An accurate and optimized capacity plan is essential for effective contact center operations.
Failure to get the plan right based on a number of input variables can have negative consequences on customer and employee experiences, revenue and expense line items. As one of many input factors in the capacity planning process, determining a target Average Handle Time (AHT) or Calls per True Hour (CPTH) for fully proficient agents is crucial. To determine the target AHT or CPTH, use a quartile analysis: a data-driven approach that helps ensure that targets are realistic and achievable.
Here's a step-by-step process to guide you through this method:
1. Data Collection
Start by collecting historical AHT / CPTH data for your contact center. You will need a substantial dataset, ideally covering a significant time period (e.g., 1 year) to account for variations due to seasonality or other factors.
2. Define Fully Proficient Agents
Clearly define what constitutes a "fully proficient" agent in your context. This definition may include meeting specific performance criteria, completing a particular training program, or achieving a certain tenure.
3. Segment the Data
Segment your AHT /CPTH data into different groups or categories that make sense for your contact center. For instance, you might segment based on call types, customer segments, agent tenure, or any other relevant factors.
4. Calculate Quartiles
For each segment, calculate quartiles (Q1, Q2, Q3, and Q4) from the AHT / CPTH data. Quartiles divide your data into four equal parts, providing insight into the distribution of AHT or CPTH.
Q1 (25th Percentile): The value below which 25% of the data falls.
Q2 (50th Percentile): The median, the middle value where 50% of the data falls below and 50% above.
Q3 (75th Percentile): The value below which 75% of the data falls.
Q4 (100th Percentile): The maximum value in your dataset.
5. Analyze the Quartiles
Examine the quartiles for each segment to understand the range of AHT / CPTH. Quartile analysis can help you identify the following:
Q1 (25th Percentile): This represents the lower end of AHT times. You might consider this as an ideal target for fully proficient agents if you want to emphasize efficiency. However, be cautious not to set targets too low (AHT) or too high (CPTH) if it compromises service quality.
Q2 (50th Percentile - Median): This represents the midpoint of your AHT / CPTH data. It's a good reference point for an AHT target, balancing efficiency, and service quality.
Q3 (75th Percentile): This indicates the AHT time below which 75% of your data falls. You can use this as a target if you want to prioritize service quality over efficiency.
Q4 (100th Percentile): This represents the maximum AHT time in your dataset. While it's less commonly used as a target, it can be a reference point for handling exceptionally complex cases.
Note: Quartiles are inverted for CPTH: A higher CPTH is generally better than a lower CPTH provided service quality and first contact resolution are not impacted.
Sample CPTH Quartile Analysis
Calls per True Hour (CPTH) = 3600 / AHT
6. Choose the Appropriate Quartile
Select the quartile that aligns with your contact center's goals and objectives. This choice depends on whether you want to emphasize efficiency, quality, or a balance of both.
7. Consider Industry Benchmarks
Compare your chosen quartile-based target to industry benchmarks or best practices to ensure competitiveness.
8. Monitor and Adjust
Continuously monitor AHT / CPTH for fully proficient agents and compare it to the chosen quartile-based target. Adjust the target as needed based on performance trends and changing circumstances.
9. Communication and Feedback
Communicate the AHT / CPTH targets derived from quartile analysis clearly to all relevant stakeholders, including agents, supervisors, and management. Encourage feedback from agents to ensure that targets remain realistic and achievable without compromising service quality.
10. Continuous Improvement
Maintain a culture of continuous improvement and regularly revisit your AHT / CPTH targets as your contact center evolves or customer expectations change.
Quartile analysis provides a robust and data-driven method for setting AHT / CPTH targets that consider the distribution of AHT /CPTH in your contact center. It helps ensure that your targets are based on real performance data and align with your operational goals.
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