Abstract: System and methods for controlling customer retention are disclosed. In some embodiments, a disclosed method includes: storing historical customer data and order data associated with a customer of a retailer within a database, receiving the order data within a time period, the order data including a plurality of orders, parsing the order data to determine an order status for each of the plurality of orders, identifying a negative order from the order data based on the order status, the negative order associated with the customer, generating, in real-time, appeasement data for the negative order, the appeasement data being associated based on the historical customer data, and transmitting, in real-time, the appeasement data to an electronic device having a user interface for display of an appeasement offer associated with the appeasement data to the customer.

Abstract: Example implementations relate to generating textual user interface elements. A request to generate a textual user interface element for a selected interface element package is received. The request includes a plurality of targeted characteristics of the textual user interface element. In response to receiving the request, an input prompt based on first information derived from the plurality of targeted characteristics is generated. The initial textual user interface element based on first criteria is processed to generate a processed textual user interface element. At least the first criteria or the first information based on feedback information of the processed textual user interface element is modified. The textual user interface element based on the modified first criteria and/or the modified first information is generated.

Abstract: Systems and methods of generating personalized interfaces are disclosed. An interface generation request is received and, in response to receiving the interface generation request, a plurality of user-relevant element bundles is selected. The plurality of user-relevant bundles is selected in part by a weighted random sampling process. In response to selecting the plurality of user-relevant element bundles, a set of interface elements associated with each of the plurality of user-relevant element bundles is ranked based on interface element relevance to a user associated with the interface generation request. Instructions to cause generation of an interface including at least one of the plurality of user-relevant element bundles and at least a top-ranked interface element selected from the set of interface elements associated with the at least one of the plurality of user-relevant element bundles are transmitted to a user device.

Abstract: Systems and methods of user segmentation and customized interface generation are disclosed. A user data structure including a set of user features is obtained. A segment probability for each of a plurality of segments is generated by a deep neural net classifier model that receives a first subset of the user features. A classification probability of the user data structure is generated for each of the plurality of segments by a data segmentation model configured to receive the segment probability from the deep neural net classifier model and a second subset of the user features. A user-application-segment score is generated based on a weighted combination of the segment probability and the classification probability for the at least one of the plurality of segments. The weighted combination is applied by a combinatorial model. The user data structure is modified to include an application-specific label based on the user-application-segment score.

Abstract: A system and method for recommending products based on characteristics of a customer's household. The system and method associates age dependent products with developmental stages on a universal developmental scale and determines a subset of age dependent products based on prior engagements by the customer's household. Using the development stages associated with the subset of age dependent products characteristics of the customer's household may determine specifically the number and ages of juveniles in the customer's household. Performing Gaussian mixture model or multivariate kernel density estimation on the developmental stages associated with the engagements of customer's household, the age(s) and number of juveniles respectively may be determined and recommendations of products and services to the customer or customer's household based upon these characteristics may be advantageously made.

Abstract: This application relates to apparatus and methods for providing recommended items to advertise. In some examples, a computing device determines a first set of items for recommendation based on historical user data associated with a user, and a second set of items for recommendation based on real-time user session data for the user. The computing device may then determine a subset of the first set of items based on associated scores and a predetermined threshold number of first items that can be presented for optimal user interaction. The computing device may generate a set of item recommendations by combining the subset of the first set of items and at least one of the second set of items to present to the user as advertisements.

Abstract: Systems and methods utilizing a classification model and a ranking model are disclosed. A user identifier is received and a user profile is generated. The user profile includes a plurality of user features. The user profile is classified into a classification using a trained classification model. The trained classification model receives a first subset of the plurality of user features. A set of communication elements is ranked using a trained ranking model. The trained ranking model receives a second subset of the plurality of user features. An electronic communication including a plurality of interface elements is generated. The plurality of interface elements includes at least one communication element selected from the ranked set of communication elements in descending ranked order. A type of the electronic communication is selected based on the classification of the user profile.

Abstract: A method being implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media. The method can include training two sets of item embeddings for items in an item catalog and a set of user embeddings for users, using a triple embeddings model, with triplets. The triplets each include a respective first user of the users, a respective first item from the item catalog, and a respective second item from the item catalog, in which the respective first user selected the respective first item and the respective second item in a respective same basket. The method also can include randomly sampling an anchor item from a category of items selected by a user. The method additionally can include generating a list of complementary items using a query vector associated with the user and the anchor item.

Abstract: This application relates to apparatus and methods for automatically determining and providing personalized user personas of a customer for specific platforms (e.g., applications). In some examples, a computing device receives a persona request identifying a user and a platform. In response, the computing device obtains user data associated with the user and a plurality of potential user personas from a database. For each of the plurality of potential user personas, the computing device then determines a combination score for the user based on the user data. The combination score indicates user's affinity to a corresponding potential user persona within the platform. The computing device selects at least one potential user persona of the plurality of potential user personas as a final user persona for the user and the platform based on the corresponding combination score.

Abstract: This application relates to apparatus and methods for automatically determining and providing digital advertisements to targeted users. In some examples, a computing device receives campaign data identifying items to advertise on a website, and generates campaign user data identifying a user that has engaged all of the items on the website. The computing device may then determine a portion of the users based on a relationship between each user and the campaign user data, and may determine user-item values for each of the items for each user of the portion of users, where each user-item value identifies a relational value between the corresponding user and item. The computing device may then identify one or more of the items to advertise to each user of the portion of users based on the user-item values, and may transmit to a web server an indication of the items to advertise for each user.

