Abstract: A method being implemented via execution of computing instructions configured to run at one or more processors. The method can include determining a primary store and one or more secondary stores for pickup of an order of a user, based at least in part on a pickup type of the order. The method also can include determining real-time availabilities of first time slots at the primary store and real-time availabilities of second time slots at the one or more secondary stores. The method additionally can include generating a list of available time slots comprising at least a portion of the first time slots at the primary store and at least a portion of the second time slots at the one or more secondary stores, based at least in part on the real-time availabilities of the first time slots at the primary store and the real-time availabilities of the second time slots at the one or more secondary stores.

Abstract: The present disclosure is directed to systems and methods for predicting an outcome of a user journey. For example, a method may include: identifying a plurality of patterns based on a plurality of user interactions of a plurality of users with a plurality of touchpoints; applying a parameter to filter the plurality of patterns; evaluating the filtered plurality of patterns based an evaluation criterion; and applying a feedback loop based on the evaluation of the filtered patterns to modify the parameter or adjust a user experience.

Abstract: Based on the interaction data and response data, an interaction monitoring platform may determine a first known sentiment and a second known sentiment, identify a first pattern and a second pattern in the interaction data, and generate a first pattern-level sentiment and a second pattern-level sentiment based on the known sentiments and the identified patterns. A binary indicator may indicate which identified patterns are exhibited in a subset of the interaction data. The platform may train a gradient boosting model using known sentiment as a target variable and using binary indicators and pattern-level sentiments as input data. The platform may predict a sentiment corresponding to a subset of interaction data with unknown sentiment that exhibits one or more of the first pattern or the second pattern based on a binary indicator and the trained gradient boosting model.

Abstract: The present disclosure is directed to systems and methods for predicting an outcome of a user journey. For example, a method may include: identifying a plurality of patterns based on a plurality of user interactions of a plurality of users with a plurality of touchpoints; applying a parameter to filter the plurality of patterns; evaluating the filtered plurality of patterns based an evaluation criterion; and applying a feedback loop based on the evaluation of the filtered patterns to modify the parameter or adjust a user experience.

Abstract: The present disclosure is directed to systems and methods for predicting an outcome of a user journey. For example, a method may include: identifying a plurality of patterns based on a plurality of user interactions of a plurality of users with a plurality of touchpoints; applying a parameter to filter the plurality of patterns; evaluating the filtered plurality of patterns based an evaluation criterion; and applying a feedback loop based on the evaluation of the filtered patterns to modify the parameter or adjust a user experience.

Abstract: Based on the interaction data and response data, an interaction monitoring platform may determine a first known sentiment and a second known sentiment, identify a first pattern and a second pattern in the interaction data, and generate a first pattern-level sentiment and a second pattern-level sentiment based on the known sentiments and the identified patterns. A binary indicator may indicate which identified patterns are exhibited in a subset of the interaction data. The platform may train a gradient boosting model using known sentiment as a target variable and using binary indicators and pattern-level sentiments as input data. The platform may predict a sentiment corresponding to a subset of interaction data with unknown sentiment that exhibits one or more of the first pattern or the second pattern based on a binary indicator and the trained gradient boosting model.

Abstract: The present disclosure is directed to systems and methods for predicting an outcome of a user journey. For example, a method may include: monitoring interactions of a user with a plurality of touchpoints; predicting, based on a machine learning model, whether the interactions will result in a first outcome or a second outcome different than the first outcome, the machine learning being trained using a dataset based on historical interaction data, the dataset comprising a first plurality of patterns resulting in the first outcome and a second plurality of patterns resulting in the second outcome, wherein the predicting is based on a minimum number of interactions with the plurality of touchpoints; and providing an alternative interaction with the plurality of touchpoints to increase a probability that the interactions will result in the second outcome.

Abstract: A device obtains historical interaction data concerning a plurality of user interactions, where a user interaction of the plurality of user interactions includes one or more touchpoint sets, obtains historical response data concerning a plurality of user responses, where a user response of the plurality of user responses corresponds to a user interaction, and processes the historical interaction data and the historical response data using a modelling pipeline to determine an association between user interaction score and a touchpoint set. The device determines information concerning a current user interaction, processes the information concerning the current user interaction using the modelling pipeline to determine a current user interaction score and ranking of one or more touchpoint sets of the current user interaction, and causes, based on the current user interaction score and the ranking of the one or more touchpoint sets, at least one action to be performed.

Abstract: A device obtains historical interaction data concerning a plurality of user interactions, where a user interaction of the plurality of user interactions includes one or more touchpoint sets, obtains historical response data concerning a plurality of user responses, where a user response of the plurality of user responses corresponds to a user interaction, and processes the historical interaction data and the historical response data using a modelling pipeline to determine an association between user interaction score and a touchpoint set. The device determines information concerning a current user interaction, processes the information concerning the current user interaction using the modelling pipeline to determine a current user interaction score and ranking of one or more touchpoint sets of the current user interaction, and causes, based on the current user interaction score and the ranking of the one or more touchpoint sets, at least one action to be performed.

Abstract: A device obtains historical interaction data concerning a plurality of user interactions, where a user interaction of the plurality of user interactions includes one or more touchpoint sets, obtains historical response data concerning a plurality of user responses, where a user response of the plurality of user responses corresponds to a user interaction, and processes the historical interaction data and the historical response data using a modelling pipeline to determine an association between user interaction score and a touchpoint set. The device determines information concerning a current user interaction, processes the information concerning the current user interaction using the modelling pipeline to determine a current user interaction score and ranking of one or more touchpoint sets of the current user interaction, and causes, based on the current user interaction score and the ranking of the one or more touchpoint sets , at least one action to be performed.