Automated communication systems

A computing system comprising:

• one or more processors;
• and
• one or more memories storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
• obtaining a graph comprising a plurality of nodes and a plurality of edges, wherein:
• the nodes include behavior nodes that correspond to respective behavior reasons in a plurality of predefined behavior reasons, the predefined behavior reasons being reasons for behaviors of clients,
• the edges correspond to transitions between the nodes and are associated with weights, and
• for each respective edge of the plurality of edges that corresponds to a transition from any first behavior node to any second behavior node, the respective edge is associated with a weight that corresponds to a probability of the clients having a behavior reason associated with the second behavior node after having a behavior reason associated with the first behavior node;
• determining a spatial similarity score based on the graph;
• obtaining one or more interaction records, wherein the one or more interaction records are records of interactions involving a current client;
• applying a trained machine learning (ML) model to the one or more interaction records to identify one or more behavior reasons associated with the current client;
• generating a behavior vector based on the one or more behavior reasons associated with the current client;
• computing, based on behavior vector of the current client, pairwise contextual similarity scores for behavior reasons associated with the current client;
• determining a behavior consistency score for the current client based on the pairwise contextual similarity scores and the spatial similarity score;
• classifying, based on the behavior consistency score, the current client into a category;
• and
• performing a communication action with respect to the current client based on the category.

One or more non-transitory computer-readable storage media having processor-executable instructions stored thereon that, when executed by one or more processors, cause the one or more processors to:

• obtain a graph comprising a plurality of nodes and a plurality of edges, wherein:
• the nodes include behavior nodes that correspond to respective behavior reasons in a plurality of predefined behavior reasons, the predefined behavior reasons being reasons for behaviors of clients,
• the edges correspond to transitions between the nodes and are associated with weights, and
• for each respective edge of the plurality of edges that corresponds to a transition from any first behavior node to any second behavior node, the respective edge is associated with a weight that corresponds to a probability of the clients having a behavior reason associated with the second behavior node after having a behavior reason associated with the first behavior node;
• determine a spatial similarity score based on the graph;
• obtain one or more interaction records, wherein the one or more interaction records are records of interactions involving a current client;
• apply a trained machine learning (ML) model to the one or more interaction records to identify one or more behavior reasons associated with the current client;
• generate a behavior vector based on the one or more behavior reasons associated with the current client;
• compute, based on behavior vector of the current client, pairwise contextual similarity scores for behavior reasons associated with the current client;
• determining a behavior consistency score for the current client based on the pairwise contextual similarity scores and the spatial similarity score;
• classify, based on the behavior consistency score, the current client into a category;
• and
• perform a communication action with respect to the current client based on the category.