Simulation of Customer Operations with Python
Exploring your customer operation through simulation gives a great understanding of its dynamics.
The six fundamentals of customer operations forecasting
Philip Stubbs outlines six essential elements of forecasting for a customer operation
In praise of real time teams
The impact of real time teams might not always be in the spotlight, but they profoundly influence efficiency and performance.
Using Python for contact centre planning – three application examples
Philip Stubbs outlines three areas where Python can assist with contact centre planning
How accurate forecasting benefits your customer operation
Philip Stubbs describes the benefits that accurate forecasting can add to a customer operation
The human element: how relationships drive better forecasting
Philip Stubbs discusses how interactions between humans are important to achieving forecast accuracy
Forecasting for Customer Operations using Python – how I got started
Philip Stubbs shares how he learned about Python in 2024
Are your service level targets inconsistent with each other?
Problems arising from service level targets that are conflicting…
Key factors influencing resource requirement within customer operations
Eight ingredients that can be included as tweakable assumptions within a weekly model
Estimating resource requirement for customer operations – why it is crucial
Calculating weekly resource requirement for the Capacity Planning process is extremely valuable. Here are seven key functions this practice enables, contributing to maximising CX, commercial performance and employee engagement.
Understanding the causes of service level failure
Philip Stubbs suggests a graphical report that helps pinpoint the causes of service level failure
How a simple chart can help deliver optimum customer operations performance
Study whether each future week will be over- or under-staffed
Recommended Forecasting Books
Two texts to help your forecasting become more accurate and useful
How can an established complex forecasting model be outperformed by a simple one?
Philip Stubbs suggests reasons why a simple forecasting model sometimes outperforms a more complex live model
Do your colleague schedules meet the four objectives necessary for success?
Philip Stubbs outlines the four crucial objectives to achieve when designing a new schedule structure.
How validation can boost the accuracy of your resource model
Philip Stubbs explains the importance of validation for accurate resource modelling
Achieving better forecasting accuracy through error measurement
Philip Stubbs discusses why the measurement of forecast error within an operation plays a crucial step in helping to reduce such error.
How do you know if your forecasting is any good?
Philip Stubbs suggests a method to determine the quality of a forecasting model
About Erlang C
The ErlangC equation is a standard tool in inbound contact centres for estimating the number of advisors needed to meet a service level target, typically defined as answering a set percentage of calls within a specific number of seconds. It works best in environments where every advisor can handle every call, and is usually applied […]