Plume Design, Inc.

Yusuke Sakamoto

Nipun Agarwal

Yoseph Malkin

Adam R Hotchkiss

Na Hyun Ha

Sandeep Eyyuni

Rohit Thadani

William John McFarland

William J McFARLAND

Sachin Vasudeva

Muhammad Ali Valliani

Systems, methods, and non-transitory computer-readable storage media are provided for predicting the likelihood or probability of a subscriber of a service to cancel or not renew a subscription. A method, according to one implementation, includes a step of receiving data pertaining to aspects of a service that is provided by a service provider to a subscriber in accordance with a subscription. The data may include one or more impact factors each having a positive, neutral, or negative influence on the likelihood of subscriber churn. The method also includes a step of using the one or more impact factors to predict the likelihood that the subscriber will cancel the subscription.

Predicting the likelihood of subscriber churn

System and methods include obtaining data, over the Internet, associated with a plurality of Wi-Fi networks each Wi-Fi network having one or more access points and each Wi-Fi network being associated with a customer of one or more service providers; aggregating and filtering the data; analyzing the aggregated and filtered data for a condition of each of the plurality of Wi-Fi networks; determining one or more resolutions for the condition of a Wi-Fi network of the plurality of Wi-Fi networks; and initiating a customer outreach workflow to provide the one or more resolutions to the customer associated with the Wi-Fi network.

Intelligent monitoring systems and methods for wi-fi metric-based resolutions for cloud-based wi-fi networks

System and methods include obtaining data, over the Internet, associated with a plurality of Wi-Fi networks each Wi-Fi network having one or more access points and each Wi-Fi network being associated with a customer of one or more service providers; aggregating and filtering the data; analyzing the aggregated and filtered data for Wi-Fi metric based alarms, each Wi-Fi metric based alarm being associated with detection of one of an offline Wi-Fi network of the plurality of Wi-Fi networks, an offline node of the Wi-Fi network, instability of the Wi-Fi network, congestion in the Wi-Fi network, and interference in the Wi-Fi network; determining the Wi-Fi metric based alarms based on the analyzing; and performing one or more actions based on the determined Wi-Fi metric based alarms.

Intelligent monitoring systems and methods for Wi-Fi metric-based alarms for cloud based Wi-Fi networks

System and methods include obtaining Wi-Fi network data, over the Internet, associated with a plurality of Wi-Fi networks each Wi-Fi network having one or more access points and each Wi-Fi network being associated with a customer of one or more service providers and obtaining customer data for each customer associated with the plurality of Wi-Fi networks, the customer data including call-ins made by customers; aggregating and filtering the data; analyzing the aggregated and filtered data including correlating the call-ins made by customers to the Wi-Fi network data; predicting customer call-ins based on correlations made between the call-ins made by customers and the Wi-Fi network data; and initiating a customer outreach workflow prior to a predicted customer call-in.

Intelligent monitoring systems and methods for wi-fi metric-based predictions for cloud based wi-fi networks

Intelligent monitoring systems to predict likelihood of subscriber churn

System and methods include obtaining data, over the Internet, associated with a plurality of Wi-Fi networks each Wi-Fi network having one or more access points and each Wi-Fi network being associated with a customer of one or more service providers; aggregating and filtering the data; analyzing the aggregated and filtered data for the network condition of each of the plurality of customers of one or more service providers; determining an internet service provider (ISP) outage based on a plurality of factors; and performing one of a plurality of resolution or notification actions.

Intelligent monitoring systems and methods for Wi-Fi metric-based ISP outage detection for cloud based Wi-Fi networks

Intelligent monitoring systems and methods for Wi-Fi metric-based predictions for cloud-based Wi-Fi networks

System and methods include obtaining data, over the Internet, associated with a plurality of Wi-Fi networks each Wi-Fi network having one or more access points and each Wi-Fi network being associated with a customer of one or more service providers; aggregating and filtering the data; analyzing the aggregated and filtered data based on one or more use cases; determining any of predictions and alerts for the one or more use cases based on the analyzing; and performing one or more actions based on the any of predictions and alerts.

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San Jose, CA, United States of America

Yusuke Sakamoto