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Stay on top of customer feedback with AI-powered alerts

Anomaly notifications trigger when machine learning detects atypical spikes in feedback volume.

We live in an age where we’re constantly bombarded with alerts and notifications. Apps on your computer and phone are always vying for your limited attention. So alerts need to be important to merit that attention. The last thing you want is an alert that isn’t useful and wastes time.

But alerts are so necessary, especially when it comes to delivering exceptional experiences to your customers. With 50% of consumers switching to a competitor after one bad experience (according to Zendesk), it has never been more important to stay on top of customer feedback.

As your consolidated, searchable platform for user feedback, unitQ Monitor extracts data-driven insights from what users are saying to help you increase product quality. Our real-time monitoring platform sends alerts to your preferred communication platforms including Slack and PagerDuty so you don’t miss out on any widespread or high-impact issues affecting your users. While you may have a monitoring stack for machine-level data like logs, unitQ builds on that stack by giving you a window into human qualitative data like app store reviews, help desk tickets, social media posts and more. 

Alerts keep you and your team aware of new or changing quality issues and user feedback trends. You get immediate notification when issues arise from a new release, or an old bug resurfaces. We want to make our alerts even better, so now we’ve made our alerts system even more intelligent with a new type of alert called an anomaly alert. Anomaly alerts use machine learning to automatically trigger when the system detects an atypical spike in feedback volume. 

Use anomaly alerts when you want alerts to account for sudden departures from your seasonal trends where feedback may fluctuate normally. Also use anomaly alerts when you want to tune the sensitivity of your alert triggers, which allows for flexibility in how often these alerts trigger. The higher the sensitivity, the higher the alert volume.

Anomaly alerts should cut down on unnecessary or too many alerts. Over time unitQ Monitor learns the behavior of your product and knows when a true anomaly is occurring, not just seasonal variations in your user feedback.

If you want to learn more about how anomaly alerts work, book a demo today!

Pete Bratach is unitQ documentation manager.