Improving issue identification and resolution in the globally distributed contact center

Issue identification and resolution in globally distributed call center
In this article, we’ll share why data is critical to improving issue identification and resolution in the globally distributed contact center.

As discussed in Why your globally distributed contact center depends on access to clean data, quality data across your operation underpins the effectiveness of your contact center. Real-time data insights can drive better business decisions, increase productivity, and elevate service quality and customer satisfaction rates.

But did you know that quality data also can help you find and resolve issues faster—and even prevent some from ever happening? In this follow-up post, I’ll share how globally distributed contact centers can leverage data to improve issue identification and resolution.

Intelligently designed metrics

The ability to set thresholds is vital to issue identification and resolution. And this means using intelligently designed metrics to define what constitutes an issue.

For example, let’s say that you’re a cellular provider. Your service level agreement specifies that 95% of calls are answered within five minutes, and 90% of issues are resolved in the first call. To ensure you meet these targets, you need to know when you’re at risk of missing them. Intelligently designed metrics enable you to define the thresholds that alert you to potential issues. 

Automated issue identification

If you’re doing manual work to identify issues, you’re overdue for some built-in automation. Not only is automation more efficient than having humans perform this work, it’s also far more timely and accurate.

Case in point: say an issue pops up in one of your remote centers. The responsible person has stepped away from their desk and doesn’t see it until 20 minutes later—or worse, misses the issue altogether, negatively impacting service levels.

Automation, on the other hand, never steps away from the desk. And while a human might misinterpret data and send teams to chase the wrong issue, automation is far more likely to make the proper diagnosis.

Monitoring 100% of data

Not only do you need access to data across your call centers—you must monitor every system that feeds into your ecosystem.

Going back to the cellular service provider example, let’s say you suddenly see a massive spike in call volumes. Your call center data is clean, and you’re scrambling to pinpoint the cause. Meanwhile, unbeknownst to you, the retail point-of-sale systems are down across the west coast, prompting in-store representatives to call into customer care to activate phones. Having access to that data would have saved you valuable time.

Defining issue resolution

When tackling an issue, it’s important to know that you’ve addressed the root of the problem and not just a symptom. For example, perhaps you resolved a spike in call volume with additional agents. But have you determined what caused the spike and will it happen again? This is why you need to establish clear metrics that define issue resolution—such as recurrence within a specific timeframe.

You also need very clear processes on how to achieve resolution. The most successful organizations have a runbook that saves them the time and frustration of the trial-and-error approach. They have a checklist that they follow immediately if something goes wrong, much like an airplane pilot. Clear, repeatable processes drive efficiency.

Discover opportunities to improve issue identification and resolution

Operating a globally distributed contact center has become increasingly complex. Access to quality data across the business streamlines your ability to identify and resolve issues and meet service level requirements. For a more holistic understanding of your organization’s strengths and weaknesses in this area, I highly recommend completing this free survey.

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