Merlang Improves Predictability

By Dennis Cox, Pipkins UK

Variability vs. Predictability

The volume of work arriving in a contact center, including calls, emails, correspondence etc., is quite variable and, some would argue, unpredictable. Indeed, if we looked at a history of work arriving in a typical contact center, this belief might appear to be true. The volume of work does indeed vary over a day, from one day to the next and week on week — facts which may give the impression that the accurate WFM forecasting of future workloads is an impossible task. In reality, using the right tools can help eliminate unpredictability.

However, variability should not be confused with unpredictability. How much of this work volume is actually predictable, given the right tools, and what are the consequences of inaccurate forecasting?

Getting It Wrong

There are two simple effects of getting the forecast wrong:

1. Forecasting Too High

• you have too many agents scheduled
• which means low occupancy
• which leads to agents getting distracted/bored
• which lowers agent morale and quality work, resulting in
• costing your company money.

2. Forecasting Too Low

• too few agents are scheduled, contributing to
• poor customer service, long waiting times, or
• agents being overworked
• resulting in low morale and poor quality work leading to
• high number of callers abandon — possibly to go elsewhere; which means
• lost business, and
• costing your company money.

The amount of lost revenue through callers abandoning can be significant, particularly in a sales environment. Consider the average revenue generated per sales call — then multiply this by the number of abandoned calls you recorded last month. The answer would probably justify the cost of an accurate WFM forecasting tool on its own!

Accurate WFM Forecasting. Keep these tips in mind to maximize the accuracy of the forecasting tool.

1. Beware of averages: While forecasting averages is a safe bet, it is not likely to be the most accurate.
2. Give the forecaster some data: The more data, the better. If the tool cannot process more than a few weeks of data, its accuracy will be compromised.
3. Have realistic expectations: The tool’s predictions can only be based on what has happened historically and on what it is told will happen in the future.
4. Understand how your forecasting tool works:

• How much data can it store /use?
• Can it account for inflation due to abandoned calls?
• Can it recognize seasonal trends and growth trends?
• Can you input special event information and apply correlation factors?
• Does their AI component reliably analyze the data to determine if a pattern is present in the data?
• How does it accomplish all these things?

A poor forecast can result in high staffing costs and lost customer revenue, but forecast accuracy depends on many factors. The key to accurate WFM forecasting is ensuring that the forecasting tool has as much information about what happened in the past and what you expect in the future, and that it will allow you to input this information and make proper use of it.

Erlang versus Merlang^®

For maximum efficiency, your software algorithms must incorporate busies and abandoned calls. Merlang® algorithms improve upon traditional Erlang-C algorithms by eliminating the assumptions that queues are infinitely long, callers never abandon the queue, and all calls have to be answered by one group of agents.

Merlang equations offer the following advanced features that are not available from software packages that use Erlang-C:

• Correct modeling of queue sizes — allows for the prediction and limiting of the number and percent of busies.
• Modeling caller abandon rates — allows for the prediction and limiting of the number and percent of abandons.
• Calls handled, a complement of busies and abandons, can be used as a service level type.
• Occupancy is calculated, and caps on it may be taken into account when determining agent requirements.
• The traditional way of expressing a service level as a percent answered within a given time.
• Accounts for indirectly occupied time of agents (bathroom breaks, supervisor queries, etc.).
• Accounts for retries of both busies and abandons.
• Accounts for skill group queue assignments, queue priorities, and overflow.

A Final Word

Companies today do not have the luxury of making mistakes. Ensuring your workforce management system produces accurate forecasts is your most important consideration. Educate yourself on Merlang® algorithms and how they improve upon traditional Erlang-C algorithms. Make sure your company is using the best fit.

A privately-held, American-owned company, Pipkins, Inc. was founded in 1983. Headquartered in St. Louis, Missouri, the firm is a leading supplier of workforce management software, providing sophisticated, cloud-based forecasting and scheduling technology, as well as other fully integrated solutions for performance management, real-time adherence, time and attendance tracking, task tracking, compliance monitoring, mentoring, collaboration, and more. Visit www.pipkins.com for more information.