purchases, generally on a monthly basis. Typically, the customer incurs a finance charge for instance, if the bank is not fully repaid by the due date. The card issuer or attribute provider may also charge an annual fee.
A “business classification” is a group of merchants and/or businesses, classified by the type of goods and/or service the merchant and/or business provides. For example, the group of merchants and/or businesses can include merchants and/or businesses which provide similar goods and/or services. In addition, the merchants and/or businesses can be classified based on geographical location, sales, and any other type of classification, which can be used to define a merchant and/or business with similar goods, services, locations, economic and/or business sector, industry and/or industry group.
Determination of a merchant classification or category may be implemented using one or more indicia or merchant classification codes to identify or classify a business by the type of goods or services it provides. For example, ISO Standard Industrial Classification (“SIC”) codes may be represented as four digit numerical codes assigned by the U.S. government to business establishments to identify the primary business of the establishment. Similarly a “Merchant Category Code” or “MCC” is also a four-digit number assigned to a business by an entity that issues payment cards or by payment card transaction processors at the time the merchant is set up to accept a particular payment card. Such classification codes may be included in the payment card transactions records. The merchant category code or MCC may be used to classify the business by the type of goods or services it provides. For example, in the United States, the merchant category code can be used to determine if a payment needs to be reported to the IRS for tax purposes. In addition, merchant classification codes are used by card issuers to categorize, track or restrict certain types of purchases. Other codes may also be used including other publicly known codes or proprietary codes developed by a card issuer, such as NAICS or other industry codes, by way of non-limiting example.
As used herein, the term “processor” broadly refers to and is not limited to a single- or multi-core general purpose processor, a special purpose processor, a conventional processor, a Graphics Processing Unit (GPU), a digital signal processor (DSP), a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, one or more Application Specific Integrated Circuits (ASICs), one or more Field Programmable Gate Array (FPGA) circuits, any other type of integrated circuit (IC), a system-on-a-chip (SOC), and/or a state machine.
Referring now to
The network 130 can be virtually any form or mixture of networks consistent with embodiments as described herein include, but are not limited to, telecommunication or telephone lines, the Internet, an intranet, a local area network (LAN), a wide area network (WAN), virtual private network (VPN) and/or a wireless connection using radio frequency (RF) and/or infrared (IR) transmission by way of example.
The managing computer system 110 for the payment card service provider 112 as shown in
The at least one memory device 210 may be any form of data storage device including but not limited to electronic, magnetic, optical recording mechanisms, combinations thereof or any other form of memory device capable of storing data, which associates payment card transactions of a plurality of transaction acquirers and/or merchants. The computer processor or CPU 220 may be in the form of a stand-alone computer, a distributed computing system, a centralized computing system, a network server with communication modules and other processors, or nearly any other automated information processing system configured to receive data in the form of payment card transactions from transaction acquirers or merchants 122. The managing computer system 110 may be embodied as a data warehouse or repository for the bulk payment card transaction data of multiple customers and merchants. In addition, the computer system 120 or another computer system 121 (e.g. user computer of
Referring now to
In one embodiment, the level of granularity associated with the determination of product type or product category may be a function of various factors, including but not limited to, factors such as one or more of purchase price, price breaks between items and/or categories of items, merchant category (e.g. MCC), customer purchasing history, customer and merchant profiles, purchase sequencing, and/or the particular merchant or customer at issue. Further, it is to be understood that the target decision as to a predicted product type or product category may relate to categorical or differential products or services, such as pools, big screen television sets, gaming systems, home theatre installations, automobiles, computers, jewelry, cosmetics, and myriad other product types and categories that the present system and method may use to discriminate (in relation to other products that a particular merchant may sell) based on one or more of the above-identified factors. Implementation of the present disclosure may performed without obtaining personally identifiable (private) data such that the results are not personalized. This enables maintaining privacy of a given user's identity unless the user opts-in to making such data available. In some implementations, the user data is anonymized to obscure the user's identify. For example, received information (e.g. user interactions, location, device or user identifiers) can be aggregated or removed/obscured (e.g., replaced with random identifier) so that individually identifying information is anonymized while still maintaining the attributes or characteristics associated with particular information and enabling analysis of said information. Additionally, users can opt-in or opt-out of making data for images associated with the user available to the system.
