Customer SmartDetect

    Mood detection with Azure Cognitive Services to prevent churn.

Detecting a customer at risk of leaving is a time-critical activity that often requires drawing on indicators of a customer's satisfaction level that are not formally captured in systems.

The Customer SmartDetect solution, utilising Azure Cognitive Services, allows businesses to detect the mood of a customer by the tone of their voice or their correspondence, rate the risk of customer churn and produce timely alerts to operational staff - all in real-time.

What's the
  • Natural Language Processing (NLP) understands unstructured data, such a phone call
  • Customer mood is detected immediately with real time feedback provided
  • Real Time alerts indicate whether a customer is most likely to churn
Improve Customer
  • Use data-driven evidence to train and provide real time recommendations to operational staff
  • Escalation workflows to ensure customer is effectively engaged
  • Ability to take preventative action when customer is “likely to churn”
Prevent the
  • Customer history displayed at time of call allowing operational staff to evaluate customer’s engagement
  • Speech to text and Vocal Emotion Analysis indicate primary and secondary moods
  • Recognize the up-sell and cross-sell opportunities before they arise
“Early AI adopters that combine strong digital capability with proactive strategies have higher profit margins and expect the performance gap with other firms to widen in the next three years.”
- McKinsey Global Institute

Our Promise to you

Customer SmartDetect gives you the tools to provide a better customer experience and take preventive action at the point of contact.

Solution in Action

Facial Recognition Customer Service Kiosk with Cortana Intelligence

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Solution Benefits

  • Real-time Customer Sentiment Analysis coupled with Machine Learning provides the likelihood of a customer churning
  • Operational staff are enabled to take corrective action immediately
  • Opportunity to cross-sell or up-sell becomes apparent as the customer’s temper, positivity, vocal mood and textual analysis of transcripts are analysed