Volume Forecasting - WFManagement

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Sunday, 29 January 2012

Volume Forecasting


Today I am going to specifically talk about volume forecasting, a critical section for deriving a workload forecast that can be used to plan against.

The workforce planning forecasting process, like any planning process, is one part art one part science. It is an art because sometimes the accuracy of your forecast will be as a result of your judgement and experience. It is a science because there many step-by-step mathematical processes that can be used to turn raw data into predictions of future events.

For me even if you are lucky enough to have software solutions that help with this challenge, it is critical for you to understand these calculations so you can quality check the inputs/outputs and secondarily so you are able to have an educated discussion with business leaders. After all, if you are not able to explain how you came up with the forecast in the first place how do you expect operations to buy-in and support your plan…

Another important point is knowing where to stop refining your method/output, this being a critical part of the art of forecasting that applies to all of the below, i.e a question any good forecaster should be constantly asking is whether the effort expended at each stage is worth it?, I touch on FVA  as method to help with this if you are looking for assistance - click on this link 

There are three main forecasting methods used for Customer Contact Centre forecasting:
  • Time-Series –  a method based solely on past history in order to extrapolate forward. If you want to know more about this method I have written a article on the subject (click here to read that)
  • Cause & Effect (Causal) – a method best suited for situations with regularly characterised ups and downs due to causal factors or drivers. 
  • Guesswork – yes sometimes when there is a lack of accurate data gut feel is all you have. 
Data Gathering The source of your data of course depends upon your technological set-up and type of work i.e. Front Office – Call Centre or Customer Back Office – processing unit. However, the point I would like to make here is about data quality by cleansing outliers, an outlier being any data point that falls outside of the expected range of the data. Ignore outliers at your peril, they will have a significant adverse impact upon the accuracy of your forecast. As any stock broker will tell you history is not necessarily a true reflection of the future. So make sure you look out for abnormally low or high numbers, missing information and trends that you know will not repeat.

Devising a Prediction for Monthly/Weekly Volumes - it is usually wise to start at a high level of granularity and break this down to ever lower interval levels. Monthly and weekly levels are often used as part of the the long term capacity planning (two great articles on this if you want to know more about capacity planning Part 1 & Part 2) although there is a strong argument that capacity planning should be bottom up - but again for another day. Best practice is typically a mix of time-series and causal drivers, for example history might suggest the Month of November is going to be quiet but if you know that there is going to be a big marketing drive in that month then you will also know that history for this month is not going to be a particularly true reflection of the future.

Refine to a Daily and 30/15 minute interval forecast
The aim at this stage is to devise an intra-day volume pattern that represents customer opening hours for each 60/30/15 min interval during the day as well as each different day of the week i.e. it is likely that Monday's in most contact centres are busier and it likely that the intra-day pattern follows some sort of lop-sided M curve. Again as per above best practice is typically a mix of time-series and causal drivers.

Refine for Special Events
There are an infinite number of potential events that might disrupt a forecast pattern, especially as the granularity of forecast interval drops down ie. say at 15 minute interval. Picking your battles is important, (as above the FVA  method can help with this). However, allowing for obvious changes in customer behaviour is critical. For example a major event like the super bowl or the world cup final is likely to mean less customer demand during this period, but more subtle special events might also include the changing of the clocks or student term time schedules.


Hope as a high level overview this helps conceptualise, my aim will be to expand upon the above with dedicated articles as time goes on... until then I will leave this thought with you...

At times getting to an accurate forecast can be difficult, especially when there are some who believe you possess a crystal globe - so for those frustrated forecasters out there... the first rule of forecasting is that all forecasts are wrong (its impossible to be 100% correct 100% of the time) - forecasting is like trying to drive a car blindfolded and following directions given by a person who is looking out of the back window.