Database 310 contains a multiplicity of transaction data associated with managing computer system 110 (
An exemplary transaction record 400 associated with the payment card transaction data received via managing computer system 110 is illustrated in
More particularly, the transaction data is categorized or grouped by the processor in a plurality of ways so as to decompose or break down the various informational components of the transaction data collected within the database. Payment card transaction data 310 stored in managing computer system 110 may be filtered 330 according to the requirements of a particular application in order to selectively identify specific customers, merchants and/or industries for targeted analysis. Filtering of the data may be based on one or more of transaction purchase price (amount), merchant identifier, and terminal identifier associated with the particular merchant terminal at which the transaction was made. Filtering of the data based on time sequencing of transaction events and temporal intervals (e.g. last five years' data, seasonal date ranges, etc.), may be applied to further target particular aspects of the transaction data for given applications. In one non-limiting example, the transaction data may be augmented with external data 340 (e.g. non-payment card transaction data). The external data may reside within the same transactions data base or may be linked in a separate date base, by way of non-limiting example.
The payment card transactions records 312 may be obtained via various transaction mechanisms, such as credit and debit card transactions between customers and merchants. The external data 340 that may optionally be included in the transactions data may include samples of itemized or detailed receipts which identify specific products (and even SKUs), itemized or detailed receipts relating customer and merchant accounts with specific items purchased, dates, and locations, organizational characteristics and features of a business (firmographics) useful for segmenting markets (market research), and other relevant market data relating to one or more services, customers, and merchants contained in the transaction data. Such data may operate to link customers and merchants with particular purchases of products or services within a given transaction. Additional information such as transaction data relating to on-line purchase transactions vs. in-person purchase transactions may also be included.
Analytics engine 350 operates on the transaction data by performing statistical analyses in order to construct logical relationships within and among the transactions records data in order to determine particular properties purchased (e.g. cars, boats, gasoline purchases, oil burning water heaters, etc) as well as relationships between different purchase transactions in order to predict products purchased without the benefit of direct itemization information. Various types of models and applications may be configured and utilized by analytics engine 350 in order to derive information from the transactions data. Further statistical processing of the transaction data includes independent variable analysis purchase sequencing, segmentation, clustering, ranking, and parameter modeling, to establish profiles, trends and other attributes and relationships that link merchants, customers, events and transaction amounts to various purchases or returns. For example, the analysis engine operates on the transactions records to cluster or group certain sets of objects (information contained in the data records) whereby objects in the same group (called a cluster) express a degree of similarity or affinity to each other over those in other groups (clusters).
Further statistical and variable analysis processing 370 is utilized in order to ascribe attributes to purchasers of a given transaction. Variables such as time, purchase frequency, purchasing geography and location, aggregate customer spending, and the like may be used to develop profiles for particular transaction events, merchants, and customers, as well as more generalized aggregate profiles directed to classes or categories of products purchased, merchants, customers, and regions, as well as overall information falling within a particular goods or services category.
Data segmentation of the transactions data associated with analytics engine 350 includes dividing customer information (e.g. customer IDs) into groups that are similar in specific ways relevant to other variables or parameters such as geographic region, spending preferences, customer type (e.g. individual consumer or business), demographics, and so on. By way of example only, variables may be defined according to different merchant categories and may have different degrees of correlation or association based on the type or category of merchant. Similarly, different products and/or services of particular merchants may likewise have different degrees of correlation or association. Furthermore, variable analysis of purchasing frequency with respect to particular products and/or merchants may also be utilized as part of the analytical engine 350 in order to determine particular consumers who purchase a given property.
The profiles and attributes from block 370 may be applied to one or more particular customers 382, merchants or service providers 384, markets 386, and other applications 388 in order to provide particular insights for a select application. Such applications include by way of non-limiting example, providing enhanced product information to a third party with predicted goods and services purchased in a particular sales transaction tailored to each particular customer in view of overall customer transaction data. Additional applications may be directed to customer prospecting, customer relationship management, service interval predictions and reminders, as well as comparative profiling and evaluation of merchant and/or market costs of particular goods and services.
Data modeling may be used to develop, define, and update certain attributes and behaviors of purchasers based on classifications of purchasers and their actual purchased products. As data is collected by the system based on the transaction records, the predictive model operates to infer that a certain product was purchased in a given transaction, without itemization of that transaction. Validation of the probability modeling may be obtained by feeding data transaction records into the system (e.g. test data) that contain, customer, merchant, transaction amount, and terminal ID information (devoid of direct itemization), determining from the data and historical analytical processing a likelihood indicator that each transaction represented a particular type or category of product purchased, and then comparing the predicted values with actual data (e.g. external data such as merchant data) representing the actual products purchased in each of the transactions. Based on the comparison in view of the prior predictions, factors that influence the predictive model may be altered or updated to better enhance and refine the quality of the predictive model.
