overview

Issue

How ForeScite™ Handles the Issue

Forecasts must be able to distinguish differences in underlying patterns between contact types to be inaccurate.

ForeScite™ recognizes that all contacts are not alike; each contact type has its own set of unique drivers. Billing contacts behave differently than advertising contacts, which behave differently than contacts to initiate or disconnect service. And, don’t forget about events, those one-time occurrences that can skew historical data. ForeScite™ addresses disaggregates contacts, provides customized forecasts for each of them and then aggregates them for you.

Forecasts must mitigate differences in historical service performance to be accurate.

ForeScite™ utilizes a proprietary algorithm to take historical data and make it comparable despite wide variations in service performance between time periods. ForeScite™ creates an “equivalent contact” comparison which determines what contact volumes would be if service levels were maintained at targeted levels over the historical comparison periods. Using this more accurate comparison basis increases the accuracy of forecasts.

Comparison of time period with different number and type of days cause forecast inaccuracies.

ForeScite™ accounts for variations in the number of days in a month (contact volumes should be different between February and March) and the types of days in a month (March has five Wednesdays, Thursdays and Fridays and April has five Saturdays and Sundays) to increase contact forecast accuracy.