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Taking customers out of the dark with Automated Intelligence: An example

Published: 20 August 2018

Experiencing an outage or service degradation usually results in several disconnected monitoring systems reporting multiple potential causes. Meaningless events drown out the definite root cause of an issue due to lack of visibility and disjointed information, leaving you to only notice something is wrong when your customer logs a support ticket or make a complaint.

First line support staff are supposed to focus on quick issue resolution and reduced mean time to repair (MTTR), but if they’re not equipped with the right tools, this isn’t easy. Unnecessary time is spent on guesswork and trudging through a plethora of information, escalating incidents to second and third line support, creating delays and frustrated customers and staff.

If you don’t want to distort your customers’ perception of the service that you’re delivering, empowering your service management teams and customers with clear, automated monitoring is fundamental. Typically, an effective solution will:

  • Improve the intelligibility of complex monitoring data by displaying all the necessary data but in a format which is easy to understand
  • Provide fully shared visibility of this data, enabling effective communication between you and your customers
  • Have the ability to detects patterns and trends to pinpoint recurring issues
  • Have alert filtering which provides only the alerts you need with fewer false alarms

Let’s look at a typical scenario where automated monitoring aids in quick issue resolution and improving productivity.

John has been experiencing slow WiFi speed at his office in London, so he calls up his service provider, and speaks to Katie, from the first line support team. Both John and Katie are logged onto the same instance of the monitoring tool which has a clear, business-level interface. They can immediately see and agree that there’s an issue and see where it’s arising. Having the same visibility of the data enables a smooth conversation from the start as both parties can agree with each other.

Drilling deeper into the information, which the monitoring tool has filtered by location, and by service, Katie can see the health of the relevant wireless controller over the past seven days, as well as utilisation rates of access points (AP) and the number of clients within each AP. By looking at this information she’s able to conclude that the reason John is experiencing slow WiFi speed is because of the number of clients using the same AP that John is, has almost reached its limit. If further clients were to use the same one, John could expect his WiFi speed to reduce further and he would also receive a customised alert notifying him of the cause (as a precaution). It takes Katie a matter of minutes to get to the root cause of John’s problem and both of them can be satisfied in knowing there is a solution can be sought simply with no time wasted. The monitoring service reports the problem as fixed which, again, is visible to both sides.

Human involvement is still necessary, but with the use of intelligent solutions which clearly visualise relevant data, the output is less overwhelming and it’s much easier to identify what’s happening in the network. Despite significant automation being in place, support teams are still expected to make rapid and important decisions. The right tools aid in sensible and tactical conversations with customers, improve productivity and business processes whilst freeing up time to focus on what’s important such as building customer relationships.

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