Abstract: Disclosed herein are systems and methods of individual level learning that include receiving purchase event data from a merchant device that indicates that a purchase event occurred by a user on a user device, and transmitting the purchase event data to an analytics server. The methods may also include processing the purchase event data. The processing may include calculating a time gap for each of two sequential purchase events in a list of purchase events, and calculating an average duration of consecutive events by averaging all of the purchase events in the list of purchase events. The method may determine a purchase hazard probability that a purchase event will occur on a given day, when the average duration of consecutive events is larger than a standard deviation of the event occurring. When the purchase hazard probability is above a threshold, the system may push a message to the user device.

Abstract: An apparatus for generating merchant clusters based upon monitored consumer transaction activity can include a processor configured to communicate with a consumer transaction database. The processor can be configured to obtain transaction activity of consumers. The processor can be configured to generate a reference cluster based on the obtained transaction activity. The processor can be configured to generate at least one regional merchant cluster for each of a plurality of different geographic regions based on the obtained transaction activity. The processor can be configured to generate a cluster labeling scheme, for each of the different geographic regions, based on comparing each regional merchant cluster for that corresponding geographic region against the reference cluster.

Abstract: Disclosed herein are systems and methods of individual level learning that include receiving purchase event data from a merchant device that indicates that a purchase event occurred by a user on a user device, and transmitting the purchase event data to an analytics server. The methods may also include processing the purchase event data. The processing may include calculating a time gap for each of two sequential purchase events in a list of purchase events, and calculating an average duration of consecutive events by averaging all of the purchase events in the list of purchase events. The method may determine a purchase hazard probability that a purchase event will occur on a given day, when the average duration of consecutive events is larger than a standard deviation of the event occurring. When the purchase hazard probability is above a threshold, the system may push a message to the user device.

Abstract: A method and system include receiving a request to initiate a purchase transaction with a payment card product; generating a message to verify an authenticity of a cardholder of the payment card product, wherein the message includes a request for data about a previous transaction executed with the payment card product; receiving, in response to the generated message, input from the cardholder; comparing the input from the cardholder to stored data associated with the previous transaction; and one of confirming the cardholder as a true owner of the payment card product or denying the cardholder as the true owner of the payment card product based on the comparison. Numerous other aspects are provided.

Abstract: Disclosed herein are systems and methods of individual level learning that include receiving purchase event data from a merchant device that indicates that a purchase event occurred by a user on a user device, and transmitting the purchase event data to an analytics server. The methods may also include processing the purchase event data. The processing may include calculating a time gap for each of two sequential purchase events in a list of purchase events, and calculating an average duration of consecutive events by averaging all of the purchase events in the list of purchase events. The method may determine a purchase hazard probability that a purchase event will occur on a given day, when the average duration of consecutive events is larger than a standard deviation of the event occurring. When the purchase hazard probability is above a threshold, the system may push a message to the user device.

Abstract: An apparatus for generating merchant clusters based upon monitored consumer transaction activity can include a processor configured to communicate with a consumer transaction database. The processor can be configured to obtain transaction activity of consumers. The processor can be configured to generate a reference cluster based on the obtained transaction activity. The processor can be configured to generate at least one regional merchant cluster for each of a plurality of different geographic regions based on the obtained transaction activity. The processor can be configured to generate a cluster labeling scheme, for each of the different geographic regions, based on comparing each regional merchant cluster for that corresponding geographic region against the reference cluster.

Abstract: An apparatus for determining local availability of a merchant can include a processor configured to communicate with a consumer transaction database. The processor can be configured to identify an estimated location of a potential consumer based at least in part on transaction activity of the potential consumer obtained from the consumer transaction database. The processor can also be configured to identify, for each merchant category, a location of a merchant located closest to the estimated location of the potential consumer from a plurality of merchants in the merchant category. The processor can also be configured to determine, for each merchant category, whether the potential consumer has local availability of a merchant based on the identified location of the merchant located closest to the estimated location of potential consumer for the corresponding merchant category.

Abstract: A method and system include receiving a request to initiate a purchase transaction with a payment card product; generating a message to verify an authenticity of a cardholder of the payment card product, wherein the message includes a request for data about a previous transaction executed with the payment card product; receiving, in response to the generated message, input from the cardholder; comparing the input from the cardholder to stored data associated with the previous transaction; and one of confirming the cardholder as a true owner of the payment card product or denying the cardholder as the true owner of the payment card product based on the comparison. Numerous other aspects are provided.

Abstract: Disclosed herein are systems and methods of individual level learning that include receiving purchase event data from a merchant device that indicates that a purchase event occurred by a user on a user device, and transmitting the purchase event data to an analytics server. The methods may also include processing the purchase event data. The processing may include calculating a time gap for each of two sequential purchase events in a list of purchase events, and calculating an average duration of consecutive events by averaging all of the purchase events in the list of purchase events. The method may determine a purchase hazard probability that a purchase event will occur on a given day, when the average duration of consecutive events is larger than a standard deviation of the event occurring. When the purchase hazard probability is above a threshold, the system may push a message to the user device.

Abstract: Exemplary systems and methods are provided for directing offer content to consumers at communication devices, while the consumers are at merchants. An exemplary method includes capturing an image of a consumer when the consumer is in the vicinity of a product at a merchant, where the image depicts a facial expression of the consumer, and determining an intensity associated with the facial expression of the consumer, as captured in the image. The method also includes determining, by a computing device, a location at the merchant of a communication device associated with the consumer and selecting, by the computing device, an offer associated with the product for the consumer, based on the intensity of the facial expression and the determined location of the communication device, thereby relying at least in part on a consumer reaction to select the offer.

