Ad revenue optimization in live broadcasting

In live broadcasting, the break lengths available for commercials are not always fixed and known in advance (e.g., strategic and injury time-outs are of variable duration in live sports transmissions). Broadcasters actively manage their advertising revenue by jointly optimizing sales and scheduling...

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Main Authors: POPESCU, Dana G., CRAMA, Pascale
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2016
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Online Access:https://ink.library.smu.edu.sg/lkcsb_research/5150
https://ink.library.smu.edu.sg/context/lkcsb_research/article/6149/viewcontent/AdRevenueOptimizationLiveBroadcasting_mnsc_2016_afv.pdf
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spelling sg-smu-ink.lkcsb_research-61492017-06-06T07:08:48Z Ad revenue optimization in live broadcasting POPESCU, Dana G. CRAMA, Pascale In live broadcasting, the break lengths available for commercials are not always fixed and known in advance (e.g., strategic and injury time-outs are of variable duration in live sports transmissions). Broadcasters actively manage their advertising revenue by jointly optimizing sales and scheduling policies. We characterize the optimal dynamic schedule in a simplified setting that incorporates stochastic break durations and advertisement lengths of 15 and 30 seconds. The optimal policy is a "greedy" look-ahead rule that accounts for the remaining number of breaks; in this setting, there is no value to perfect information at the scheduling stage, and hence knowing the duration of all breaks would not change the schedule. We present heuristics to help solve scheduling problems of even greater complexity. The performance of these heuristics under various scenarios is tested by running simulations calibrated using industry data. The simple greedy heuristic is shown to perform well except when revenues are concave in ad length, in which case the look-ahead aspect of the optimal schedule becomes more important. Finally, we recommend ways for broadcasters to balance their portfolio of booked ads by determining the optimal overbooking level and mix of ads as a function of their associated revenues generated and penalties incurred. 2016-04-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/lkcsb_research/5150 info:doi/10.1287/mnsc.2015.2185 https://ink.library.smu.edu.sg/context/lkcsb_research/article/6149/viewcontent/AdRevenueOptimizationLiveBroadcasting_mnsc_2016_afv.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection Lee Kong Chian School Of Business eng Institutional Knowledge at Singapore Management University live broadcasting advertising scheduling random capacity Advertising and Promotion Management Operations and Supply Chain Management
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic live broadcasting
advertising
scheduling
random capacity
Advertising and Promotion Management
Operations and Supply Chain Management
spellingShingle live broadcasting
advertising
scheduling
random capacity
Advertising and Promotion Management
Operations and Supply Chain Management
POPESCU, Dana G.
CRAMA, Pascale
Ad revenue optimization in live broadcasting
description In live broadcasting, the break lengths available for commercials are not always fixed and known in advance (e.g., strategic and injury time-outs are of variable duration in live sports transmissions). Broadcasters actively manage their advertising revenue by jointly optimizing sales and scheduling policies. We characterize the optimal dynamic schedule in a simplified setting that incorporates stochastic break durations and advertisement lengths of 15 and 30 seconds. The optimal policy is a "greedy" look-ahead rule that accounts for the remaining number of breaks; in this setting, there is no value to perfect information at the scheduling stage, and hence knowing the duration of all breaks would not change the schedule. We present heuristics to help solve scheduling problems of even greater complexity. The performance of these heuristics under various scenarios is tested by running simulations calibrated using industry data. The simple greedy heuristic is shown to perform well except when revenues are concave in ad length, in which case the look-ahead aspect of the optimal schedule becomes more important. Finally, we recommend ways for broadcasters to balance their portfolio of booked ads by determining the optimal overbooking level and mix of ads as a function of their associated revenues generated and penalties incurred.
format text
author POPESCU, Dana G.
CRAMA, Pascale
author_facet POPESCU, Dana G.
CRAMA, Pascale
author_sort POPESCU, Dana G.
title Ad revenue optimization in live broadcasting
title_short Ad revenue optimization in live broadcasting
title_full Ad revenue optimization in live broadcasting
title_fullStr Ad revenue optimization in live broadcasting
title_full_unstemmed Ad revenue optimization in live broadcasting
title_sort ad revenue optimization in live broadcasting
publisher Institutional Knowledge at Singapore Management University
publishDate 2016
url https://ink.library.smu.edu.sg/lkcsb_research/5150
https://ink.library.smu.edu.sg/context/lkcsb_research/article/6149/viewcontent/AdRevenueOptimizationLiveBroadcasting_mnsc_2016_afv.pdf
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