Guillaume Roels of the Anderson UCLA created a 2008 case study on ad serving used in his MBA courses.
The case studies a 90 day period in the life of an adserver matching supply and demand. There are three associated data elements:
- Traffic data for the four inventory zones. This gives expected "supply" on a daily basis in the form of ad unit requests (impressions) per zone per day on average.
- A set of booked orders with flight dates, budget and impression allocation limits per zone.
- A set of proposed orders with the same data elements as orders.
- How much daily or weekly surplus/unsold inventory is available per zone?
- How many of the booked orders will be completed at a given level of supply?
- How much total revenue results from fully completed orders?
- How much total revenue results if partial credit is given for incomplete orders?
- How many of the proposals should be accepted such that they will complete?
- If the orders and proposals were treated equally which of the total set would complete?
- How much revenue results given either completed and/or partially complete orders?
- (advanced) Is there an optimal serving schedule (other than price order) that would result in more total revenue or more completed orders?
- ASAP pacing of orders versus even pacing of volume per day across the flight dates.
- Which traffic level to use per day (low/baseline/high).
- The mechanism to decide the priority of orders to be served in a given day/zone.