Optimal budget allocation strategies for real time bidding in display advertising

Author(s):  
Pavankumar Murali ◽  
Ying Li ◽  
Pietro Mazzoleni ◽  
Roman Vaculin
2018 ◽  
Vol 55 (4) ◽  
pp. 489-506 ◽  
Author(s):  
Courtney Paulson ◽  
Lan Luo ◽  
Gareth M. James

In today's digital market, the number of websites available for advertising has ballooned into the millions. Consequently, firms often turn to ad agencies and demand-side platforms (DSPs) to decide how to allocate their Internet display advertising budgets. Nevertheless, most extant DSP algorithms are rule-based and strictly proprietary. This article is among the first efforts in marketing to develop a nonproprietary algorithm for optimal budget allocation of Internet display ads within the context of programmatic advertising. Unlike many DSP algorithms that treat each ad impression independently, this method explicitly accounts for viewership correlations across websites. Consequently, campaign managers can make optimal bidding decisions over the entire set of advertising opportunities. More Importantly, they can avoid overbidding for impressions from high-cost publishers, unless such sites reach an otherwise unreachable audience. The proposed method can also be used as a budget-setting tool, because it readily provides optimal bidding guidelines for a range of campaign budgets. Finally, this method can accommodate several practical considerations including consumer targeting, target frequency of ad exposure, and mandatory media coverage to matched content websites.


2022 ◽  
Vol 18 (1) ◽  
pp. 0-0

Budget constrained sponsored search advertisers must decide how to allocate their advertisement budget across ad campaigns and individual keywords. In this paper, a simulation model that integrates the complex issues involved in keyword segmentation and campaign organization is used to evaluate performance of various budget allocation strategies. Using the buying funnel model as the basis for keyword segmentation and campaign organization, we analyze Volume-based, Cost-based, and Clicks-based budget allocation strategies and evaluate their performance implications for different firms. The simulation model is empirically evaluated using four Fortune 500 companies and their keyword data obtained from a leading provider of keyword research technology. The results and statistical analyses show significant improvements in budget utilization using the proposed allocation strategies over a Baseline commonly used in practice. The study offers useful insights into the budget allocation problem by leveraging a theoretical framework for keyword segmentation and campaign management.


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