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Up until now, expensive, and mostly inaccurate post incident surveys were the only way to track and measure customer satisfaction. If you are using Jira Service Management for customer support then customer satisfaction can now be effortlessly improved utilising automated AI driven natural language processing (NLP).
Here are 3 simple steps to leverage AI to improve customer satisfaction
The tendency of unsatisfied customers to express themselves, while satisfied customers remain silent can be countered utilising AI natural language analysis.
Rather than gather feedback from a minority utilising post-incident surveys, utilise AI to analyse all customer and agent sentiment in real-time and do away with intrusive surveys.
Real-time feedback opens up new opportunities for action. If dissatisfaction is detected as soon as it appears, agents can intervene and prevent escalation and mitigate the risk of customers leaving dissatisfied.
Leverage real time sentiment analysis to enable agents to identify, intervene and repair interactions where negative customer sentiment has been detected.
Customer sentiment alarms allow agents and managers to watch customer satisfaction metrics and receive notifications when satisfaction scores fall below configured thresholds.
Get notified in real-time and stay up-to-date with all your customer satisfaction metrics to enable you to take action when required.