Understanding Skewed Data in Customer Service Reports
Understanding your customer service data is crucial for making informed decisions. However, skewed data can paint a misleading picture, leading to poor strategies and inefficiencies. One major culprit is how outliers affect key metrics like Average Handling Time (AHT) and Median Handling Time (MHT). In this post, we’ll break down how skewed data impacts your reports and how to interpret it correctly. Let’s dive in!
The Problem with Skewed Data
As a customer service manager, team leader, or business owner, you rely on reports to assess performance. However, skewed data can mislead you, leading to incorrect conclusions and ineffective decisions. One major factor that distorts data is the Average Handling Time (AHT)—the amount of time it takes to resolve a customer service ticket from creation to resolution.
Imagine a scenario where tickets have widely varying resolution times: some take just a few minutes, while others take several hours or even weeks due to forgetfulness or system errors. If you don’t account for extreme outliers, your reports will paint a misleading picture of team performance.
Average Handling Time vs. Median Handling Time
Two key metrics are used to analyze handling times:
Average Handling Time (AHT): The total sum of all ticket resolution times divided by the number of tickets. This metric can be heavily influenced by extreme cases, making your data appear worse (or better) than it truly is.
Median Handling Time (MHT): The middle value when all ticket resolution times are ordered from shortest to longest. This metric eliminates extreme outliers, providing a more accurate representation of typical handling time.
Example Calculation:
Suppose we have ticket resolution times (in hours): 1, 2, 2, 0.03 (2 minutes), 16, 0.02 (1 minute), 2, 2.5, 0.03 (1 minute), 0.05 (3 minutes), 840 (5 weeks).
AHT Calculation: Sum of all times ÷ 11 = 173 minutes.
MHT Calculation: Excluding extreme outliers, the median is approximately 84 minutes.
This shows that AHT can give a distorted perspective, whereas MHT provides a more realistic view of performance.
Why This Matters for Your Business
If you rely solely on AHT, your team’s performance might seem far worse than it actually is, leading to unnecessary changes or stress. Conversely, ignoring outliers completely could mean missing critical inefficiencies in your system.
Key Considerations:
Ticket Distribution: If your team handles a mix of chat, phone, and email tickets, resolution times will vary significantly.
Identifying Problem Areas: High AHT may indicate inefficiencies, whereas MHT allows you to see typical performance trends without distractions.
Balancing Insights: AHT is useful for diagnosing system-wide issues, while MHT helps track day-to-day team productivity.
Best Practices for Managing Skewed Data
To make the most of your data, follow these best practices:
Use Both AHT and MHT: AHT highlights overall inefficiencies, while MHT shows standard performance.
Identify Outliers: Investigate extreme cases (e.g., forgotten tickets) to determine if systemic issues exist.
Segment Data by Channel: Compare live chat, email, and phone support separately to better understand performance.
Monitor Trends Over Time: A single report is not enough—track handling times over weeks and months for accurate analysis.
Use Automated Alerts: Set triggers for tickets that exceed normal resolution times to prevent outliers from skewing future reports.
Conclusion: Making Informed Decisions
Skewed data in customer service reporting can lead to poor decision-making if not carefully analyzed. By understanding the difference between Average Handling Time and Median Handling Time, you can gain clearer insights into your team’s actual performance.
AHT helps pinpoint systemic issues, while MHT offers a more accurate day-to-day measure. Using both metrics ensures balanced, informed decision-making, helping you improve efficiency while maintaining realistic performance expectations.
By incorporating these insights, you can optimize customer service operations and ensure data-driven success. Always remember: don’t rely on a single metric—context is key!
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