Quality of Experience Analytics

Quality of Experience Analytics

Quality of Experience Analytics

  • Overview

    Quality of Experience Analytics uses network performance, fault and error indicators to understand the quality of experience encountered by subscribers, devices and services. It is Quality targeted at operations, engineering and network planning personnel.

    This analysis is performed at the transactional level (subject to the granularity of error indicators) to determine the service impact of each customer interaction. Because all events are considered, these metrics go beyond averages and aggregates to allow analysis down to an individual session, subscriber, service, device, or any combination thereof.

    By integrating with other Customer Analytic modules, it is possible to incorporate financial metrics (from the Profitability Analytics module) or behavioral metrics (from the Behavioral Analytics module) to derive a more holistic view of each customer. For example, financial information can be associated with particular network outages to quantify the business impact, and thus prioritize corrective activities. Especially in the case of premium or roaming services, the volume of traffic or number of affected subscribers may have little indication of the actual cost or revenue impact.
     

  • Benefits

    • Multi-dimensional analytics assigns performance and fault KPIs and KQIs at the subscriber, device, service and network-level, as well as any combination of the various dimensions.
    • Contextual drill-down is available down to to specific events.
    • QoE analysis is extensible with behavioral and profitability attributes.
    • Analytic environment for deeper investigation into individual customers, or group of customers with similar experiential issues.

  • Features

    • Subscriber Analytics: tracks performance and fault KPIs and KQIs at the subscriber level to understand the potential satisfaction of the customer technology experience. When paired with profitability and behavioral data, this creates a powerful means for tailoring personalized correcting, retentive or upsell campaigns.
    • Device Analytics: Analysis of performance and fault at the device, class and manufacturer level provides important information not only to the team that manages the device portfolio, but also to the team that manages handset suppliers. Observations of the performance of these devices on the operators own network corroborate (or refute) manufacturer’s specifications, and allow for the correction (or decommissioning) of non-performing handsets.
    • Service Analytics: deliver analytics focused on service or content. By performing service level QoE analytics, it is possible to create new bundles or implement new services and partnerships to take advantage of customer preferences.