Abstract: Marketing is facilitated based on a profitability prediction. A merchant optimizes offers to a website visitor based on predicted profit for potentially offered items. The profitability predication can be deployed to determine incentives to marketing affiliates, and to determine bids for search terms when the merchant uses a paid search bid manager. Information specific to a site visitor is used to predict a profitability metric for specific items that can be offered to that visitor.

Abstract: Marketing is facilitated based on a profitability prediction. A merchant optimizes offers to a website visitor based on predicted profit for potentially offered items. The profitability predication can be deployed to determine incentives to marketing affiliates, and to determine bids for search terms when the merchant uses a paid search bid manager. Information specific to a site visitor is used to predict a profitability metric for specific items that can be offered to that visitor.

Abstract: Utilization of information in a closed loop data system further augments modeling while at the same time enabling customization of offers based on spend patterns. Data mining techniques are leveraged to identify rules to determine higher response rate populations. These rules are referred to herein as “triggers,” in that the presence of particular attributes will trigger a cardholder as being more likely to respond to a particular offer. The benefit yielded by this approach is a greater acceptance rate to an offer provided by a transactional account company. To identify the triggers, records of cardmembers who already utilize a given product are analyzed to determine their spend patterns. The spend histories of customers who are eligible to use the product are analyzed according to the identified triggers. Customers whose spend patterns most closely correspond to the triggers are then targeted with offers for the given product.

Abstract: Utilization of information in a closed loop data system further augments modeling while at the same time enabling customization of offers based on spend patterns. Data mining techniques are leveraged to identify rules to determine higher response rate populations. These rules are referred to herein as “triggers,” in that the presence of particular attributes will trigger a cardholder as being more likely to respond to a particular offer. The benefit yielded by this approach is a greater acceptance rate to an offer provided by a transactional account company. To identify the triggers, records of cardmembers who already utilize a given product are analyzed to determine their spend patterns. The spend histories of customers who are eligible to use the product are analyzed according to the identified triggers. Customers whose spend patterns most closely correspond to the triggers are then targeted with offers for the given product.

Abstract: Utilization of information in a closed loop data system further augments modeling while at the same time enabling customization of offers based on spend patterns. Data mining techniques are leveraged to identify rules to determine higher response rate populations. These rules are referred to herein as “triggers,” in that the presence of particular attributes will trigger a cardholder as being more likely to respond to a particular offer. The benefit yielded by this approach is a greater acceptance rate to an offer provided by a transactional account company. To identify the triggers, records of cardmembers who already utilize a given product are analyzed to determine their spend patterns. The spend histories of customers who are eligible to use the product are analyzed according to the identified triggers. Customers whose spend patterns most closely correspond to the triggers are then targeted with offers for the given product.

Abstract: Utilization of information in a closed loop data system further augments modeling while at the same time enabling customization of offers based on spend patterns. Data mining techniques are leveraged to identify rules to determine higher response rate populations. These rules are referred to herein as “triggers,” in that the presence of particular attributes will trigger a cardholder as being more likely to respond to a particular offer. The benefit yielded by this approach is a greater acceptance rate to an offer provided by a transactional account company. To identify the triggers, records of cardmembers who already utilize a given product are analyzed to determine their spend patterns. The spend histories of customers who are eligible to use the product are analyzed according to the identified triggers. Customers whose spend patterns most closely correspond to the triggers are then targeted with offers for the given product.

Abstract: Marketing is facilitated based on a profitability prediction. A merchant optimizes offers to a website visitor based on predicted profit for potentially offered items. The profitability predication can be deployed to determine incentives to marketing affiliates, and to determine bids for search terms when the merchant uses a paid search bid manager. Information specific to a site visitor is used to predict a profitability metric for specific items that can be offered to that visitor.

Abstract: Marketing is facilitated based on a profitability prediction. A merchant optimizes offers to a website visitor based on predicted profit for potentially offered items. The profitability predication can be deployed to determine incentives to marketing affiliates, and to determine bids for search terms when the merchant uses a paid search bid manager. Information specific to a site visitor is used to predict a profitability metric for specific items that can be offered to that visitor.

Abstract: Utilization of information in a closed loop data system further augments modeling while at the same time enabling customization of offers based on spend patterns. Data mining techniques are leveraged to identify rules to determine higher response rate populations. These rules are referred to herein as “triggers,” in that the presence of particular attributes will trigger a cardholder as being more likely to respond to a particular offer. The benefit yielded by this approach is a greater acceptance rate to an offer provided by a transactional account company. To identify the triggers, records of cardmembers who already utilize a given product are analyzed to determine their spend patterns. The spend histories of customers who are eligible to use the product are analyzed according to the identified triggers. Customers whose spend patterns most closely correspond to the triggers are then targeted with offers for the given product.

Abstract: Utilization of information in a closed loop data system further augments modeling while at the same time enabling customization of offers based on spend patterns. Data mining techniques are leveraged to identify rules to determine higher response rate populations. These rules are referred to herein as “triggers,” in that the presence of particular attributes will trigger a cardholder as being more likely to respond to a particular offer. The benefit yielded by this approach is a greater acceptance rate to an offer provided by a transactional account company. To identify the triggers, records of cardmembers who already utilize a given product are analyzed to determine their spend patterns. The spend histories of customers who are eligible to use the product are analyzed according to the identified triggers. Customers whose spend patterns most closely correspond to the triggers are then targeted with offers for the given product.

Abstract: Marketing is facilitated based on a profitability prediction. A merchant optimizes offers to a website visitor based on predicted profit for potentially offered items. The profitability predication can be deployed to determine incentives to marketing affiliates, and to determine bids for search terms when the merchant uses a paid search bid manager. Information specific to a site visitor is used to predict a profitability metric for specific items that can be offered to that visitor.

Abstract: Utilization of information in a closed loop data system further augments modeling while at the same time enabling customization of offers based on spend patterns. Data mining techniques are leveraged to identify rules to determine higher response rate populations. These rules are referred to herein as “triggers,” in that the presence of particular attributes will trigger a cardholder as being more likely to respond to a particular offer. The benefit yielded by this approach is a greater acceptance rate to an offer provided by a transactional account company. To identify the triggers, records of cardmembers who already utilize a given product are analyzed to determine their spend patterns. The spend histories of customers who are eligible to use the product are analyzed according to the identified triggers. Customers whose spend patterns most closely correspond to the triggers are then targeted with offers for the given product.