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Technology Partnership SilverEngine/Cubro

SilverEngine is offering analytics services together with our technology partnership Cubro which provide network probe data. Analyzing probe data in an intelligent way is helping Mobile operators optimizing and fixing issues in their network, understanding network and user behavior and to segment subscribers for the purpose of getting insights on user experience trends valuable for marketing purposes or targeted optimization.

Operators are getting actionable insights from the data generated by the Probe. The insights include for example how to improve subscribers experience and optimize mobile network performance using Analytics and Machine Learning techniques.

Operators benefits include better network performance, lower costs for network infrastructure, reduced subscriber churn due to better network quality and improved return on investment (ROI).

Depending on the needs and interest of the operator we are offering service packs for VoLTE, Mobile Broad Band (MBB), Customer Experience Management (CEM), Applications, 5G and IoT. For each service we provide following:

  • Identify underperforming cells, information for Network Optimization activities
  • Core NE elements issues
  • Poor performing User Equipment and Devices
  • Subscribers with poor experience - ensure VIP, high ARPU and In-bound roaming subscribers getting the service they expect
  • Suggested marketing campaigns for churn preventions and upsells

SilverEngine is offering together with our partner Cubro following service packages with actionable insights:

VoLTE

  • Example KPIs: IMS Registration Success rate, Speech quality (MOS), mute calls, dropped call rate, repeat calls
  • Machine Learning to identify Radio KPIs associated with mute calls and poor speech quality

Mobile Broadband (MBB)

  • Example KPIs: Attach issues, APN failures, Streaming data throughput, DNS resolution issues
  • Subscribers Speed test performance e.g. Ookla

Customer Experience Management (CEM)

  • Clustering of subscriber’s experience and forecasting
  • QoE index [0-100] for each subscriber or subscriber segment consisting of relevant C-plane and  U-plane KPIs
  • Optimized Subscriber QoE index model based on Machine Learning
  • Subscriber segmentation

Applications

  • Application profiling, access patterns and used protocols
  • Application QoE – QoE model using machine learning
  • Improve relevant KPIs for each popular application
  • Video performance e.g. YouTube and Netflix
  • Rationale of local video or gaming cache

5G

  • Time spent on 5G vs LTE in 5G NSA deployment per subscriber
  • Performance advantage in 5G compared to LTE
  • Machine Learning to find out relevant Radio and Backhaul KPIs associated with long RTT and low throughput

IoT

  • Understand and learn communication patterns of different classes of IoT devices
  • Detect anomalies and outages

Mobility

From the mobility patterns mobility data sales packages can be created for advertisement, commercials, real estate and traffic planning. The insights can help deciding:

  • Whether to open a new branch store in a specific spot
  • Where to focus infrastructure improvements based on biggest traffic jams

Realized by:

  • Analyzing movement vectors and speed of users between radio cells
  • Creating density and activity patterns of subscribers as a function of time in area of interests
  • cCorrelating mobility patterns with CRM data.

 

Service Delivery Cycle

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