Abstract: The system may be configured to perform operations including receiving a transaction history for a consumer having transaction information associated with a plurality of transactions; detecting within the transaction information for each transaction a characteristic, resulting in a plurality of characteristics; calculating a respective value associated with each characteristic, wherein the respective value is at least one of a number or percentage of transactions having the characteristic; assigning a respective weight to each characteristic, producing an assigned respective weight for each characteristic; applying the assigned respective weight to the respective value associated with each characteristic to produce a respective weighted value for each characteristic; combining the respective weighted values of the plurality of characteristics; and/or producing a compliance score in response to the combining the respective weight values.

Abstract: Aspects of the disclosure relate to a computing system, computer-implemented method, and computer readable storage media for predicting, for a consumer in a group of consumers assigned to a subset of consumers in a plurality of subsets at the present time, which subset the consumer will be assigned to at a predetermined time in the future. Further aspects of the disclosure relate to a computer-implemented method for determining the likelihood that consumers in a group of consumers will migrate between subsets in a plurality of subsets of consumers. Further aspects of the disclosure related to a computer-implemented method for determining an expected revenue for a group of consumers at a predetermined time in the future.

Abstract: A system, method, and computer readable storage media for training a model to estimate an unknown consumer behavior while preserving consumer privacy by combining a first training data set of a first organization with a second training data set of a second organization on a third-party computer system, wherein the second training data set contains an attribute value the first organization wishes to estimate, and wherein the first organization cannot access the third-party computing system, providing a trainable model definition to the third-party computer, training the model, and returning the trained computer model.

Abstract: The system may be configured to perform operations including receiving a transaction history for a consumer having transaction information associated with a plurality of transactions; detecting within the transaction information for each transaction a characteristic, resulting in a plurality of characteristics; calculating a respective value associated with each characteristic, wherein the respective value is at least one of a number or percentage of transactions having the characteristic; assigning a respective weight to each characteristic, producing an assigned respective weight for each characteristic; applying the assigned respective weight to the respective value associated with each characteristic to produce a respective weighted value for each characteristic; combining the respective weighted values of the plurality of characteristics; and/or producing a compliance score in response to the combining the respective weight values.

Abstract: A third party facilitates exchange of customer data between first and second entities while maintaining customer privacy. Personally identifiable information (PII) and first entity customer attributes of a first set of customers are received from a first entity. PII for a second set of customers is received from a second entity. First and second set common customers are identified using the PII of the first and the second set of customers. Subsequently, a list of third set of customers is sent to the second entity. The list of third set of customers includes the common customers and a plurality of other customers from the second set of customers. Second entity customer attributes are received for each customer in the list of third set of customers. Further, the first entity customer attributes of the common customers and the second entity customer attributes of the common customers are linked.

Abstract: The present disclosure includes a system, method, and article of manufacture for spend based targeting and measurement. The method may comprise transmitting to a third party system a model and/or a measurement file associating a first party cookie ID with a model, and receiving, from the third party system, an updated model (and/or measurement) file associating a third party cookie ID with web-browsing data. The third party cookie ID may anonymously and uniquely correspond to the first party cookie ID, and the first party cookie ID may be stripped from the updated model (and/or measurement) file to de-identify a customer associated with the first party cookie ID. Moreover, the web-browsing data may be received by the third party system from a second party system.

Abstract: A method and computer program product for identifying attributes of customers and potential customers, allowing marketing to be directed at such consumers. Information is received regarding each of a number of consumers. This information can include demographic information, data regarding spending habits, and information as to how such attributes may have changed over time. Once attributes have been determined, and any changes over time to such attributes have been determined, a correlation of the attributes is performed. Such combined attributes are denoted as composite dimensions. Given the composite dimensions, as well as attributes that may not have been correlated with any other attributes, consumers are plotted in the resulting multi-dimensional space. Sets of consumers that appear to be grouped in this multi-dimensional space are then identified. Common attributes of this consumer group are identified. Unique attributes of a given consumer in such a group are also identified.

Abstract: A third party facilitates exchange of customer data between first and second entities while maintaining customer privacy. Personally identifiable information (PII) and first entity customer attributes of a first set of customers are received from a first entity. PIT for a second set of customers is received from a second entity. First and second set common customers are identified using the PII of the first and the second set of customers. Subsequently, a list of third set of customers is sent to the second entity. The list of third set of customers includes the common customers and a plurality of other customers from the second set of customers. Second entity customer attributes are received for each customer in the list of third set of customers. Further, the first entity customer attributes of the common customers and the second entity customer attributes of the common customers are linked.

Abstract: According to embodiments of the present invention there is provided a computer-based method and a computer program product for automatically sorting customers who make purchases from merchants using a credit card. The computer-based method includes the following steps. An adjusted index is calculated for a customer based on an amount of money the customer spent across merchants during a time period. The adjusted index is compared to a cut-off value. The customer is classified based on the comparison of the adjusted index to the cut-off value. In an embodiment, the method also includes re-classifying the customer based on at least one of (i) information about the merchants, or (ii) a number of transactions in a predetermined industry.

Abstract: A third party facilitates exchange of customer data between first and second entities while maintaining customer privacy. Personally identifiable information (PII) and first entity customer attributes of a first set of customers are received from a first entity. PII for a second set of customers is received from a second entity. First and second set common customers are identified using the PII of the first and the second set of customers. Subsequently, a list of third set of customers is sent to the second entity. The list of third set of customers includes the common customers and a plurality of other customers from the second set of customers. Second entity customer attributes are received for each customer in the list of third set of customers. Further, the first entity customer attributes of the common customers and the second entity customer attributes of the common customers are linked.

