What is Outlier Detection?
In contact centers, outlier detection helps spot irregularities in operations, agent performance, or customer interactions. These anomalies can indicate potential issues such as fraud, system errors, or unusual customer behavior. By flagging these deviations early, managers can take corrective action to maintain service quality and compliance.
Common Types of Unusual Patterns
- In Call Data Records (CDRs): Abnormal call durations, excessive transfers, or unusually high call volumes that may point to technical glitches or fraudulent activity.
- In Customer Behavior: Sudden spikes in complaints, unexpected cancellations, or atypical sentiment scores that indicate dissatisfaction or churn risk.
Techniques and Algorithms for Detection
- Statistical Methods: Identifying data points that lie outside normal distributions.
- Machine Learning Algorithms: Using clustering, classification, or neural networks to detect anomalies in real time.
- Rule-Based Detection: Setting predefined thresholds for metrics like call length or wait times.
- AI-Powered Analytics: Leveraging speech analytics and customer interaction analytics to uncover hidden behavioral outliers.
Outlier detection enables contact centers to proactively address risks, improve service quality, and gain deeper insights into both operational data and customer behavior.
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