Predictive Resource and Channel Selection for Contact Centers
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Solution Overview
Problem
Contact centers face challenges in optimally selecting resources and channels for customer transactions, leading to inefficiencies and poor user experiences due to ad hoc approaches and the need for balancing automation and agent-driven actions, which can result in increased costs and incorrect channel/resource decisions.
Innovation Solution
An action model device within the contact center that receives transaction and interaction data to generate a predictive score, using a predictive model to determine the optimal resource and channel for ongoing transactions, thereby optimizing the customer journey by dynamically selecting the best resource and channel based on current and past data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If ad hoc approaches are used for resource and channel selection, then operational flexibility is maintained, but efficiency and user experience deteriorate
Solution Approach 1:
The system enables self-service through automated predictive modeling that independently analyzes transaction data, interaction data, and historical patterns to determine optimal resource and channel assignments without requiring manual operational decisions, thereby maintaining flexibility while dramatically improving efficiency
Solution Approach 2:
The system performs preliminary actions by pre-calculating predictive scores and determining optimal resource/channel combinations before transactions are fully processed, allowing the system to proactively assign resources and channels based on predicted outcomes rather than reactive ad hoc decisions
2Measurement precision
If more manual agent intervention is used for resource selection, then decision accuracy may improve, but costs and processing time increase
Solution Approach 1:
The system replaces manual mechanical decision-making processes with automated computational predictive modeling that analyzes multiple data dimensions simultaneously, achieving superior selection accuracy without the time costs associated with human review and approval processes
Solution Approach 2:
The system implements feedback mechanisms by continuously analyzing transaction outcomes and interaction data to refine predictive models, using historical performance data to improve future resource and channel selection accuracy automatically without requiring manual intervention
3Speed
If automated response systems are used for all initial interactions, then processing speed increases, but user experience and transaction resolution quality may deteriorate
Solution Approach 1:
The system applies dynamics by enabling flexible, context-dependent routing that can dynamically switch between automated and human-based interactions based on real-time predictive scoring, allowing the system to optimize for speed when appropriate and for quality when necessary
Solution Approach 2:
The system changes parameters by using predictive modeling to dynamically adjust the level of automation based on transaction characteristics, user behavior patterns, and contextual factors, transforming static automated routing into adaptive decision-making that balances speed and quality
Data Source
AI summary
A method, a device and a system selects an optimal resource and/or channel. The device of a contact center receives transaction data and interaction data corresponding to a transaction between the contact center and a user device. The transaction data includes one of a current resource being utilized for the transaction or a current channel indicating a communication channel being utilized for the transaction. The interaction data corresponds to interaction information of the current resource and the user device. The device generates a current score value of the transaction based on the transaction data and the interaction data. The device determines one of a further resource or a further channel to be used for the transaction based on the current score value and a predictive model. The predictive model defines relations between score values with the resources or the channels.