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 functions comprising: generating one or more feature vectors for a user, the one or more feature vectors at least comprising transaction-based features and slot-based features; generating, using a machine learning architecture, a repurchase prediction for the user based, at least in part, on the one or more feature vectors; generating, using the machine learning architecture, a time slot prediction for the user based, at least in part, on the one or more feature vectors, the time slot prediction predicting a time slot desired by the user for an upcoming transaction; and executing a reservation function that facilitates reserving of the time slot for the user. Other embodiments are disclosed herein.

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: receiving in-session user activity entered into on an initial graphical user interface (GUI) from a user electronic device of a user; pre-processing the in-session user activity to determine one or more intents of the in-session user activity; comparing the one or more intents of the in-session user activity with one or more complementary intents; and coordinating displaying a complimentary GUI on the user device of the user based on the one or more complementary intents. Other embodiments are disclosed herein.

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: generating a feature vector for a user based, at least in part, on historical data pertaining to the user's previous transactions; generating, using a quantity prediction model of a machine learning architecture, a respective item quantity prediction for each of one or more items included in a predicted basket based, at least in part, on the feature vector for the user; and populating a respective quantity selection option for each of the one or more items included in the predicted basket based on the respective item quantity prediction generated for each of the one or more items. Other embodiments are disclosed herein.

Abstract: A method including tracking impression response data in response to online impressions of content elements displayed to users of a website. The impression response data can include (i) first responses by the users in one or more physical stores in response to the online impressions, and (ii) second responses by the users in the website in response to the online impressions. The method also can include receiving a request from a user of the users to display a webpage of the website. The method additionally can include generating the webpage to include a content element selected from among the content elements based on a classification of the user and the impression response data for the content elements. Other embodiments of related systems and methods are described.

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: utilizing historical transaction information to derive metric information associated with prior transactions; generating a listing of user-item pairs, each of the user-item pairs identifying a user and an item; executing a machine learning model that is configured to generate a transmission list for sending push notifications; generating a transmission list by selecting user-item pairs based on the conversion probability values and the confidence indicators that are assigned to the user-item pairs; customizing content for the push notifications to include information for items identified by the user-item pairs included in the transmission list; and transmitting the push notifications to the users identified by the user-item pairs included in the transmission list.

Abstract: A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform: determining a set of items to recommend to a user based on a probability exceeding a predetermined threshold that the user will re-order each item of the set of items at a present time, wherein the probability is determined based at least in part on previous transactions of the user and other users within a first period of time, and wherein the set of items includes at least a predetermined number of items; sending instructions to display the set of items to the user on a user interface, wherein at least a portion of the set of items is shown as selected on the user interface, and the user interface further comprises a single-click option to add to a cart all selected items of the set of items; receiving a selection of the single-click option to add to the cart the all selected items of the set of items; and after receivin

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, perform certain acts. The acts can include obtaining reorder likelihood scores for items that a user has ordered historically. The acts also can include grouping the items into groups using a taxonomy. The acts additionally can include adjusting the groups based on a respective number of items in each of the groups and a respective group-specific threshold for each of the groups. The acts further can include ranking the items within the groups based on the reorder likelihood scores. Other embodiments are described.

Abstract: A method including tracking usage data for users using a first channel and a second channel. The method also can include performing a classification of first users of the users into a first group and second users of the users into a second group. The classification can be based on the usage data. The method additionally can include, for each impression of a content element of content elements being displayed on a website to a user of the users, tracking impression response data including (a) whether the user is grouped into the first group or the second group, and (b) response data including: (i) a first response by the user to the content element in the first channel, and (ii) a second response by the user to the content element in the second channel. The method further can include receiving a request from a first user of the users to display a webpage of the website. The method additionally can include generating the webpage to include a selected content element from among the content elements.

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: utilizing historical transaction information to derive metric information associated with prior transactions; generating a listing of user-item pairs, each of the user-item pairs identifying a user and an item; executing a machine learning model that is configured to generate a transmission list for sending push notifications; generating a transmission list by selecting user-item pairs based on the conversion probability values and the confidence indicators that are assigned to the user-item pairs; customizing content for the push notifications to include information for items identified by the user-item pairs included in the transmission list; and transmitting the push notifications to the users identified by the user-item pairs included in the transmission list.

Abstract: A method including tracking usage data for users using a first channel and a second channel. The method also can include performing a classification of first users of the users into a first group and second users of the users into a second group. The classification can be based on the usage data. The method additionally can include, for each impression of a content element of content elements being displayed on a website to a user of the users, tracking impression response data including (a) whether the user is grouped into the first group or the second group, and (b) response data including: (i) a first response by the user to the content element in the first channel, and (ii) a second response by the user to the content element in the second channel. The method further can include receiving a request from a first user of the users to display a webpage of the website. The method additionally can include generating the webpage to include a selected content element from among the content elements.