InterComms :: International Communications Project
  Intercomms Issue 22
Issue 22 Articles

Solvatio logoTriple Play Support Automation

InterComms talks to Solvatio about how they are improving subscriber experience and reducing Opex

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Automated Customer Incident Resolution
Automated Customer Incident Resolution
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Customer support efficiency, generally measured by MTTR and cost per call, is increasingly challenged by the complexity of troubleshooting triple play services. Support organizations and their helpdesk agents get literally flooded by a growing number of automated data sources (from service outage monitoring via sophisticated physical line testing all the way to remote scanning of home device configurations), that presumably enable rapid identification of even exotic failure locations. Reality looks very different though: most support departments find themselves struggling with degrading first call resolution rates, prolonged MTTR and rising cost per support call, even for extended periods of time following the market launch of a new service offering. In a nutshell: Growing complexity of communication services jeopardizes customer problem handling efficiency and subscriber experience.

In search of new concepts, allowing them to keep pace with increasing service complexity, a growing number of customer support departments decided to leverage the potential of smart troubleshooting automation. The idea behind the concept: establish a powerful set of automated support flows, rapidly designable and deployable for runtime execution, thus assuming the complex task of well-structured root cause analysis and fault resolution while reducing support agent ‘freestyle’ to a minimum. Innovative solutions for troubleshooting automation do not only ensure that smart diagnostic decision-making is consistently applied across all customer support touch points (i.e. self service, helpdesk, back office and field service etc.) but also provide the basis for continuous improvement of future troubleshooting quality. Systematically applied diagnostic procedures, automated data interpretation and consistent collection of relevant troubleshooting details across ALL support cases prepare the ground for statistical analysis and continuous diagnostic improvement.

The results are impressive: dramatically improved efficiency in customer problem handling (e.g. reduced agent interaction, shorter MTTR, dropping number of field calls) combined with greater agility of customer support departments when adapting to changing support requirements (i.e. introduction of new communication services and technologies, constantly accompanied by emergence of additional diagnostic data sources).

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