Abstract: This application relates to apparatus and methods for automatically determining and providing personalized user personas of a customer for specific platforms (e.g., applications). In some examples, a computing device receives a persona request identifying a user and a platform. In response, the computing device obtains user data associated with the user and a plurality of potential user personas from a database. For each of the plurality of potential user personas, the computing device then determines a combination score for the user based on the user data. The combination score indicates user's affinity to a corresponding potential user persona within the platform. The computing device selects at least one potential user persona of the plurality of potential user personas as a final user persona for the user and the platform based on the corresponding combination score.

Abstract: A system and method for recommending products based on characteristics of a customer's household. The system and method associates age dependent products with developmental stages on a universal developmental scale and determines a subset of age dependent products based on prior engagements by the customer's household. Using the development stages associated with the subset of age dependent products characteristics of the customer's household may determine specifically the number and ages of juveniles in the customer's household. Performing Gaussian mixture model or multivariate kernel density estimation on the developmental stages associated with the engagements of customer's household, the age(s) and number of juveniles respectively may be determined and recommendations of products and services to the customer or customer's household based upon these characteristics may be advantageously made.

Abstract: A system and method for recommending products based on characteristics of a customer's household. The system and method associates age dependent products with developmental stages on a universal developmental scale and determines a subset of age dependent products based on prior engagements by the customer's household. Using the development stages associated with the subset of age dependent products characteristics of the customer's household may determine specifically the number and ages of juveniles in the customer's household. Performing Gaussian mixture model or multivariate kernel density estimation on the developmental stages associated with the engagements of customer's household, the age(s) and number of juveniles respectively may be determined and recommendations of products and services to the customer or customer's household based upon these characteristics may be advantageously made.

Abstract: In some examples, a system may be configured to obtain a set of features of a set of users including one or more features of transaction data of the set of users and one or more features of engagement data of the set of users. Additionally, the system may be configured to implement a first set of operations that generate output data including a plurality of conversion scores, based on the set of features. In some examples, each conversion score of the plurality of conversion scores are associated with a particular user of the set of users and characterize a likelihood of a conversion event of the corresponding user changing from a trial-member status to a full-member status prior to a predetermined future time.

Abstract: This application relates to apparatus and methods for automatically determining and providing digital advertisements to targeted users. In some examples, a computing device receives campaign data identifying items to advertise on a website, and generates campaign user data identifying a user that has engaged all of the items on the website. The computing device may then determine a portion of the users based on a relationship between each user and the campaign user data, and may determine user-item values for each of the items for each user of the portion of users, where each user-item value identifies a relational value between the corresponding user and item. The computing device may then identify one or more of the items to advertise to each user of the portion of users based on the user-item values, and may transmit to a web server an indication of the items to advertise for each user.

Abstract: This application relates to apparatus and methods for providing recommended items to advertise. In some examples, a computing device determines a first set of items for recommendation based on historical user data associated with a user, and a second set of items for recommendation based on real-time user session data for the user. The computing device may then determine a subset of the first set of items based on associated scores and a predetermined threshold number of first items that can be presented for optimal user interaction. The computing device may generate a set of item recommendations by combining the subset of the first set of items and at least one of the second set of items to present to the user as advertisements.

Abstract: This application relates to apparatus and methods for automatically determining and providing personalized digital recommendations including sponsored items. In some examples, a computing device receives a recommendation request. In response, the computing device determines an initial set of items for recommendation based on a relevance of associated items to the user and potential revenue from user interactions with the associated items. The computing device then generates final item recommendations by replacing at least one item of the initial set of items with a closest sponsored item that is selected based on a similarity of the closest sponsored item to the corresponding item. The final item recommendations are then presented to the user.

Abstract: This application relates to apparatus and methods for automatically determining and providing carousels specifically curated for a user. In some examples, a computing device obtains user transaction data identifying in-store and/or online transactions, and user engagement data identifying user interactions with items and carousels from user's prior sessions. The computing device determines a sequential order for presentation of carousels with a set of item recommendations. For example, the computing device scores each potential carousel based on prior user interactions and transactions with items and carousels. The carousels are then ranked and subsequently presented to the user based on their corresponding scores.

Abstract: A method including receiving a basket including basket items selected by a user from an item catalog. The method also can include grouping the basket items of the basket into categories based on a respective item category of each of the basket items. The method additionally can include randomly sampling a respective anchor item from each of the categories. The method further can include generating a respective list of complementary items for the respective anchor item for the each of the categories based on a respective score for each of the complementary items generated using two sets of trained item embeddings for items in the item catalog and using trained user embeddings for the user. The two sets of trained item embeddings and the trained user embeddings can be trained using a triple embeddings model with triplets.

Abstract: A method being implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media. The method can include training two sets of item embeddings for items in an item catalog and a set of user embeddings for users, using a triple embeddings model, with triplets. The triplets each include a respective first user of the users, a respective first item from the item catalog, and a respective second item from the item catalog, in which the respective first user selected the respective first item and the respective second item in a respective same basket. The method also can include randomly sampling an anchor item from a category of items selected by a user. The method additionally can include generating a list of complementary items using a query vector associated with the user and the anchor item.