Each or any combination of the modules and components shown in
Referring now to
As a non-limiting illustration, consider a customer who enters a big-box type electronics store and makes a substantial purchase costing $750.00. The consumer presents a payment card at the merchant point of sale (POS) terminal, such as a cash register to pay for the item(s) purchased. The transaction records processed over the payment card network and analyzed by the system may include one or more of the customer card number (Customer ID), the date and possibly the time of the transaction, the Merchant ID, and the Terminal ID of the POS terminal where the sale was processed and the total amount of the transaction. The transaction is processed and upon receiving the transaction information, the card processing system stores the transaction information as a transaction record in managing computer system 110 (
Based on the data in each transaction record, a number of type-indicating variables may be determined. For instance, the class of merchant (e.g. a big-box electronics store) may give insight into the general class or category of goods and services. Additionally, the amount of the transaction may provide information for identifying or predicting the item purchased in the transaction.
Consider, for example, a merchant who operates an automobile dealership. A sales transaction that amounts to thousands of dollars may be indicative of a car purchase, while a transaction for less than fifty dollars, would not indicate a likelihood of a car purchase. Rather, such transaction amount may be indicative of a simple service such as an oil-change. While a single transaction may not be informative as to the specific nature of a purchased product, considering the transactions of numerous purchasers at numerous merchants of the same type may provide information that is used to infer the class of goods or services that are the subject of a particular transaction. Statistical analysis of the data by the analysis engine of the managing computer system enables determination of sets of price thresholds corresponding to clusters of transaction purchase prices and allocated to a corresponding category or type of item for offered for sale by the merchant. In another exemplary embodiment, the system may be configured to analyze the transaction record data such that knowledge as to the specific merchant and/or cardholder is not required in the transaction data in order to generate an itemized (product) prediction. For example, for a given transaction record that includes the transaction amount and the merchant category/classification (e.g. MCC code), the system may be able to predict the type or category of product purchased in the transaction. For example, based on historical data associated with transactions corresponding to the automobile industry, even dollar amounts (e.g. $500, $1,000, $5,000, etc.) generally describe vehicle purchases, in contrast to other possible purchases associated with that category of merchant (e.g. repairs or vehicle component/accessory purchases which tend not to be rounded). Such characteristics or traits associated with the historical data for a given merchant category may be stored in a rules data base within the analytics engine. In such a case, the system may be configured, based on determination of the merchant category (e.g. automobile dealership) and transaction amount (e.g. $4,500—round or even number) to predict that the transaction was a vehicle purchase. Thus, the particular customer or merchant historical data may not be needed in order to generate a prediction because historical profiles and analyses using the merchant's category and analysis of the merchant category's historical transaction amounts (to discriminate between categories of products) may be employed.
In addition, the analysis engine utilizes temporal sequencing of transaction data associated with a particular customer to further distinguish and allow inference of a particular class of item sold in the transaction. For example, if the electronics consumer discussed above who spent $750 at the big-box electronics store is identified as executing subsequent (or precedent) transactions soon after (or before) the big-box purchase, such as spending $150 at a video game store, and spending $25 for a subscription to a gaming magazine, it may be inferred that the $750 spent at the big-box electronics store was used to purchase a video gaming system. Sequential data analysis may be applied to individual populations or demographics to determine what types of purchases these groups of individuals make.
For example, 10 years of purchase data may be used to identify sales indicators that occur within 6 months of a triggering event. For example, if the transaction of buying a car is identified in a dataset spanning ten years, the individuals making car purchases along with their other purchases before and/or after the sale may indicate what the population generally buys within six months of buying a new car. Once such patterns are identified, particular individuals may be identified as to the likelihood that he/she is going to buy that item or not.
In another example, the terminal ID provided with the transaction data may be used to provide information as to the class of goods or services based on identifying the particular point of sale represented by the terminal ID. This is particularly relevant for a merchant such as a department store. In a department store, goods are typically segregated by class of goods into different departments. Each department generally has one or more POS terminals for processing sales within that department. Using analytical processing to correlate various transactions to a particular terminal ID or group of terminal IDs, the particular department (e.g. men's clothing, women's clothing, children's clothing, jewelry, cosmetics, etc.) associated with the terminal ID may be determined. Once the department in known, it becomes a simpler determination of the class of goods or services due to the restriction of the inquiry to goods generally offered in the identified departments. Furthermore, once the department associated with terminal ID is identified, transactions may be identified and processed with regard to amounts. When analyzing transaction amounts for a given terminal, patterns may be identified by the analytics engine that define natural breaks which allow inference of the class of goods being sold.
For example, assume a terminal is observed to have a large number of purchases for a small dollar figure (e.g. less than $10), a large number of purchases for moderate dollar figures (e.g. between $50 and $100), and a small number of transactions for “big ticket” items (e.g. transactions greater than $1,000). The gaps between these price ranges are known as natural breaks. If the terminal is associated with a perfume and jewelry counter, distinctions may be inferred based on the natural breaks between price groupings. For example, the system may be configured to identify distinctions between a fine jewelry purchase, versus a perfume purchase, versus a purchase of jewelry cleaning solution.
