KPI Modeling

KPI (Key Performance Indicators) modeling is core to Predictive UC Analytics Dynamic Service Modeling feature set. Predictive’s Service Modeling capabilities span all UC&C media type of voice, contact center, video, and collaboration systems whereby KPIs can look at one specific dataset or if needed, provide the ability to cross correlate KPIs thus gaining visibility into complex analytics.

To simplify the significance of dynamic service modeling first remember that service models are time based where the time interval is user defined. Next, a model definition enables any attributes of a session or associated fields to be modeled with the use of associating both AND/OR filter criteria, the final component of the service model creation of KPIs. KPIs represent sub-criteria that are modeled within the definition of the overarching service model. Each KPI can act as an accumulator, a distinct accumulator, as well as perform mathematical equations. Finally, each KPI can have associated thresholds that trigger automatic events in context of the KPI definition. Triggers can be absolute values, recognized changes in standard deviation, or shifts in statistical regression models.

A basic use case of using Service Modeling can be to monitor Quality of Service (QoS) by setting a model that monitors device pools or device locations where the KPIs track fair, poor, and bad MoS scores as well as distinct users effected by poor and bad quality. Event triggers can provide immediate notification as well as publish automated content of those effecting your internal collaboration tools or ticketing system.

The feature set is also very popular in omni-channel contact centers where voice, video, IM, and application sharing are widely used. Here both inbound and outbound communications can be tracked. Triggers can notify supervisors of increases in abandon activity, increases in call activity, emails that have not been responded to, or aging voice mails.

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