The delivery of excellent CX is driven by a variety of factors, but the ability to handle customer service and support issues quickly and efficiently cannot be overlooked, particularly in the hyper-competitive telecommunications sector. The always-on, digitally connected telecommunications customer simply will not tolerate a lack of responsiveness, nor will they put up with having to repeatedly put in service tickets.
That is why it is imperative to ensure that a robust back-end system and its supporting technologies are put into place so that the service assurances made in the company’s advertising, marketing, and customer service policies can be delivered upon.
Telecom operators must be able to quickly and efficiently resolve any issues that arise, even if a single issue winds up triggering a range of tickets or alarms. As a result, CX agents need a clear understanding of how each of these tickets relate to each other and impact services and customers—and which issues are the most urgent to address.
Several features or functions should be deployed to help manage the flow of support tickets and issues, while simultaneously helping to support CX initiatives. But automated ticket correlation is a relatively straightforward tool that can be used to assist back-end technical staff and CX agents, and promote more transparency and accountability to customers themselves.
Automated ticket correlation can be used to ensure that related service tickets can be correlated together. This helps ensure that technical staff is properly coordinated to address and remedy the issue. It can also help agents remain informed, so they can keep customers updated on the progress of an open ticket, and provide estimates on when the issue is likely to be resolved.
For example, if a fiber cable is severed, it is likely that several tickets will be generated, including at the resource layer, as well as at the customer level, where it is likely that hundreds or thousands of tickets may be generated from that single event. The use of automated ticket correlation can ensure that these tickets are correlated and understood as stemming from the same event or issue.
This correlation can also help a CX agent prioritize which ticket or tickets to address first (in the example, it is better to address the underlying fiber cut issue first, since the customer service tickets stem from that issue). This will help the organization quickly prioritize their activities to reroute traffic, as well as prioritize customer notifications of the outage while the fiber is repaired.
In addition to providing insight to prioritize tickets for service, automated ticket correlation can be used to analyze and cluster tickets generated around the same time, event, or location so it can be easier to identify potential common causes, as well as ancillary issues that may have arisen.
To extend the example, a fiber outage may be generating additional customer support calls to the contact center, which may be impacting response times with live agents. By correlating the expected time-to-resolution for a specific issue with staffing levels, additional live agents can be activated to handle the additional demand, or additional direct-to-customer communications can be launched to explain the situation to customers, and offer specific goodwill tokens.
Initially, operators likely will need to develop and deploy specific rules to dictate how tickets should be classified, clustered, and correlated, as well as define the specific patterns that may indicate specific correlations.
Ultimately, the use of machine learning (ML) to understand patterns across tickets and update this cognition over time is a more sustainable approach. This will allow back-end staff to migrate their focus from time-consuming incident prioritization and analysis, and concentrate on more complex remediation actions that require human intervention. In turn, customer-facing agents will be provided with more visibility into the source of issues, the expected impact, and greater insight into when the issues may be resolved, which they can relay to customers.
The end goal is to create a more streamlined approach to managing back-end issues that often directly impact front-end communication issues that can negatively impact the customer’s experience.
Author Information
Keith has over 25 years of experience in research, marketing, and consulting-based fields.
He has authored in-depth reports and market forecast studies covering artificial intelligence, biometrics, data analytics, robotics, high performance computing, and quantum computing, with a specific focus on the use of these technologies within large enterprise organizations and SMBs. He has also established strong working relationships with the international technology vendor community and is a frequent speaker at industry conferences and events.
In his career as a financial and technology journalist he has written for national and trade publications, including BusinessWeek, CNBC.com, Investment Dealers’ Digest, The Red Herring, The Communications of the ACM, and Mobile Computing & Communications, among others.
He is a member of the Association of Independent Information Professionals (AIIP).
Keith holds dual Bachelor of Arts degrees in Magazine Journalism and Sociology from Syracuse University.
