Transition From Resource Monitoring to Service Quality Monitoring

Expert Opinion


Davor Kobal

Apr. 30, 2024
2 min. read

Digital service providers are already monitoring devices/resources (fault and performance monitoring) in their network and can determine in an empirical way if their end customer service is working or not. Usually, employees working in the network operating center (NOC) have enough experience to determine if end customer service is working or not based on collected fault and performance data.

In order to move from this network centric view to a service or customer centric view, an additional software solution is required that would determine end customer service status automatically based on different data sources. This transition requires the implementation of a Service Quality Management solution (SQM) and the establishment of a Service Operating Center (SOC) or Service Management Center (SMC).

Service quality management solutions are designed to allow digital service providers to monitor and manage the level of end-to-end service they are delivering. Collected measurements about service quality are compared against defined service quality indicators, and the conclusions are made available to the interested parties.


According to ITU-T recommendation E.800, service quality can be defined as "the collective effect of service performances which determine the degree of satisfaction of a user of the service". To put it in less formal words, service quality is the customer’s perception of a delivered service.

With constant growth in the number and complexity of services offered to customers, the impact of network performance on the quality of service will be more complex. It is crucial that service monitoring tools identify and report network-performance issues that impact customer service. Service monitoring tools also must be able to perform a business impact analysis in terms of lost revenue due to service degradation.

Service monitoring tools

Service monitoring tools in general consists of two major building blocks:

  • a powerful data-aggregation engine
  • an end-to-end service-mapping tool

The data aggregator is designed to collect data from a diverse range of sources like UDR, performance data or network alarms in a multi-vendor environment across different network technologies (mobile, ADSL, MPLS, etc.).

The service-mapping tool enables the mapping of performance data onto service-quality data. Key performance indicators (KPI) measure a specific aspect of the performance of either a service resource or a group of service resources of the same type. A KPI is derived from network measurements and is always restricted to a specific resource type. Key quality indicators (KQI) are calculated based on one or more KPIs.

With the service-mapping tool, it’s possible to combine KQIs from multiple key performance indicators (KPIs) across different services. This top-down approach of a service-mapping tool provides several benefits. It helps digital service providers to manage end-to-end quality of service from a customer’s perspective and allows them to reuse key performance indicators and key quality indicators across services and products. Lastly, it helps digital service operators drill down to the service elements that are responsible for quality degradations.


The hierarchical structure of service model

The service model is defined by a hierarchical structure where each item represents an individual component needed to successfully run the service. Each service model item is assigned one or more KPIs and KQIs that are calculated based on collected data. Thresholds and rules can be defined to determine the service model item state. Service model state is defined based on the rules and relationships between service model items. Different actions can be triggered based on threshold violation rules (like network resource reconfiguration).

The defining of KPI and KQI algorithms and service model rules is done in a service model editor by service operating center employees. The process of service model creation can be accelerated by importing product and service definitions from the product and service catalog. Product offerings, customer facing resources and resource facing services defined in the product and service catalog are mapped to individual service model items. Threshold values defined in customer facing services can be used in the service model to determine its state.

An analytics engine responsible for calculating KPIs, KQI and service model states also requires reference data like network inventory, customer and location data or billing data. This information can be loaded to the analytics engine periodically.

The visualization of collected network data and calculated KPIs, KQIs and service model states is done using a single pane glass portal that can display all relevant data for individual services or a group of services. Service operating center employees can select a service and drill down according to a defined service model and assess each service model item state. This information can help them restore service faster or even prevent service from going down. By being proactive, the end customer will have a better experience.


Implementation of service quality strategy improves the Net Promoter Score

After the successful implementation of a service quality management solution, digital service providers are closer to achieving customer experience management goals and improving their Net Promoter Score (NPS).

End customer experience can be measured by considering all the information about all of the customer's interactions with the digital service provider. This information includes billing, usage records, social media interactions and other available information. Combining all this information will enable the digital service provider to measure end customer experience by implementing a customer experience management solution.

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