Handling Ticket Outliers in Operational Settings Fin Analytics
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Handling Ticket Outliers in Operational Settings


The top 5% of cases usually take a massively disproportionate amount of team time and engagement. What are best practices and benchmarks for handling the hardest cases?


On average, the longest 5% of cases consume 20-30% of most customer service teams’ time and engagement.

Time-intensive edge cases are an unavoidable reality for CX teams. In an ideal world, 5% of cases should take 5% of your team’s time. In reality, that is obviously not the case. Customers will have complicated questions, agents will need to take longer on some issues than others, and outliers will always crop up.

The question then becomes, what are acceptable benchmarks for how much time your customer service team should be spending on these outlier cases, and how can you ensure your team’s time and engagement is spent efficiently?

Benchmarks for Time Spent on Outliers

According to the benchmarks Fin Analytics has generated from working with a number of operations teams, 20% of agent time is spent on P95 cases. That is, the longest 5% of cases. This number varies from team to team, but high performing teams typically hover around the 20-25% range.

No matter what your team’s time breakdown looks like, the goal should always be to drive for greater efficiency, starting with eliminating these outliers where possible. So how do you begin to drive that number down?

Identifying Outliers

One of the first things we always do with Fin Analytics customers who want to reduce the cost of time spent on outliers is build a custom QA Review Priority Queue. This is absolutely essential, because if you rely on random sampling, then by definition, only 5% of the cases your QA team reviews will be in the top 5% of slowest cases. Yet, as we have said, these cases typically consume 25-35% of your team’s time, so any insights and improvements you make to this band of cases will have a huge payoff.

We work with organizations to sort their custom QA Review Priority Queue by a heuristics that matter most to their goals, giving higher priority to things like cases with low CSAT scores, cases within the highest variance Case Types, and outlier cases from the p95+ effort band.

Once you have ensured your QA team is focusing their valuable time and effort on the cases that present the biggest opportunities for improvement, the next step is to look for things that the outlier cases have in common.

  • Is there a certain workflow that all these outliers are associated with?
  • Are certain people setting up tasks incorrectly, causing those cases to take much longer to resolve than others?
  • Is there a certain tool that your team is using that highly correlates with outliers?

After you’ve identified a few common root causes across outlier cases, you can start to have an idea of where to focus, and begin to tackle those root causes.

Each CX organization is different, but some of the most common issues we’ve seen driving up handle time are:

  • Workflow Issues. If a certain workflow corresponds with a high number of outliers, you may need to redesign that workflow. Perhaps the workflow can be broken down and simplified, or separated into different queues.

  • Unclear Instructions. A workflow may be undefined, missing templates, or perhaps doesn’t time-box the amount of time agents should be spending, which costs agents time on research and composition, in an effort to be precise.

  • Personnel Issues. If a certain number of people are driving a large percentage of those outliers, it’s worth a conversation to understand why. Front-line managers can shadow work sessions and find ways to help these agents get faster or more efficient.

  • Training. It could be that your team simply needs better training, or re-training, on how to handle certain cases, to drive down handle time.

These are just a few possible causes for why a support case can become an outlier. Realistically, no CX team will ever reach a state where all cases are handled in equal time, but understanding the root causes of these outliers and systematically addressing each contributing factor within your control will make your team more efficient as a whole.

For more in-depth reading on how to eliminate outliers to drive continuous improvement, check out our recent post, Driving Success Metrics with an Operations Flywheel .


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