Intelligent Virtual Assistant for Dynamic Query Redistribution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Enterprise organizations face challenges in detecting underutilized communication channels to optimize resource utilization and reduce wait times for customer queries, while also considering customer preferences and the need for licensed professionals.
Innovation Solution
A computing platform that monitors user traffic across multiple communication channels, uses an intelligent virtual assistant to gather query attributes, and applies machine learning models to select the most appropriate channel based on wait times and query requirements, directing users to the best-suited channel and resource.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If customers are directed to their preferred communication channels, then customer satisfaction is improved, but wait times increase due to channel utilization imbalances
Solution Approach 1:
The system dynamically adjusts communication channel assignments based on real-time utilization metrics and customer preferences. The intelligent virtual assistant evaluates current channel loads and modifies routing decisions on-the-fly, transitioning from static preference-based routing to dynamic optimization that balances customer satisfaction with wait time reduction.
Solution Approach 2:
The system changes the parameter of channel selection from purely customer-preferred to a composite metric incorporating both customer preference and current channel utilization. This parameter transformation enables the system to redirect customers to underutilized channels that still meet their service needs, thereby reducing wait times while maintaining satisfaction.
2Productivity
If communication channels are optimized for resource utilization, then productivity is improved, but detection complexity increases
Solution Approach 1:
The intelligent virtual assistant serves as an intermediary layer between customers and communication channels. It absorbs the complexity of monitoring multiple channel utilization metrics, analyzing real-time data, and making optimization decisions, thereby simplifying the overall system architecture while achieving improved resource utilization.
Solution Approach 2:
The system replaces manual or rule-based channel detection with machine learning models that automatically analyze utilization patterns and predict optimal routing decisions. This substitution of mechanical detection methods with intelligent algorithms reduces operational complexity while enhancing productivity.
3Loss of time
If machine learning models are used to optimize channel selection, then wait times are reduced, but system complexity increases
Solution Approach 1:
The machine learning models operate autonomously to optimize channel selection, continuously learning from utilization data and making real-time routing decisions without requiring manual intervention. This self-service capability reduces wait times while the automated nature of the system prevents complexity from escalating through human management overhead.
Data Source
AI summary
Aspects of the disclosure relate to automated redistribution of queries to underutilized channels. A computing platform may monitor user traffic for one or more customer service communication channels. Subsequently, the computing platform may identify estimated wait times for a plurality of users to be served via the one or more channels. Then, the computing platform may initiate, via an intelligent virtual assistant, a communication with a given user of the plurality of users. Then, the computing platform may receive, via the intelligent virtual assistant, one or more attributes of a query of the given user. Then, the computing platform may select a channel of the one or more channels. Then, the computing platform may provide, to an enterprise agent associated with the selected channel, the one or more attributes of the query. Subsequently, the computing platform may direct the given user to the selected channel.