Workforce management has four creation method options when creating a short-term forecast. Each option has specific uses, capabilities, and requirements.

Method Overview Pre-requisites
Automatic Best Method

This AI powered forecasting method is the most sophisticated methodology offered in workforce management. It includes:

  • Built-in, automated capabilities for historical data cleanup
  • Outlier and calendar effect identification
  • Pattern detection including seasonality and trends
  • Best-of-best modeling to select from 20+ methodologies including ARIMA, WM, Decomp

Plus, if a custom model based on multiple methodologies that are equally weighted produces a better result than a single model, the forecast is classified as ensemble. You can configure this creation method to create forecasts from one to six weeks in length. For more information, see ensemble forecasting.

Note: With Genesys Cloud EX, forecasting based on data injected from an external system should always be performed using the Automatic Best Method. While the Weighted Historical Index might work in most cases, when the weighted historical index calculation is performed, any conversation data older than one day is permanently cached and any data before that time that has not yet been injected are permanently excluded. If there are any delays with injecting data from the external system, using Weighted Historical Index results in inaccurate and unreliable forecasts due to incomplete data.

This method requires historical data in Genesys Cloud. While Automatic Best Method works with as little as one week of historical data, it is much more powerful with more historical data. Specifically, to determine daily arrival patterns for scheduling, this methodology looks at up to 90 days of historical data. For seasonality detection, a full season or multiple seasons of data produce the best results.

Note: For volume and AHT, this method uses all historical data.

Weighted Historical Index This method allows the forecaster to weight, at a daily level, one or more weeks of historical data. It uses the resulting weighted average, by day, to output a forecast one week at a time. 

This method requires historical data in Genesys Cloud. However, it does give the option to import data from outside the platform. Because of the flexibility of this creation method, customers often use it when a lot of manual forecast manipulation is necessary.

Weighted Historical Index with Source Data Import This method is the same as Weighted Historical Index except that it asks you to import data from a file instead of using data in the Genesys Cloud platform.

This method requires a data file with at least one historical week of data in it to use for the weight averaging process. New Genesys Cloud customers who do not have historical data on the platform often use this method.

Import Forecast

This method is for customers who: 

  • have an existing forecast process outside Genesys Cloud and want to use forecasts created elsewhere. For example, let’s say that the Genesys Cloud customer is an outsourcer who staffs to forecasts that their customers create. They can load their customer’s forecast into this method and then generate a schedule based on that forecast.
  • want to heavily modify a forecast created using the Automatic Best Method. They can export an existing forecast, modify it in excel, and re-import it into a new forecast using this method.
This method requires a file containing a forecast that meets the file specifications. It does not require historical data.