Simulation Adjustments

Simulation adjustments enable you to factor in significant anticipated or past traffic deviations in your forecasts. For example, if you published videos that caused a significant increase in traffic compared to your normal traffic, then this extra traffic is going to influence all forecasts. To compensate for this traffic deviation, you mark the extra traffic in a simulation adjustment and the simulation engine then compensates for it.


Take the following considerations into account when using the simulation adjustment functionality:
  • Past traffic deviations only influence forecasts if they occurred in the past two weeks, because that is the sample window used for forecasts.
  • Traffic in the adjustment is defined as the number of ad breaks, which means that you need to enter the adjustments as such. For example, from your own metrics, you are able to see how many more content playback starts occurred in a certain category. From the insertion policy defined in Pulse on this category, you know that pre-roll and mid-roll ads are shown. This means that you need to enter the simulation adjustments as twice the amount of extra playback starts, because you have two ad breaks for this category.
  • If you create future adjustments for traffic you do not have, then they do not affect any simulations because there is no sample traffic yet from which simulated traffic can be generated. This may occur when creating adjustments for a specific category. For example, a new category was created today for, and only for, a special show, which is going to be available for viewing in two weeks. You estimate that the show is going to generate 100000 extra traffic and you enter this as a future adjustment for this specific category. Although the event appears in the graph, forecasts that include the dates of the event are not affected by it.
  • Similarly, if you create past adjustments that exceed the amount of actual traffic, then the part of the adjustment not covered by actual traffic does not affect simulations. For example, a special, one time broadcast occurred on Wednesday last week. This broadcast brought a lot of traffic to your site and caused a spike in your traffic pattern. You estimated 1000000 extra traffic, but in reality only 700000 extra traffic was recorded for the category of the broadcast. Although the full amount (1000000) of the past event appears in the graph, forecasts using the sampled traffic are only adjusted for 700000 traffic.
  • You can only select a category that is a leaf in your category tree when entering adjustments for a specific category.