Abstract: Consumer spend by industry is modeled based on the industry sizes of wallet of consumers having a high share of wallet with a financial institution. A size of wallet is calculated for each consumer in a plurality of consumers. A share of wallet for each consumer is also calculated. A subset of the plurality of consumers whose share of wallet is above a given percentage of their size of wallet is then determined. For each consumer in the subset, an industry size of wallet is determined. A correlation between the industry size of wallet of a given consumer and one or more characteristics of the given consumer is then derived using the industry size of wallet for the consumers in the subset.

Abstract: Consumer spend by industry is modeled based on the industry sizes of wallet of consumers having a high share of wallet with a financial institution. A size of wallet is calculated for each consumer in a plurality of consumers. A share of wallet for each consumer is also calculated. A subset of the plurality of consumers whose share of wallet is above a given percentage of their size of wallet is then determined. For each consumer in the subset, an industry size of wallet is determined. A correlation between the industry size of wallet of a given consumer and one or more characteristics of the given consumer is then derived using the industry size of wallet for the consumers in the subset.

Abstract: Consumer spend by industry is modeled based on the industry sizes of wallet of consumers having a high share of wallet with a financial institution. A size of wallet is calculated for each consumer in a plurality of consumers. A share of wallet for each consumer is also calculated. A subset of the plurality of consumers whose share of wallet is above a given percentage of their size of wallet is then determined. For each consumer in the subset, an industry size of wallet is determined. A correlation between the industry size of wallet of a given consumer and one or more characteristics of the given consumer is then derived using the industry size of wallet for the consumers in the subset.

Abstract: The present disclosure includes a system, method, and article of manufacture for spend based targeting and measurement. The method may comprise transmitting to a third party system a model and/or a measurement file associating a first party cookie ID with a model, and receiving, from the third party system, an updated model (and/or measurement) file associating a third party cookie ID with web-browsing data. The third party cookie ID may anonymously and uniquely correspond to the first party cookie ID, and the first party cookie ID may be stripped from the updated model (and/or measurement) file to de-identify a customer associated with the first party cookie ID. Moreover, the web-browsing data may be received by the third party system from a second party system.

Abstract: Consumer spend by industry is modeled based on the industry sizes of wallet of consumers having a high share of wallet with a financial institution. A size of wallet is calculated for each consumer in a plurality of consumers. A share of wallet for each consumer is also calculated. A subset of the plurality of consumers whose share of wallet is above a given percentage of their size of wallet is then determined. For each consumer in the subset, an industry size of wallet is determined. A correlation between the industry size of wallet of a given consumer and one or more characteristics of the given consumer is then derived using the industry size of wallet for the consumers in the subset.

Abstract: Consumer spend by industry is modeled based on the industry sizes of wallet of consumers having a high share of wallet with a financial institution. A size of wallet is calculated for each consumer in a plurality of consumers. A share of wallet for each consumer is also calculated. A subset of the plurality of consumers whose share of wallet is above a given percentage of their size of wallet is then determined. For each consumer in the subset, an industry size of wallet is determined. A correlation between the industry size of wallet of a given consumer and one or more characteristics of the given consumer is then derived using the industry size of wallet for the consumers in the subset.

Abstract: A third party facilitates exchange of customer data between first and second entities while maintaining customer privacy. Personally identifiable information (PII) and first entity customer attributes of a first set of customers are received from a first entity. PII for a second set of customers is received from a second entity. First and second set common customers are identified using the PII of the first and the second set of customers. Subsequently, a list of third set of customers is sent to the second entity. The list of third set of customers includes the common customers and a plurality of other customers from the second set of customers. Second entity customer attributes are received for each customer in the list of third set of customers. Further, the first entity customer attributes of the common customers and the second entity customer attributes of the common customers are linked.

Abstract: According to embodiments of the present invention there is provided a computer-based method and a computer program product for automatically sorting customers who make purchases from merchants using a credit card. The computer-based method includes the following steps. An adjusted index is calculated for a customer based on an amount of money the customer spent across merchants during a time period. The adjusted index is compared to a cut-off value. The customer is classified based on the comparison of the adjusted index to the cut-off value. In an embodiment, the method also includes re-classifying the customer based on at least one of (i) information about the merchants, or (ii) a number of transactions in a predetermined industry.

Abstract: According to embodiments of the present invention there is provided a computer-based method and a computer program product for automatically sorting customers who make purchases from merchants using a credit card. The computer-based method includes the following steps. An adjusted index is calculated for a customer based on an amount of money the customer spent across merchants during a time period. The adjusted index is compared to a cut-off value. The customer is classified based on the comparison of the adjusted index to the cut-off value. In an embodiment, the method also includes re-classifying the customer based on at least one of (i) information about the merchants, or (ii) a number of transactions in a predetermined industry.

Abstract: To determine brand affiliation, a consumer criterion may be identified. A filtered group of one or more consumers may be created in which each consumer in the filtered group is subject to the consumer criterion. A percentage of consumers in the filtered group of consumers having at least one transaction with a brand of interest may then be determined. A percentage of consumers in a control group of consumers having at least one transaction with the brand of interest may also be determined. A ratio comparing the percentage of consumers in the filtered group to the percentage of consumers in the control group may then be calculated. Based on the calculated ratio, the brand affiliation between the consumer criterion and the brand of interest is determined. This affiliation can be leveraged to target marketing materials for one of the brands to consumers of the second brand.