Abstract: A system is configured to train a customer understanding model to generate a preference score for substitution items. The customer understanding model generates a preference score for each of a plurality of related substitution items based on order data including data indicative of at least one item ordered and location data indicating a location of a first store. The customer understanding model ranks each of the substitution items based on the preference score. Order data is transmitted including substitution data identifying each of the substitution items and corresponding rank. Performance data associated with a set of operations implemented based on the order data and the substitution data is obtained. An updated customer understanding model is trained based on the performance data and iteratively modified based on the updated training dataset and updated performance metrics generated from second performance data.

Abstract: The disclosed subject matter relates to a system and method for personalizing customer experience at a retailer's physical location in order to increase sales and customer satisfaction. The personalization is based upon classification of customer's online interaction with the retailer. Upon detecting the customer's presence at the retailer's physical location, data of the customer's online interactions is retrieved and classified based on the type of online interactions and temporal characteristics. Push content is transmitted to the customer, the push content being based upon at least the classification and data associated with retailer's physical location.

Abstract: A system includes a computing device configured to obtain item attribute data that corresponds to a characteristic of an item ordered by a customer on an e-commerce platform and a common characteristic of a plurality of substitution items. The computing device is also configured to obtain customer attribute data identifying preferences of the customer and to determine a preference score for each substitution item in the plurality of substitution items based on the item attribute data and the customer attribute data. The preference score indicates a likelihood that the customer will accept one of the plurality of substitution items as a replacement for the item ordered by the customer. The computing device is also configured to rank each substitution item in the plurality of substitution items based on the preference scores.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations: training, using labeled training data and a list of substitutes for an item, a machine learning algorithm; determining, using the machine learning algorithm, as trained, a respective similarity score for each substitute of the list of substitutes; ranking each substitute of the list of substitutes based on its respective similarity score; and re-training the machine learning algorithm based on at least the labeled training data and a highest ranked substitute of the list of substitutes. Other embodiments are disclosed herein.

Abstract: A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform: determining a list of possible substitutes for an item when the item is out of stock; training a machine learning algorithm, using labeled training data as input and a list of possible substitutes for the item as output; determining, using the machine learning algorithm, as trained, a respective similarity score for each substitute of the list of possible substitutes; determining a respective historical substitution score for each possible substitute; determining a respective final score for each possible substitute comprises using at least one or more rectifiers having ReLU non-linearity; ranking each possible substitute; storing a selection of the highest ranked possible substitute as additional training data with the labeled training data; and re-training the machine learning algorithm

Abstract: A system includes a computing device configured to receive order data indicating an order placed by a customer on an e-commerce platform and route the order into a test group or a control group when the order data indicates that the order will be filled by a store participating in the comparison test. The computing device is further configured to apply test features to the order if the order was routed into the test group and apply control features to the order if the order was routed into the control group and determine recommended substitute items based on the test features or the control features. The recommended substitute items are intended to replace items ordered by the customer that are unavailable. The computing device is also configured to determine one or more performance metrics of the test group and the control group.

Abstract: In some examples, a system may be configured to, implement a first set of operations that generate a first set of data characterizing an importance of the most recently added anchor item to the user. Further, the system may be configured to, implement a second set of operations that generate a second set of data characterizing a likelihood of an occurrence of a substitution rejection event associated with the user. That way, based on the first set of data and the second set of data, the system may be configured to generate output data, and implement a set of notification operations based on the output data.

Abstract: The disclosed subject matter relates to a system and method for personalizing customer experience at a retailer’s physical location in order to increase sales and customer satisfaction. The personalization is based upon classification of customer’s online interaction with the retailer. Upon detecting the customer’s presence at the retailer’s physical location, data of the customer’s online interactions is retrieved and classified based on the type of online interactions and temporal characteristics. Push content is transmitted to the customer, the push content being based upon at least the classification and data associated with retailer’s physical location.