Abstract: Exemplary systems and methods are provided for directing offer content to consumers at communication devices, while the consumers are at merchants. An exemplary method includes detecting an application identifier associated with a communication device at or near a merchant, and receiving signal strength records from the communication device indicative of signal strengths for wireless networking devices at a given time and associated with the application identifier. The method also includes accessing a zone map for the merchant that includes different zones each defining a position of at least one product, and plotting a path of the communication device, per given time, relative to the zone map. The method further includes displaying an offer for a product to the communication device, based on the path of the consumer through the merchant, along with other factors such as historical behavior patterns of the consumer, similar patterns from other consumers, etc.

Abstract: A system, method, and computer-readable storage medium configured to detect and model impulse behavior.

Abstract: Exemplary embodiments of systems and methods are provided for locating products within a shopping region. In an exemplary embodiment, a method includes receiving, at a computing device, a request for a product from a consumer, and accessing, by the computing device, a product position data structure that is specific to the shopping region, where the product position data structure includes multiple product entries with each of the multiple product entries being specific to a merchant within the shopping region. The method also includes identifying at least one product entry, from the product position data structure, based on the request for the product received from the consumer, and then causing, by the computing device, via an internet-based application, a position of the identified at least one product entry to be displayed at a communication device associated with the consumer, relative to a position of the consumer in the shopping region.

Abstract: Method for predicting cardholder spending can include storing information regarding payment card transactions of at least one cardholder at a database. Information regarding cultural heritage locations can be stored at the database. Merchants related to cultural heritage tourism can be automatically identified based on the information stored at the database. Based on the information regarding payment card transactions of each cardholder at the identified merchants, whether each cardholder is in a cultural heritage tourist target category can be automatically detected. Whether each cardholder is interested in additional cultural heritage tourism transactions can be predicted using a predictive model based on the information regarding payment card transactions, the information regarding cultural heritage locations, and the detected cultural heritage tourist target category. Systems, networks, and media are also disclosed.

Abstract: A method for identifying repulsive brands includes: storing a plurality of brand profiles, each brand profile including data related to a brand including a brand identifier and a plurality of competitor brand identifiers associated with competitors to the related brand; storing a plurality of transaction data entries, each transaction data entry including data related to a payment transaction involving a consumer including a specific brand identifier associated with a brand involved in the related payment transaction; identifying an associated brand profile for each transaction data entry where the included brand identifier corresponds to the specific brand identifier included in the respective transaction; and identifying repulsive brands based on inclusion of an associated competitor brand identifier in the competitor brand identifiers included in each associated brand profile identified for each transaction data entry in the transaction database.

Abstract: Systems, apparatus and methods for obtaining consumer survey responses from a payment account holder at a point of interaction and then using that information to predict future consumer purchasing behavior. In an embodiment, at least one consumer survey question that represents a portion of a consumer survey poll is transmitted to a cardholder for display on a display screen at a point of interaction. The process includes receiving a response to the consumer survey question(s), storing the response(s) in association with purchase transaction data of the cardholder in a storage device, and then transmitting a commercial message personalized to the cardholder for display on the display screen at the point of interaction.

Abstract: A computer-implemented method of imputing a personal holiday associated with a consumer into a forecast model includes steps, using a processing device, for accessing transaction records associated with purchasing activity of a consumer in a payment network over a predetermined period of time, the transaction records including information about a purchase and date when the purchase was made. The method further includes predicting a personal holiday of the consumer within the predetermined period of time based on the transaction records, wherein the personal holiday repeats at regular intervals within the predetermined period of time; and imputing the date into a forecast model to predict future purchase activity of the consumer. A system includes a processing device and memory to store instructions that, when executed by the processing device, cause the processing device to perform the operations comprising the method steps of the computer-implemented method.

Abstract: A method and system for predicting online and in-store purchasing by a cardholder using a computer device coupled to a database are provided. The method includes receiving a set of active cardholders along with their corresponding historical transaction information, categorizing the set of cardholders based on predefined parameters, and selecting a representative subset of cardholders from the categorized set of cardholders. The method also includes analyzing the historical transaction information for each of the cardholders included within the subset of cardholders and grouping each cardholder included within the subset of cardholders to one of an online shopper group or a physical store shopper group. The method further includes developing a model based on the analyzed historical transaction information and the grouping of the cardholders and applying the model to a candidate cardholder to predict a likelihood that the candidate cardholder will purchase an item online or from a physical store.

Abstract: A system, method, and computer-readable storage medium configured to model and predict consumer cellular mobile carrier switching intentions of a payment cardholder based on transaction payment card purchases.

Abstract: Methods, systems, and computer readable media for predicting consumer purchase behavior are disclosed. In one example, the method includes obtaining consumer intention data and purchase transaction data associated with a consumer and merging the consumer intention data with the purchase transaction data to construct a target variable. The method further includes merging the target variable with at least one independent variable to create an aggregated data set, generating an intent-action gap model utilizing a portion of the aggregated data set, and applying the intent-action gap model to the aggregated data set to generate an intent-action gap score associated with the consumer, wherein the intent-action gap score serves as an indication of purchase behavior indicator associated with the consumer.