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How To Improve Capacity Planning Accuracy

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, the learning curve concept helps in predicting how the performance of new hires will improve over time, which directly impacts capacity planning accuracy. It is important to note that learning curve analysis is not a once-and-done exercise but is a dynamic analysis tool that should adapt as new data becomes available.


The new hire learning curve essentially shows the relationship between learning or experience and the time taken to perform a task. As employees gain experience, they become more efficient, leading to reduced handling times and increased productivity, eventually achieving the target level of proficiency.


How to Determine the New Hire Learning Curve


1. Collect Data


Start by collecting data on the performance of new hires over a time (e.g., over a period of 1 – 2 years, or a minimum of 20 new hires). This data could include metrics like average handling time, resolution rates, and quality. This is important because it is necessary to determine the optimal performance target for a fully proficient agent.


2. Track Time and Performance


Using data from the WFM platform, correlate the production start date of an employee with their weekly or monthly productivity as measured with AHT. Tracking should be consistent and objective to ensure accurate results.


3. Plot the Learning Curve


For visual acuity, plot the learning curve graphically. On the graph, time is represented on the x-axis, and performance metrics (such as average handling time) are represented on the y-axis.


4. Pinpoint the Initial Performance Point


The learning curve starts with the initial performance point, where new hires have relatively longer handle times and potentially higher error rates due to their lack of experience.


5. Calculate Rate of Improvement


As employees gain experience, their performance (generally) improves, resulting in a downward-sloping curve. However, highlight that the rate of improvement isn't constant. Initially, it might be steep, but it gradually levels off as employees approach their optimal performance.


6. Determine Maximum Potential Improvement


The theoretical point at which agent performance reaches its maximum potential improvement is referred to as the asymptote. This is a critical point for capacity planning because it helps estimate when new hires will become as efficient as experienced agents.



How to Interpret the Learning Curve


1. Identify Optimal Performance


By analyzing the curve, you can identify the point where the learning curve levels off which indicates when new hires have reached a performance plateau.


2. Predict Future Performance


Capacity planners can use the learning curve to predict how new hires will perform in the future. This prediction of new hire performance when weighted with the expected performance of existing experienced agents informs staffing requirements and ensures that the contact center has the right number of agents to handle the expected work volume to achieve a service standard.


3. Make Training and Onboarding Adjustments


Insights from the learning curve can also help to refine training and onboarding processes. If there are areas where new hires struggle consistently, where improved performance lags over too long a period, then adjustments can be made to training materials and methods, nesting period and other post-training support.


Case Study


A multi-site utility company with over 1500 front-line agents successfully applied the learning curve concept to improve capacity planning accuracy in contact centers. They gathered and plotted the appropriate data for 39 classes across 4 sites with a total of 570 new hires to determine the new hire learning curve and conducted a quartile analysis to determine the target level of proficiency.


Using Cinareo, they were able to develop a forward-looking weighted average handle time rather than using historical average handle time (which is often is less accurate depending on the level and variability of attrition rate in the contact centre) as input into their capacity plan.



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Want to prioritize workforce planning with an effective tool?

Cinareo complements any WFM platform and sets a new standard for strategic and tactical capacity planning and decision support for multi-channel contact center environments.



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