Abstract: A method and system may input telemetry into a data structure. The telemetry may comprise a plurality of measurement results or other data collected from remote network nodes. Subscriber use metrics may be determined from the telemetry. From the use metrics and habits of the subscriber, user characteristics may be determined. A likelihood of service cancellation, based on the user characteristics, may be estimated in an embodiment. In another embodiment, a likelihood of receiving an inquiry from the subscriber at a subscriber support center may be estimated. Service enhancements may be proactively provided to the subscriber, based on the telemetry, the subscriber use metrics and the user characteristics in advance of service cancellation or receiving the inquiry. Last mile equipment may provide telemetry. The last mile equipment may include set top boxes, cellular phones, tablets and the like.

Abstract: A method may comprise collecting data from a core network, an operator network and a customer premise network. KPI values corresponding to one or more KPIs may be determined, based on the collected data. KQI values corresponding to one or more KQIs may be determined, based on the one or more of the determined KPI values. The determined KQI values may be output to an operator. In an embodiment, data collected from the core network may include data collected from one or more analyzer devices and head end probes. Data collected from a customer premise network may include data collected from last mile equipment or customer premise probes.

Abstract: A method and apparatus for estimating bitrate, buffering events and/or other QoE metrics of video reception, based on CDN logs, are provided. The method and apparatus may rely on an AI model, including a neural network. The neural network may receive a training data set comprised of a plurality of CDN server logs. The CDN server logs may be associated with known QoE metrics collected from a plurality of user devices. Once the neural network is trained, a CDN server log, without associated QoE metrics, may be received as input. With the trained neural network and the CDN server log, buffering events and average bitrate QoE metrics may be estimated for one or more user devices without explicitly receiving QoE metrics from the user device.

Abstract: A method may comprise collecting data from a core network, an operator network and a customer premise network. KPI values corresponding to one or more KPIs may be determined, based on the collected data. KQI values corresponding to one or more KQIs may be determined, based on the one or more of the determined KPI values. The determined KQI values may be output to an operator. In an embodiment, data collected from the core network may include data collected from one or more analyzer devices and head end probes. Data collected from a customer premise network may include data collected from last mile equipment or customer premise probes.

Abstract: A method and system may input telemetry into a data structure. The telemetry may comprise a plurality of measurement results or other data collected from remote network nodes. Subscriber use metrics may be determined from the telemetry. From the use metrics and habits of the subscriber, user characteristics may be determined. A likelihood of service cancellation, based on the user characteristics, may be estimated in an embodiment. In another embodiment, a likelihood of receiving an inquiry from the subscriber at a subscriber support center may be estimated. Service enhancements may be proactively provided to the subscriber, based on the telemetry, the subscriber use metrics and the user characteristics in advance of service cancellation or receiving the inquiry. Last mile equipment may provide telemetry. The last mile equipment may include set top boxes, cellular phones, tablets and the like.