Referring again to
The probability is compared to a score threshold 650 to determine if the likelihood that the transaction involves a particular item is higher than a baseline likelihood. If the scores do not exceed the threshold relating to each item type considered, then no prediction is made 695. If one or more scores exceed the threshold for an associated item, the scores that exceed the threshold value are selected for further analysis 660. The differential of the selected scores is determined along with a threshold variance for the probability of each score 670. If the score differential exceeds the threshold variance 680, then the highest score is selected 690, and the score's associated item type or category is identified as the object of the transaction. If the differential does not exceed the threshold variance, then no prediction is made 695.
As shown in
The terminal ID field 7024 is also analyzed (block 820) and the transaction purchase price 7022 is compared with an average terminal purchase price associated with the particular terminal ID for the given merchant 7023, based on historical transactions data. In some instances, particular stores have terminals located at different parts of the store (e.g. terminals 015 and 010 of big box electronics merchant ID 108) and tend to process different transaction amounts due to different product categories or types. Comparison yields data indicating whether the particular transaction falls within or outside the range of one or more average transaction amounts associated with the particular merchant terminal. That is, for a given terminal ID, historical transactions data compiled for the particular terminal and merchant may yield associations of particular categories or types of purchases, and hence corresponding price amounts. For example, based on statistical analysis, the particular terminal ID 015 for merchant 108 (e.g. big box electronics store) may yield an average purchase price in the range of $700-$1500. Further, based on statistical sampling, as well as possibly external data such as price listings of the merchant, advertisements, data feeds, previously supplied merchant data, and the like, particular product or item listings or categories are associated with that terminal. For example, statistical analysis and predictive modeling (block 860) indicates that terminal ID 015 for big box electronics merchant represents a mid range ticket price for that merchant/terminal combination. Based on the merchant's product listing and product prices, gaming systems and computers represent the two categories that tend to be most associated with this terminal ID. In this example, based on the historical data and statistical analysis of the transaction data, analysis of the price differences between the given transaction price and the average terminal ID price provide insight into whether a given transaction may be determined according to the product types or categories associated with a given terminal ID. In this instance, because the transaction amount falls within the range predicted for both a gaming product and a computer, both categories are viable (as opposed to other categories, such as home theatre systems, whose price threshold exceeds that of the transaction, or ancillary equipment such as cables or small electronic devices, whose price threshold is less than that of the transaction).
In addition, purchase sequencing (block 830) and historical analysis of prior transaction purchases (and/or subsequent transaction purchases) is performed in order to obtain further information to aid in predicting the likelihood of a particular category or type of item purchased. For example,
Continuing with the present example, analysis of the transaction purchase sequencing, purchase prices, merchant/terminal IDs, and transaction date/time, the processing concludes that transaction 780 represented a gaming magazine subscription, transaction 720 represented a computer game purchase, and transaction 730, although not necessarily determinative of a given product, represented a relatively low end purchase. Other customer data may also be examined to assist in making a determination regarding a different customer. For example, customer ID 4567 and 1234 are included in the same aggregated customer profile and tend to exhibit similar purchase/spend activities. In this case, analysis of the transactions data indicates a likelihood that both purchased the same item (710, 740), with a greatest likelihood of the item being a game station, based on the transactions data and predictive model. Information concerning the determined item may be output (block 870) to third parties interested in providing advertisements and/or other information relating thereto. Although the predictive model may also provide a likelihood indicator that the particular category of product purchased in transaction 710 was a computer, the higher value likelihood indicator represents a game station. Additional factors in predicting certain products for a given transaction include checking the return flag (block 860) and analyzing history data to determine the nature of the return and of the original product purchase and/or new product purchased. The terminal ID may be checked in relation to the original transaction as well as the return, in addition to the transaction return amount and original purchase amount. Refunds from other stores of the same merchant or similar merchants may also be analyzed to determine a likelihood of a given category or type of item.
The flow charts described herein do not imply a fixed order to the steps, and embodiments of the present invention may be practiced in any order that is practicable. In embodiments, one or more steps of the methods may be omitted, and one or more additional steps interpolated between described steps. Note that any of the methods described herein may be performed by hardware, software, or any combination of these approaches. For example, a non-transitory computer-readable storage medium may store thereon instructions that when executed by a processor result in performance according to any of the embodiments described herein. In embodiments, each of the steps of the methods may be performed by a single computer processor or CPU, or performance of the steps may be distributed among two or more computer processors or CPU's of two or more computer systems. In embodiments, one or more steps of a method may be performed manually, and/or manual verification, modification or review of a result of one or more processor-performed steps may be required in processing of a method.
The embodiments described herein are solely for the purpose of illustration. Those in the art will recognize that other embodiments may be practiced with modifications and alterations limited only by the claims.
