Abstract: A computer-implemented method for providing a list of frequently purchased items by a user from a retailer to a computing device corresponding to the user includes receiving a request to generate the list of frequently purchased items. A purchase history of the user is obtained in response to the request, the purchase history including a plurality of previously purchased items. A number of purchases for each of the purchased items within a predetermined time period is monitored. The list of frequently purchased items is generated based on the monitored number of purchases for each of the purchased items. The generated list of frequently purchased items is transmitted to the computing device.

Abstract: Systems, methods, and computer-readable storage media for improved inventory management of returned merchandise. Returned items are labeled using a smart label, which allows for easy tracking of the physical location of the returned items, as well as the reasons for the product being returned. When the items are scanned using the smart labels, updates to the store inventory and the global product inventory can occur, thereby allowing the returned item to be used in both local and online sales.

Abstract: Described is a shopping cart basket for affixing to a shopping cart. The shopping cart basket can be used to carry tools, accessories, or other items that an individual wants to carry with, but keep separate from, items in the shopping cart. The shopping cart basket includes a basket body with a pair of hooks on arms that hook the shopping cart basket to the shopping cart. The shopping cart basket can be hung from many different locations on a shopping cart, including the front or back, the sides, the inside, or the outside. The shopping cart basket can be hung from any type of shopping cart, including basket types of shopping carts or flatbed carts. The shopping cart basket includes elements that hold and secure tools commonly used by employees performing tasks with a shopping cart.

Abstract: Described is a shopping cart bagging station. The shopping cart bagging station is a bagging station that couples to a shopping cart. The shopping cart bagging station described is used by customers to bag their purchases as they shop, or by employees bagging items for a customer. The shopping cart bagging station is designed to be heavy-duty so that it can be used in pick-up areas and other areas that require a bagging station that can withstand temperatures and handle heavy loads. The shopping cart bagging station includes a support structure and three shopping cart coupling hooks. The shopping cart coupling hooks couple the bagging station to a shopping cart, and they hold and dispense the shopping bags. The support structure and the three shopping cart coupling hooks are formed of metal rod that can withstand extremes in temperature and hold heavy loads.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of accessing an online catalog for an online retailer comprising a plurality of digital images of a plurality of items for sale by the online retailer, training a two-branch a Siamese convolutional neural network (CNN) model to determine a similarity between two digital images of the plurality of digital images, receiving one or more digital images of a new item for the online catalog, determining, using the two-branch Siamese CNN model and the one or more digital images of the new item, a similar item of the plurality of items to which the new item is most similar, and coordinating a display of the new item on a webpage based on a ranking of the similar item.

Abstract: A method including sending to a first user an input form comprising an input element for an intent weight for each of a plurality of features. The plurality of features can represent purchasing criteria that are common to each item in a category of items. The method also can include receiving from the first user the intent weights for the plurality of features. Each of the intent weights can represent a level of importance of a different feature of the plurality of features to the first user. The method additionally can include selecting one or more first items from among a plurality of items in the category of items based at least in part on: (a) the intent weights for the plurality of features for the first user, and (b) sentiment data comprising a sentiment score for each feature for each of the plurality of items. The sentiment scores for the plurality of features for each of the plurality of items can be derived from user-generated post-purchase content about the plurality of items.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of determining a lifetime value (LTV) for customers of a retailer, segmenting the customers into customer states based upon one or more purchases made by each customer at the retailer within a predetermined period of time, determining a first average LTV for customers in a first customer state and a second average LTV for customers in a second customer state lower than the first average LTV, coordinating a first display of a first online advertisement for customers in the first LTV to transition the customers from the first customer state to the second customer state, and coordinating a second display of a second online advertisement for customers in the second state.

Abstract: In many embodiments, a method can comprise receiving an online order for an item from a user and receiving an estimate of a network stockout cost of a network associated with the item, the network comprising at least a first fulfillment center and a second fulfillment center. In some embodiments, the method further can comprise updating a first local stockout cost and updating a second local stockout. In various embodiments, the method further can comprise executing a first stocking plan for the item at the first fulfillment center based at least in part on the updated first local stockout cost, executing a second stocking plan for the item at the second fulfillment center based at least in part on the updated second local stockout cost, and selecting the first fulfillment center to deliver the item pursuant to the online order from the user based at least in part on the first stocking plan. Other embodiments of related methods and systems are also provided.

Abstract: A method including generating a weighting vector for a first user. The weighting vector can track a weight corresponding to each feature of a plurality of features. The plurality of features can represent purchasing criteria that are common to each item in a category of items. The method also can include, in response to receiving a request from the first user to view details for a selected item, recommending to the first user, in real-time after receiving the request, one or more other items that are different from the selected item. The selected item and the one or more other items can be in a category of items that is the same. The plurality of features can be in common for each item in the category of items. Sentiment data can include a sentiment score for each feature for each item in the category of items.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of: categorizing a plurality of customers based on whether or not each customer of the plurality of customers had (1) an online store transaction with a retailer, (2) an offline store transaction with the retailer, or (3) an online interaction with the retailer within a predetermined period of time; predicting, using a lifetime value (LTV) update model, a LTV for customers who did have an interaction or transaction with the retailer; predicting, using one or more LTV decay functions, the LTV for customers who did not have an interaction or transaction with the retailer; and determining an online advertisement for each customer based on the LTV for each customer.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of receiving, from a plurality of entities, objectives and budgets associated with a plurality of keywords, determining cost-per-click (CPC) bids for each of the entities for some of the plurality of keywords using the objectives and the budgets, determining internal bids by the plurality of entities for the plurality of keywords by multiplying the CPC bid and a pacing multiplier, performing one or more iterative internal auctions, determining a highest bidder for each keyword of the plurality of keywords, and coordinating bidding by only the highest bidding entity for each of the keywords on a third-party external auction.

