A system for experimentation of e-commerce pricing distribution based on time-interleaving includes one or more processors configured to identifying a first set of items from a plurality of items based on one or more item pricing policies. Scheduling the first set of items to be published on a website during a first time slot from a plurality of time slots by applying a scheduling algorithm. Identifying a first subset of substitutable items and a second subset of substitutable items from the first set of items based on one or more item substitution rules. Assigning the first subset of substitutable items with a first pricing policy and the second subset of substitutable items with a second pricing policy. Publishing the first subset of substitutable items and the second subset of substitutable items. Determining pricing distribution data based on monitoring customer interaction.

A system and methods to mutually satisfy a consumer with a discount and a vendor with a minimum number of sales by establishing a tipping point associated with an offer for a good or service. If the tipping point is met, the sale of the good or service is executed and the consumer is charged and receives an indication of the discounted sale, such as a certificate. If the tipping point is not met, the discount offer is abandoned and the consumer is not charged. Once the tipping point is established, the vendor receives a payment, even before the consumer uses the certificate.

A method, apparatus and computer program product are provided for providing auto-replenishment of an inventory of promotions. Auto-replenishment amounts and dates may be determined based on a variety of factors including current inventory, outstanding promotions, redemption ratios and redemption rates. Redemption ratios and redemption rates may be characterized based on provider information, consumer information, and/or promotion information. Auto-replenishment may result in a smoothed redemption curve and optimal impact to the provider.

The method and system are directed at providing group purchasing at a historical price in an e-commerce environment. A user may select a price that a price at which a produce previous sold, seen as the historical price. This price may then be used to generate an online purchasing campaign that other user may join. The purchasing campaign is user or consumer driver rather than merchant driven.

Methods and apparatus are disclosed for the maintenance of a virtual credit card pool for airline passenger vouchers. An example system includes server(s) that are configured to determine a target distribution of virtual credit cards within the virtual card pool for a current date-and-time. The server(s) are configured to, in response to determining that the current date-and-time corresponds with a predefined restocking time, for each card value: identify a current number of virtual credit cards within the virtual card pool; identify a threshold number of virtual credit cards based on the target distribution; compare the current and threshold numbers; in response to determining that the current number is less than the threshold number, transmit a request for virtual credit cards having the card value to an external server; and add the requested virtual credit cards to the virtual card pool upon receipt.

A system for employing machine learning to generate customized notifications for a user is provided. The system may identify a landmark in proximity to a user's mobile device and may obtain information associated with products or services offered by the landmark. While the mobile device is within a predetermined distance of the landmark, a machine learning model may be employed to generate a customized notification, such as an offer—e.g., a discount or a special financing offer for one of the identified products—generated specifically for use by the user based on the user's spending and/or financing history. The customized notification may be transmitted to the mobile device with a security key for accessing the offer. In response to receiving an indication that the security key was selected and the product purchased, the user may be authenticated and the offer may be applied to an account associated with the user.

Techniques and arrangements for performing data analysis in order to generate connections between merchants. For instance, a payment service may determine, based at least in part on transaction information, that a first customer conducted a first transaction at a first merchant followed a subsequent transaction at a second merchant. The payment service may further determine that a second customer conducted a second transaction at the first merchant followed by a subsequent transaction at a third merchant, Based on transaction information associated with the first transaction and the second transaction, the payment service may create a buyer profile including the first customer and second customer. Upon the payment service receiving a request to process a third transaction between the first merchant and the second customer, the payment service can generate a recommendation that the second customer conduct a subsequent transaction to the third transaction at the second merchant rather than the third merchant. The payment service can then send a electronic communication that includes the recommendation to the first merchant or the second customer.

This application relates to apparatus and methods for automatically determining and providing prices for items for sale in stores or online such as to clear inventor of the items. In some examples, a computing device employs artificial intelligence, such as machine learning models, to determine the pricing of the items. For example, the computing device may employ a forecasting model that determines a forecasted demand for an item based on store inventory and experimental sales of the item. The computing device may also employ an item pricing optimization model that determines a clearance price for the item based at least on the forecasted demand as well as historical data indicating previous price adjustments and sales of the item. In some examples, the item pricing optimization model determines a time period to apply the clearance price to the item. The item may then be placed on sale for the clearance price.

With an offer server computer system: receiving a first digital image file, a first mapping of product codes to audience segment identifiers, and a temporary price reduction (TPR) offer dataset; mapping a target identifier for an end user device of a consumer to an audience segment identifier; in response to determining, based on the audience segment identifier, that the TPR offer dataset has a product code and a retailer identifier that map to the audience segment identifier, and an effective date value that includes a current date value, and the retailer identifier corresponds to a retailer location within a specified distance of a then-current location of the end user computing device: creating and storing a digital offer dataset comprising both the first digital image file and a second digital image file that presents data elements of the TPR offer dataset; causing transmission of the dataset to the end user device.

A system is provided that generates values associated with a promotion impact measure for each promotional campaign based on historical data and an expected audience. A number of inventory units is determined for each promotional campaign that corresponds to a promotion inventory utilization type, based on a difference in estimated demand value for the inventory units for a specified duration for a scatter inventory utilization type and current value of actual demand units for the specified duration and a gross sum of the values for defined number of weeks of each promotional campaign and a plurality of constraints. Inventory units are allocated among each inventory utilization type based on number of inventory units for each promotional campaign to meet defined parameters for the defined amount of inventory units for specified durations until the end of the specified upcoming time-frame. Content is distributed via a channel based on allocated inventory units.