An example method includes identifying, using a browser extension, content of a first webpage of a first website being displayed using a browser. The identifying of the content includes determining a product related to the content of the first webpage and determining that the product has been added to an electronic shopping cart. The method further includes determining a merchant that offers the product for sale and determining, based on a user account associated with the browser extension, a customer reward offered by the merchant and available to the user account. The method further includes determining that the browser has navigated away from the first webpage to a second webpage of a second website. The method further includes modifying the browser to display a graphical user interface element including information about the customer reward and a link configured to navigate the browser back to the first website.

The information processing server 2 communicates with the user terminal 3 of the recipient of the article delivered by the delivery machine 1, specifies content that is content according to the delivery waiting time until the delivery machine 1 arrives at the delivery destination of the article and that can be output on the user terminal 3 during the delivery waiting time, and provides the specified content to the user terminal 3 during the delivery waiting time.

The information processing server 2 communicates with the user terminal 3 of the recipient of the article delivered by the delivery machine 1, specifies content that is content according to the delivery waiting time until the delivery machine 1 arrives at the delivery destination of the article and that can be output on the user terminal 3 during the delivery waiting time, and provides the specified content to the user terminal 3 during the delivery waiting time.

This disclosure provides a loyalty program on a network-wide level. Embodiments may associate UPC and SKU data on a network level to reward consumers and/or to analyze the data for a variety of business purposes, such as market segmentation analyzes and/or analyzes relating to consumer spending behaviors or patterns, for example. In accordance with one embodiment, the network may comprise any number of participants, including consumers (such as primary and supplementary members of an aggregate consumer account), retailers (e.g. including any of their employees), manufacturers, third-party providers, and the like. In accordance with one embodiment, this disclosure enables participation by supplementary members who are associated with a primary member and, in this manner, facilitates the tracking of supplementary member purchasing behavior, reward points earning behavior, and reward points redemption behavior.

Certain aspects of the disclosure are directed toward systems methods and computer readable media containing machine readable programs thereon for managing real estate.

Apparatus and methods related providing city services, such as parking, are described. Methods and apparatus for providing parking rewards programs are described. The methods and apparatus can include maintaining accounts for participants in the rewards program, tracking purchases of parking and other participant activities and determining parking awards based upon the activities. The parking rewards programs can be used to incentivize behaviors that balance the use of the parking space commodity according to the needs of different stakeholders, such as the city, residents, merchants and visitors. For example, parking awards and the criteria used for earning awards can be used to encourage particular behaviors while a parking space is being utilized, such as shopping, the use of public transportation or efficient trip planning.

An electronic record related to a first transaction of a user can be received at a point-of-sale device. A user interface with real-time transaction data can be generated and displayed on the user device. An eligibility of the first transaction for a reward program can be determined. A portion of the reward program based on said determined eligibility can be selected and the electronic record of the first transaction can be updated by modifying the real-time transaction data currently displayed within the UI.

An electronic record related to a first transaction of a user can be received at a point-of-sale device. A user interface with real-time transaction data can be generated and displayed on the user device. An eligibility of the first transaction for a reward program can be determined. A portion of the reward program based on said determined eligibility can be selected and the electronic record of the first transaction can be updated by modifying the real-time transaction data currently displayed within the UI.

An offer decay generation model determines, for a particular customer, a personalized optimal offer decay curve of an incentive corresponding to a product provided by an enterprise, where the offer decay curve defines a set of decreasing incentive values and respective time intervals during which each incentive value is valid. The offer decay generation model is trained on historical data indicative of customers, customer interactions, offered incentives, resulting outcomes of the incentives, and time intervals elapsing between incentives and resulting outcomes. As such, the optimized offer decay curve is structured to maximize a probability that the particular customer is motivated to accept the incentive offer, purchase a product, and/or further interact with the enterprise during the lifetime of the offer decay curve. The offer decay curve may unique to the individual customer, and may be further customized based on other parameters such as location, time/day/date, inventories, etc.

An offer decay generation model determines, for a particular customer, a personalized optimal offer decay curve of an incentive corresponding to a product provided by an enterprise, where the offer decay curve defines a set of decreasing incentive values and respective time intervals during which each incentive value is valid. The offer decay generation model is trained on historical data indicative of customers, customer interactions, offered incentives, resulting outcomes of the incentives, and time intervals elapsing between incentives and resulting outcomes. As such, the optimized offer decay curve is structured to maximize a probability that the particular customer is motivated to accept the incentive offer, purchase a product, and/or further interact with the enterprise during the lifetime of the offer decay curve. The offer decay curve may unique to the individual customer, and may be further customized based on other parameters such as location, time/day/date, inventories, etc.