Abstract: In many embodiments, the method can comprise receiving a search query from a search by a user and determining a question to present to the user. In many embodiments, determining the question to present to the user can comprise evaluating a user profile associated with the user, evaluating the search query, evaluating one or more user actions during a current browse session of the user, and selecting the question from a set of questions. In some embodiments, the method can further comprise presenting the question to the user when a confidence score associated with the question reaches or exceeds a predetermined threshold. Other embodiments of related methods and systems are also provided.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of accessing purchase information history from a website of an online retailer, training a first machine learning model configured to obtain first population demographic data for first products, coordinating a display of an advertisement associated with the first products on social media webpages of new users corresponding to at least a portion of the first population demographic data, receiving an indication that some of the new users selected the first advertisement, accessing additional information of the new users, training a second machine learning model configured to obtain additional data for the one or more first products, and coordinating a display of the first advertisement associated with the first products on additional social media webpages of additional new users.

Abstract: A method including selectively aggregating a first set of filters for an item results list. The first set of filters can include multiple filter types, including category filters, facet filters, and price filters. The method also can include, for each filter in the first set of filters and each item in the item results list, determining user engagement statistics for the item when the filter has been applied. The method additionally can include generating a filter score for each filter in the first set of filters. The method further can include selecting a second set of filters from the first set of filters based on the filter scores of the filters in the second set of filters being above a threshold filter score. The method additionally can include applying space-constraint rules to the second set of filters to limit a quantity of filters in the second set of filters based on a ranking of the filters in the second set of filters and based on a screen size of a user device.

Abstract: In many embodiments, the method can comprise storing one or more user actions of a user of one or more users in a database, sorting the one or more user actions into one or more user action types, and extracting one or more correlated signals related to the one or more user actions of the user of the one or more users based at least in part on the one or more user action types to determine one or more independent signals related to the one or more user actions of the user of the one or more users. In some embodiments, the method can further comprise averaging the one or more independent signals related to the one or more user actions of the user of the one or more users to determine a personalization score related to the user of the one or more users and updating a webpage for the user of the one or more users based at least in part on the personalization score. Other embodiments of related methods and systems are also provided.

Abstract: In some embodiments, a method can comprise receiving a search query from a search by a user during a browse session, receiving one or more items from an item database in response to the search query, and receiving one or more previous search queries from a search database, the one or more previous search queries related to the search query. In many embodiments, the method can further comprise determining a purchase probability associated with a first item of the one or more items based at least in part on a first item score for the first item, ranking the one or more items based at least in part on the purchase probability associated with the first item of the one or more items, and facilitating display of the ranking of the one or more items. Other embodiments of related methods and systems are also provided.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of training one or more first models to recommend a first item after a user has had an interaction on the web site of the online retailer with a second item, determining static features common to both the first item and the second item, training a second model to determine whether to coordinate a display of any new item as one of one or more recommended items with any of a plurality of items, and coordinating the display of the new item as one of the one or more recommended items when the one or more of the plurality of items are displayed on the website of the online retailer based on the static features of the new item.

Abstract: In some embodiments, a method can comprise receiving a search query from a search by a user during a browse session, receiving one or more items from an item database in response to the search query, and receiving one or more previous search queries from a search database, the one or more previous search queries related to the search query. In many embodiments, the method can further comprise determining a purchase probability associated with a first item of the one or more items based at least in part on a first item score for the first item, ranking the one or more items based at least in part on the purchase probability associated with the first item of the one or more items, and facilitating display of the ranking of the one or more items. Other embodiments of related methods and systems are also provided.

Abstract: In some embodiments, the method can comprise receiving one or more clicks on one or more items by a user during a browse session, measuring a distance between the one or more items, and determining a relationship between the one or more items based at least in part on the distance. In various embodiments, the method can further comprise clustering the one or more items based at least in part on the relationship into one or more clusters and presenting to the user a recommendation. In a number of embodiments, the recommendation can comprise at least one of one or more search terms related to at least one cluster of the one or more clusters or a set of items related to the at least one cluster of the one or more clusters, the set of items comprising at least a portion of the one or more items. Other embodiments of related methods and systems are also provided.

Abstract: A method including receiving a weighting vector for each of a plurality of users, the weighting vector tracking a weight corresponding to each feature of a plurality of features. The plurality of features can represent purchasing criteria that are common to each item in a category of items. Each of the weights in the weighting vector for each user of the plurality of users can represent a level of importance of a different feature of the plurality of features to the user. The method also can include applying categorization rules on the weighting vectors for the plurality of users to categorize the plurality of users into a plurality of subgroups. The method additionally can include generating a profile weighting vector for each subgroup of the plurality of subgroups.