Systems, methods, computer-readable media, and apparatuses for receiving requests for roadside assistance, generating user interfaces and using machine learning to generate roadside assistance instructions are provided. In some examples, a request for roadside assistance may be received. A user and one or more partners may be identified based on the request. In some examples, a profile associated with the user, partner or the like may be identified. A user interface may be generated based on the profile and may include features unique to the profile, partner, or the like. In some arrangements, the interface may include a first portion and a second portion. Selection of an option from the first portion may cause the system to identify data for display in the second portion and cause the data to be displayed in the second portion. Machine learning may be used to determine or identify one or more roadside assistance instructions and a roadside assistance instruction may be generated and executed.

Methods and systems are disclosed for dynamically conducting messaging campaigns, responsive to customer events. A set of parameters for a messaging campaign includes an initial schedule for providing a set of messages from a set of data objects. Responsive to a customer event associated with a first customer, an adjusted schedule is generated for providing the set of messages to the first customer. The adjusted schedule differs from the initial schedule in a timing for providing at least a first message, where the first message is scheduled to be provided at an adjusted timing proportionate to a time period between the customer event and a first milestone event. The first message is generated from a first data object and transmitted to the first customer, in accordance with the adjusted schedule.

Methods and systems are disclosed for dynamically conducting messaging campaigns, responsive to customer events. A set of parameters for a messaging campaign includes an initial schedule for providing a set of messages from a set of data objects. Responsive to a customer event associated with a first customer, an adjusted schedule is generated for providing the set of messages to the first customer. The adjusted schedule differs from the initial schedule in a timing for providing at least a first message, where the first message is scheduled to be provided at an adjusted timing proportionate to a time period between the customer event and a first milestone event. The first message is generated from a first data object and transmitted to the first customer, in accordance with the adjusted schedule.

A system and method for delivering promotional rewards to a customer mobile device comprising a promotional reward wheel, a media server, and messaging application operating on the customer's mobile device. A customer interacts with the promotional reward wheel and the system captures that interaction and responsive to the promotional reward that was won by the interaction, generates a message on the customer's mobile device with a means to claim the reward by sending the generated message to the appropriate message endpoint.

A gratuity analytics computing system, for generating gratuity analytics for a plurality of transactions of a customer at a service provider within a date range, is in communication with an electronic device of the service provider over an electronic network. The system includes a gratuity analytics computing device, a database including a memory in operable electronic communication with the gratuity analytics computing device, and a processor configured to: receive transaction data for the plurality of customer transactions occurring within the date range, match a plurality of authorization messages with a respective plurality of clearing messages, generate gratuity analytics for the plurality of customer transactions over the date range based on average tip data from the customer, and calculate a customer satisfaction score for one customer transaction of the plurality of customer transactions based on the generated gratuity analytics.

Embodiments of the present disclosure provide for predicted operational support data object selection and provision functionality. Predicted operational support data object(s) may be selected and provided to address particular malfunction classification(s) affecting networked device(s) on a dynamic home communications network. Some embodiments include identifying, in real-time, a device identification data set associated with a networked device set communicable with the dynamic home communications network; retrieving a device activity data set associated with the networked device set; applying a malfunction classification data model to the device activity data set to select the predicted operational support data object from the device operational support management repository; and outputting the predicted operational support data object to a client device in communication with the dynamic home communications network. The malfunction classification data model is trained based on training data, external aggregated activity data, and malfunction device history data.

Embodiments of the present disclosure provide for predicted operational support data object selection and provision functionality. Predicted operational support data object(s) may be selected and provided to address particular malfunction classification(s) affecting networked device(s) on a dynamic home communications network. Some embodiments include identifying, in real-time, a device identification data set associated with a networked device set communicable with the dynamic home communications network; retrieving a device activity data set associated with the networked device set; applying a malfunction classification data model to the device activity data set to select the predicted operational support data object from the device operational support management repository; and outputting the predicted operational support data object to a client device in communication with the dynamic home communications network. The malfunction classification data model is trained based on training data, external aggregated activity data, and malfunction device history data.

Based on the identification number of completely wireless earphones entered into an input unit, a checking unit of a terminal device checks whether or not conditions regarding an expiration period and the number of times of use for an exchange service for exchanging one of the pair that is not lost with left and right earphones are satisfied. A display control unit causes a display unit to display a message with an instruction to return the earphone that is not lost when the conditions are satisfied.

A device receives real-time data and historical data associated with a product or a service, wherein the real-time data includes data indicating real-time interactions between customers of the product or the service and customer service personnel. The device processes the real-time data and the historical data to generate processed data, and utilizes a first artificial intelligence model with the processed data to determine future events associated with the product or the service, and preventative solutions for the future events. The device extracts one or more features from the processed data to provide extracted features, and utilizes a second artificial intelligence model with the extracted features to determine priorities associated with the preventative solutions. The device performs one or more particular preventative solutions based on the priorities associated with the preventative solutions.

Embodiments of the present invention provide systems and methods for determining an engagement level for a customer-agent interaction (e.g., a phone call, text chat, etc.), and the engagement level can be used to evaluate the performance of the agent. For example, the engagement level can be used to determine or adjust a skill level of the agent, a performance of the communication channel that facilitates the interaction, and/or an optimal workload of the agent. According to some embodiments, the engagement level is used to determine that the agent may be experiencing difficulty satisfying a customer inquiry such that the agent should be assigned resources or assistance.