Abstract: A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform: determining a list of possible substitutes for an item when the item is out of stock; training, a machine learning algorithm, using labeled training data as input and a list of possible substitutes for the item as output; determining, using the machine learning algorithm, as trained, a respective similarity score for each substitute of the list of possible substitutes; determining a respective historical substitution score for each possible substitute; determining a respective final score for each possible substitute comprises using at least one or more rectifiers having ReLU non-linearity; ranking each possible substitute; storing a selection of the highest ranked possible substitute as additional training data with the labeled training data; and re-training the machine learning algorith

Abstract: A system includes a computing device configured to receive order data indicating an order placed by a customer on an e-commerce platform and route the order into a test group or a control group when the order data indicates that the order will be filled by a store participating in the comparison test. The computing device is further configured to apply test features to the order if the order was routed into the test group and apply control features to the order if the order was routed into the control group and determine recommended substitute items based on the test features or the control features. The recommended substitute items are intended to replace items ordered by the customer that are unavailable. The computing device is also configured to determine one or more performance metrics of the test group and the control group.

Abstract: The disclosed subject matter relates to a system and method for personalizing customer experience at a retailer's physical location in order to increase sales and customer satisfaction. The personalization is based upon classification of customer's online interaction with the retailer. Upon detecting the customer's presence at the retailer's physical location, data of the customer's online interactions is retrieved and classified based on the type of online interactions and temporal characteristics. Push content is transmitted to the customer, the push content being based upon at least the classification and data associated with retailer's physical location.

Abstract: A system includes a computing device configured to receive order data indicating an order placed by a customer on an e-commerce platform and route the order into a test group or a control group when the order data indicates that the order will be filled by a store participating in the comparison test. The computing device is further configured to apply test features to the order if the order was routed into the test group and apply control features to the order if the order was routed into the control group and determine recommended substitute items based on the test features or the control features. The recommended substitute items are intended to replace items ordered by the customer that are unavailable. The computing device is also configured to determine one or more performance metrics of the test group and the control group.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform: determining a list of substitutes for an item; determining qualities for each substitute; determining a similarity score for each substitute; determining a historical substitution score for each substitute; determining a final score for each substitute using the similarity score for each substitute and the historical substitution score for each substitute; ranking each substitute based upon the final score for each substitute; facilitating a display, on a user interface of a user device, of a highest ranked substitute; receiving, from the user interface of the user device, a selection of the highest ranked substitute; and after receiving the selection of the highest ranked substitute, substituting the highest ranked substitute for the item of the list of items. Other embodiments are disclosed herein.

Abstract: A system includes a computing device configured to obtain item attribute data that corresponds to a characteristic of an item ordered by a customer on an e-commerce platform and a common characteristic of a plurality of substitution items. The computing device is also configured to obtain customer attribute data identifying preferences of the customer and to determine a preference score for each substitution item in the plurality of substitution items based on the item attribute data and the customer attribute data. The preference score indicates a likelihood that the customer will accept one of the plurality of substitution items as a replacement for the item ordered by the customer. The computing device is also configured to rank each substitution item in the plurality of substitution items based on the preference scores.

Abstract: A system includes a computing device configured to receive order data indicating an order placed by a customer on an e-commerce platform and route the order into a test group or a control group when the order data indicates that the order will be filled by a store participating in the comparison test. The computing device is further configured to apply test features to the order if the order was routed into the test group and apply control features to the order if the order was routed into the control group and determine recommended substitute items based on the test features or the control features. The recommended substitute items are intended to replace items ordered by the customer that are unavailable. The computing device is also configured to determine one or more performance metrics of the test group and the control group.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of: determining a list of suitable substitutes for the item of the list of items, wherein the list of suitable substitutes can comprise a set of items in an item catalogue; determining qualities for each suitable substitute of the list of suitable substitutes; determining a similarity score for each suitable substitute of the list of suitable substitutes; determining a historical substitution score for each suitable substitute of the list of suitable substitutes; determining a final score for each suitable substitute of the list of suitable substitutes using the similarity score for each suitable substitute of the list of suitable substitutes and the historical substitution score for each suitable substitute of the list of suitable substitutes; ranking each suitable substitute of the list of suitable substitutes base