Recommendations enable you to fine tune your Alerts and manage your operations even more efficiently.
Using Anodot's unique machine learning capabilities, the system learns and notifies of any Alert that can be tweaked to improve performance. However, in order for Anodot to fully learn what can be tweaked, user feedback on previous Alerts is a critical part of the Recommendations process, as described below.
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Step 1: Alerts are triggered and sent.
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Step 2: Alert recipients provide their feedback on the alerts. This step is critical, because without user feedback Anodot cannot suggest any tweaks or improvements.
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Step 3: The feedback is analyzed (mainly the NOT-INTERESTING feedback) to see how the Alert can be improved. This is where Anodot's machine learning capability comes into its own.
