Revenue Optimization in Live Television Broadcasting

• Main Authors: CRAMA, Pascale, Aravamudhan, Ajay Srinivasan, POPESCU, Dana
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2012
• Subjects: live broadcasting; advertising; scheduling; random capacity; Advertising and Promotion Management
• Online Access: https://ink.library.smu.edu.sg/lkcsb_research_smu/82; https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1081&context=lkcsb_research_smu
• Institution: Singapore Management University

In live broadcasting, the break lengths available for commercials may not always be fixed and known ex ante (e.g., strategic and injury time-outs are of variable duration in live sport transmissions). Because advertising represents a significant share of the broadcasters’ revenue, broadcasters actively manage that revenue by jointly optimizing their advertising sales and scheduling policies. We characterize the optimal dynamic schedule in a simplified setting that incorporates stochastic break durations and advertisement lengths of 30 seconds and 15 seconds. The optimal policy is a greedy look-ahead rule that takes the remaining number of breaks into account. Under this setting, we find that there is no value to perfect information at the scheduling stage and knowing the duration of all the breaks will not change the schedule. When we incorporate diversity constraints (i.e., two ads from the same advertiser or for competing products cannot be shown during the same break), we characterize the optimal policy for a restricted set of stochastic break lengths. This policy combines the logic of the greedy look-ahead rule with the necessity to maintain an acceptable level of diversity in the ad portfolio. Finally, we also present heuristics that can be used to solve scheduling problems of greater complexity, and we recommend ways for broadcasters to balance their portfolio of booked ads. We run simulations to test the performance of the heuristics under various scenarios and find that two heuristic: myopic greedy and dynamic modified certainty equivalent (DMCE) perform close to optimal.