Forecasting in contact centers : A step by step method to get an accurate forecast

Sammanfattning: Teleopti WFM Forecasts is a tool that can be used in order to predict future contact volumes in contact centers and staffing requirements, both in the short and the long term. This tool uses historical data of incoming contact volumes to perform a forecast on a given forecasting period. Today this tool uses a very simple algorithm which is not always very accurate. It also requires inputs from the customer in some of the steps, in order to generate the forecast. The task of this thesis is to improve this algorithm to get a more accurate forecast that can be generated automatically, without any input from the customer. Since Teleopti has more than 730 customers in more than 70 countries worldwide [3] the most challenging part of this project has been to find an algorithm that works for a lot of different historical data. Since different data contains different patterns there is not a single method that works best for all types of data. To investigate what method that is best to use for some specific data, and to perform a forecast according to this method, a step by step method was produced. A shortened version of this method is presented below.  Remove irrelevant data that differs too much from the latest data.  Use the autocorrelation function to find out what seasonal variations that are present in the data.  Estimate and remove the trend.  Split the data, with the estimated trend removed, into two parts. Use the first part of the data to _t different models. Compare the different models with the other part of the data. The one that fits the second part best in least square sense is the one that is going to be used.  Estimate the chosen model again, using all the data, and remove it from the full sample of data.  Forecast the trend with Holts method.  Combine the estimated trend with the estimated seasonal variations to perform the forecast. There are a lot of factors that affect the accuracy of the forecast generated by using this step by step method. By analysing a lot of data and the corresponding forecasts, the following three factors seem to have most impact on the forecasting result. First of all, if the data contains a lot of randomness it is difficult to forecast it, no matter how good the forecasting methods are. Also, if there are small volumes of historical data it will affect the forecasting result in a bad way, since estimating each seasonal variation requires a certain volume of data. And finally, if the trend tends to often change direction considerably in the data it is quite difficult to forecast it, since this means that it could probably change a lot in the future as well. This step by step method has been tested on plenty of data from a lot of different contact centers in order to get it as good as possible for as many customers as possible. However, even though it has exhibited a good forecast of these data there is no guarantee that it will perform a good forecast for all possible data amongst Teleopti's customers. Hence, in the future, if this step by step method will be used by Teleopti, it will probably be updated continuously in order to satisfy as many customers as possible.