Agent Availability Scoring via Sensor Data Analysis
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Solution Overview
Problem
Current contact center systems fail to accurately assess an agent's availability for customer interactions due to lack of consideration for environmental factors and cognitive load, leading to suboptimal customer experience and performance metrics.
Innovation Solution
A computerized method and system that evaluates agent work conditions by collecting data from sensors on communication devices, performing analyses such as sentiment, network quality, and ambient noise analysis to calculate an availability score for communication-channel types, which is then used to route interactions and provide visual cues to agents for channel switching recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If binary availability methods are used where agents set their state themselves or via simple availability rules, then the system is easy to operate and implement, but the measurement precision of agent availability is insufficient and does not account for environmental factors and cognitive load
Solution Approach 1:
The availability assessment is segmented into multiple independent analysis components: sentiment analysis, network quality analysis, ambient noise analysis, and agent movement analysis. Each component independently evaluates a specific aspect of agent availability, and their results are combined to form a comprehensive availability score. This segmentation allows the system to achieve high measurement precision without overwhelming complexity, as each module can be developed and maintained independently.
Solution Approach 2:
The system uses a multi-functional availability module that performs diverse analysis functions (sentiment, network quality, noise, movement) through a unified architecture. This universal module serves multiple purposes: real-time availability scoring, routing decision support, and agent performance tracking. By consolidating these functions into a single system rather than separate tools, the patent achieves comprehensive availability assessment without proportionally increasing system complexity.
2Measurement precision
If comprehensive environmental factors and cognitive indicators are collected and analyzed, then the availability score accuracy is improved, but the loss of information processing increases and the system requires more computational resources
Solution Approach 1:
The system extracts only the most relevant features from each data source: sentiment polarity from communication content, network quality metrics from connection data, noise levels from audio sensors, and movement patterns from device sensors. By extracting only these critical features rather than processing all raw data, the system maintains high availability scoring accuracy while significantly reducing the information processing load and computational requirements.
Solution Approach 2:
The system implements partial monitoring of environmental factors by focusing on the most impactful indicators (e.g., ambient noise levels, basic movement detection) rather than continuously analyzing all possible data streams. This partial action approach provides sufficient availability information for routing decisions without the excessive computational burden of comprehensive continuous monitoring of every environmental parameter.
3Reliability
If real-time availability scoring with multiple analysis types is implemented, then routing decisions are optimized for customer experience, but the productivity of the system decreases due to increased processing time
Solution Approach 1:
The system performs availability analysis periodically at scheduled intervals (e.g., at the start of each interaction and at regular updates during interactions) rather than continuously monitoring every millisecond. This periodic action provides sufficiently up-to-date availability information for routing decisions while maintaining high processing speed and system productivity. The periodic updates balance the need for reliable routing decisions with the requirement for fast interaction processing.
Solution Approach 2:
The system performs preliminary availability assessment before routing decisions are made, evaluating sentiment, network quality, noise, and movement factors in advance. This preliminary action ensures that routing decisions are based on comprehensive availability data without adding processing delays during the actual interaction routing moment, thus maintaining both high reliability and productivity.
4Measurement precision
If agents are monitored via multiple sensors and analyses, then the availability indication becomes more accurate, but the ease of operation decreases and agents may feel surveilled
Solution Approach 1:
The system automatically collects availability data from device sensors and communication content without requiring active agent participation or manual input. Agents simply use their communication devices as normal, and the system self-service collects sentiment, network quality, noise, and movement data in the background. This eliminates the need for agents to manually report their availability state while maintaining high measurement precision, thus preserving ease of operation.
Solution Approach 2:
The system uses the communication device itself as an intermediary to collect availability data, rather than requiring direct agent input or external monitoring equipment. The device's built-in sensors (microphone, motion sensors, network interface) naturally capture the required information as part of normal communication operations. This intermediary approach maintains agent privacy and comfort while achieving accurate availability measurement through the device's existing capabilities.
Data Source
AI summary
A computerized-method for providing an indication as to an availability of a communication-channel type that is used during an interaction with a customer, via a web app is provided herein. The computerized-method includes operating a communication-channel-type availability module that includes: receiving collected data of an interaction of an agent during an interaction with a customer, via a communication-channel-type from a communication manager module; operating one or more analyses on the collected data to yield a corresponding score of each analysis of the one or more analyses; calculating an availability-score of the communication-channel-type during the interaction, based on the score for each analysis of the one or more analyses; storing the calculated availability-score, in a data storage, as an availability-score of the agent, after the interaction ends; and displaying the availability-score as an indication to an availability of a communication-channel type, on a display unit, associated with the computerized system.