Adaptive Cloud Conversation Platform for Callback Timing
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Solution Overview
Problem
Current callback scheduling systems in contact centers lack the ability to adaptively determine when and how to establish ongoing communications with consumers to maximize the relationship between consumers and brands, failing to account for the complexity of ongoing conversations and consumer interactions.
Innovation Solution
An adaptive cloud conversation platform is developed, comprising a connection management services layer, an initiation management services layer, and a user management services layer, utilizing machine learning algorithms to determine the optimal timing and channel for callbacks, thereby maximizing consumer-brand relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If basic callback scheduling systems are used to track consumer interactions, then interaction history can be maintained, but the system cannot determine when and how to establish further communications to maximize consumer-brand relationships
Solution Approach 1:
The system is divided into three distinct layers: connection management services layer (determines what communications should be established and how), initiation management services layer (determines when communications should be established), and user management services layer (stores consumer and brand information). This segmentation allows each layer to specialize in specific decision-making aspects, enabling the system to determine when and how to establish communications without overwhelming complexity.
Solution Approach 2:
Machine learning algorithms are introduced as intermediaries between the data storage layer and the communication execution layer. These algorithms process consumer behavior data and brand information to generate intelligent decisions about communication timing and method, acting as mediators that translate raw data into actionable communication strategies.
2Extent of automation
If machine learning algorithms are incorporated into services to analyze consumer behaviors and preferences, then automated decisions regarding communication timing and method can be made, but computational resources and processing time are increased
Solution Approach 1:
Machine learning algorithms are trained in advance on historical consumer behavior data and interaction patterns. This preliminary training enables the algorithms to make rapid automated decisions during actual communication scenarios without requiring extensive real-time computation, thus reducing energy consumption during operation while maintaining high automation capability.
Data Source
AI summary
An adaptive cloud conversation platform capable of making automated decisions regarding when and how to establish ongoing communications with consumers so as to maximize the relationship between the consumer and a given brand. The system has a connection management services layer which determines what communications should be established and how they should be established, an initiation management services layer which determines when communications should be established, and a user management services layer which stores information about consumers and brands for determination of when and how communications should be established. Certain of these services have machine learning algorithms incorporated into them trained to perform analyses of the particular type of operation handled by that service. The outputs of each service can be used as inputs to other services, such that a network of machine learnings algorithms is created which determines when and how to establish ongoing communications with consumers.