Aspects include synchronizing online and retail shopping experiences for limited stock products. A non-limiting example computer-implemented method includes receiving, by an item tracking system, a request for a status of an item in a retail store, the request received from a user device of an in-person shopper located in the retail store, the status indicating whether the item is currently available for purchase by the in-person shopper. It is determined, by the item tracking system, the status of the item, the determining is based at least in part on whether an action was taken by an online shopper with respect to the item via a user device of the online shopper. The user device of the online shopper is located outside of the retail store. The item tracker system transmits the status of the item to the user device of the in-person shopper for output to the in-person shopper.

In systems and methods for multiple level bot detection in e-commerce platforms during flash sale events conducted by merchants having accounts with e-commerce platform, a computer applies a first bot detection algorithm to web traffic of a webpage hosting the online store that is conducting the online sales event. The computer determines whether an actor device is executing a bot to make purchases based on a first bot detection algorithm. When the computer identifies a type of triggering instruction, such as a predetermined time period, a user instruction, or a data condition, the computer then applies a second bot detection algorithm to the web traffic. The bot detection algorithms determine signal scores for the customer devices that originated the web traffic. If the signal scores for a customer device satisfy a detection threshold, the server determines the device is operated by a bot actor, rather than a human actor.

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.

Methods, systems, and computer-readable media are provided for providing information on electronic visual displays in retail stores. In one implementation, a door for a retail storage container may include at least a first part that is configured to face customers when the door is closed and a second part configured to face the internal side of the retail storage container when the door is closed. Further, the second part may include at least an electronic visual display configured to display information, and at least part of the electronic visual display may be configured to be visible to the customers at least when the door is open at a selected angle.

Certain aspects involve systems and methods for providing display content using RFID tags in geo-fenced zones. One example method includes processing devices that perform operations that include scanning radio-frequency identification (RFID) tags in a geo-fenced zone. The RFID tags correspond to a plurality of items. Further, the operations include retrieving a marketing campaign associated with the plurality of items. The marketing campaign comprises rules based on inventory information. In addition, the operations include determining the inventory information for the plurality of items based on the RFID tags. The operations further include selecting an item from among the plurality of items based on a rule associated with the marketing campaign. The rule is based on the inventory information. Additionally, the operations include generating display content based on the item.

During online shopping, customer buying decision varies based on conditions at the time of logging such as product availability, competitor price of the product, presence of promotion, delivery options such as number of days to deliver, availability of free delivery, and availability of pay on delivery and customer review ratings. Customer shifts from one product to other product based on the options available at real time and accordingly demand of a product is transferred to other product. The method and system disclosed provides dynamic demand transfer values that are specific to a customer for available options at the time of login and it provides the list of ideal products to be displayed at the time of customer login. The method utilizes a suitable data format to apply machine learning based approach for estimating DT, wherein training data for ML captures plurality of sales drivers affecting customer decision during online retailing.

A computer-implemented system for optimizing cost of goods sold is configured to: receive supplier configuration data of a supplier associated with the at least one product, supplier configuration data being extracted from an agreement defining parameters associated with one or more tiers; receive an order history associated with the supplier; determine a current tier and current progress within the current tier based on the order history, the current tier being specified by the supplier configuration data; determine an additional quantity necessary to reach a next tier according to the supplier configuration data; determine one or more trade-off parameters affected by the additional quantity, the one or more trade-off parameters being determined by a computerized model simulating future customer demand; and transmit a request to initiate a new order for the additional quantity based on the one or more trade-off parameters.

Apparatus and associated methods relate to offering a discounted product requested by a buyer, with offer parameters determined as a function of the discounted product request, in response to detecting a predetermined product demand characteristic, and at a location, time, and discount based on the offer. In an illustrative example, offer parameters may include quantity, location, or time. The product request may be, for example, a request for a discounted product quantity at a requested location and time. In some examples, the product demand characteristic may be demand as a function of time. The discounted product may be provided at a time and location based on a discount code generated for the product. Various examples may advantageously increase revenue limited by off-peak hour and end of day demand characteristics, for example, offering products discounted in response to demand inadequate to consume time-valued inventory and/or to better utilize operation capacity, so that the seller may increase profit.

The present invention relates to a system (100) and method for inventory management of near expiry perishable goods such as food stuffs, pharmaceuticals, cosmetics and chemicals. The method consists in providing an electronic database (102) managed by a computer platform for near expiry perishable goods. The platform allows sellers of near expiry items to advertise their merchandise and allows customers to buy the near expiry items at discounted prices.

The present disclosure relates to methods, systems, and apparatuses for identifying related records in a database. The method includes receiving, via a network interface, a related records query, the related records query identifying at least one record stored in the electronic database, determining, based on transaction data, at least one related record that is related to the identified at least one record, determining, by a processor, that the at least one related record is unavailable, in response to determining that the at least one related record is unavailable, determining at least one keyword associated with the at least one related record, selecting at least one of one or more substitute records based at least in part on comparing the at least one keyword with a set of keywords associated with one or more substitute records, and providing the selected one or more substitute records as a response to the related records query.