The present disclosure is related to systems and methods of merging tenant shadow systems of record into a master system of record. First tenant record objects of a first tenant system of record can be accessed. A master record object for a master system of record can be generated using the corresponding first tenant record object. A second tenant record object of a second tenant system of record can be accessed. Whether the second tenant record object is to be merged into the corresponding master record object can be determined. When determined to merge, the second tenant record object can be merged into the corresponding master record object. When determined to not merge, a new master record can be generated.

The present disclosure relates to electronic activity classification. A plurality of node profiles corresponding to a plurality of unique entities is maintained. A plurality of electronic activities is accessed from electronic accounts of one or more data source providers. Features are extracted from the electronic activities to determine a classification of the first electronic activity. A confidence score is determined indicating a likelihood that the first electronic activity of a classification type. An association is stored between the first electronic activity and a tag corresponding to the first classification type. The confidence score is updated based on a second electronic activity.

Systems and methods for inferring schedule patterns using electronic activities of node profiles are disclosed. Exemplary implementations may: access electronic activities via electronic accounts associated with data source providers; identify, for a node profile, a set of electronic activities of each electronic account linked to the node profile within a time period; identify for each electronic activity, a timestamp of the electronic activity; generate for each time interval within the time period, a temporal distribution of electronic activity for the time interval based on respective timestamps of each electronic activity; determine an electronic activity pattern based on the temporal distribution of electronic activity for each time interval within the time period; identify based on the electronic activity pattern, a first region of the electronic activity pattern satisfying a region identification policy; and store, in one or more data structures, an association between the first region and the node profile.

The present disclosure relates to systems and methods for determining an engagement profile of a participant by associating electronic activities to a profile. It may generate the engagement profile based on analysis of the electronic activity level. An example implementation may contain the following steps. The system may access for a first record object a plurality of electronic activities linked with the first record object. The system may identify for a participant from the plurality of electronic activities a set of electronic activities including the participant. The system may determine an engagement profile of the participant based on a first number of electronic activities of the set of electronic activities sent by the participant, a second number of the set of electronic activities received by the participant and a temporal distribution of the set of electronic activities. The system may store the engagement profile in one or more data structures.

The present disclosure relates to the field of communication technologies. In one example charging method, a gateway manages, by using customer as a granularity, charging information of a plurality of devices of a customer that are currently connected to the gateway, and establishes one customer-level charging session or customer level Charging Data Record (CDR).

Methods, systems, and storage media for comparing nodes are disclosed. Exemplary implementations may: maintain a plurality of member node profiles and group node profiles; identify, for a first member node profile corresponding to a first group node profile, a first plurality of electronic activities linked to the first member node profile; determine a first performance profile corresponding to the first member node profile; identify a plurality of second performance profiles using node field-value pairs of member node profiles linked to the respective second performance profiles; generating a performance score of the first performance profile based on a comparison of the first performance profile to the plurality of second performance profiles; and storing, in one or more data structures, an association between the first member node profile and the performance score of the first performance profile.

The present disclosure relates to systems and methods for assigning node profiles to record objects. The method includes maintaining a plurality of node profiles. The method includes identifying a record object to which to assign a node profile. The method includes selecting a subset of node profiles that satisfy a node profile selection policy. The method includes generating, for each of the subset of node profiles, a performance profile using electronic activities of the node profiles and one or more object field-value pairs of the record object. The method includes determining, for a first node profile, that a match score between the first node profile and the record object based on the performance profile and one or more object field-value pairs of the record object satisfies a threshold. The method includes storing an association between the first node profile and the record object based on the match score.

Methods, systems, and storage media for managing electronic activity driven targets are disclosed. Example implementations may: maintain a plurality of node profiles; select, for a first node profile, using one or more field-value pairs of the first node profile, an endpoint profile generated using electronic activities of second node profiles including one or more field-value pairs that match the one or more field-value pairs of the first node profile, the endpoint profile specifying electronic activity driven targets that can be tracked by parsing electronic activities corresponding to the first node profile; store in one or more data structures, an association between the first node profile and the endpoint profile specifying the electronic activity driven targets; parse a plurality of electronic activities corresponding to the first node profile; and update a metric relating to the electronic activity driven targets responsive to parsing the second plurality of electronic activities.

The present disclosure relates to systems and methods for confirming a scheduled event based on one or more electronic activities. The method includes identifying an electronic activity corresponding to a scheduled event. The method includes accessing, for a participant, electronic activities corresponding to the participant. The method includes extracting, for each of the electronic activities, a feature set based on participants included in the electronic activity or content included in a body of the electronic activity. The method includes determining, for each electronic activity, a relevancy score based on the extracted feature set. The method includes identifying a subset of electronic activities of the plurality of electronic activities. The method includes determining a probability score indicating a likelihood that the participant attended the scheduled event. The method includes generating an attendance tag indicating that the participant attended the scheduled event.

The present disclosure relates to generating field-specific health scores for a system of record. Record objects of a system of record corresponding to a data source provider may be accessed. Each record object may correspond to a record object type and have one or more object field-value pairs. Node profiles may be maintained. Each node profile may include one or more node field-value pairs. A subset of node field-value pairs of the node profiles with confidence scores greater than a threshold score may be identified. Node profiles having the subset of node field-value pairs may be identified. Node field-value pairs of the subset may be compared with corresponding object field-value pairs of the record objects. A field-specific health score for each field may be generated based on the comparison.