Anomaly Detection Metrics

Leverage Anomaly Detection Metrics to identify unusual patterns and irregularities in customer behavior data and uncover contributing factors.

By applying Anomaly Detection to key metric reports within Tealeaf on Cloud or Watson Customer Experience Analytics, you can leverage the power of predictive analytics to detect outliers in your customer behavior data; such as abandonment and determine top contributing factors.  Anomaly Detection affords the opportunity to easily differentiate unexpected behavior patterns across numerous key metrics and offers important use cases such as detection of fraud or intrusion, user struggle and system errors.  

Click here to check out other IBM Watson CXA and Tealeaf Use Cases. 

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Read the Shubert TIcketing CXA Case Study