Errors in Geotargeted Display Advertising

2021 ◽  
Vol 5 (CSCW1) ◽  
pp. 1-19
Author(s):  
Jack Bandy ◽  
Brent Hecht
Keyword(s):  
Display Advertising

WHERE ARE THE 99%?: THE IMPACTS OF DISPLAY ADVERTISING ON CONSUMER RESPONSES BEYOND CLICK

2019 ◽  
Vol 2019 ◽  
pp. 182-183
Author(s):  
Jang Ho Moon ◽  
◽  
Kichang Han ◽  
Sanghyun Lim ◽  
Gyungjin Bae ◽  
...  
Keyword(s):  
Consumer Responses ◽  
Display Advertising

scholarly journals Optimized Cost per Click in Taobao Display Advertising

2017 ◽  
Author(s):  
Han Zhu ◽  
Junqi Jin ◽  
Chang Tan ◽  
Fei Pan ◽  
Yifan Zeng ◽  
...  
Keyword(s):  
Display Advertising

Display Advertising – Renaissance des Banners

2017 ◽  
pp. 87-102
Author(s):  
Ingo Kamps ◽  
Daniel Schetter
Keyword(s):  
Display Advertising

Focused matrix factorization for audience selection in display advertising

2013 ◽  
Author(s):  
B. Kanagal ◽  
A. Ahmed ◽  
S. Pandey ◽  
V. Josifovski ◽  
L. Garcia-Pueyo ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Display Advertising

scholarly journals Partner tiering in display advertising

2014 ◽  
Author(s):  
Anand Bhalgat ◽  
Nitish Korula ◽  
Hannadiy Leontyev ◽  
Max Lin ◽  
Vahab Mirrokni
Keyword(s):  
Display Advertising

Rejoinder—Implications of “Online Display Advertising: Targeting and Obtrusiveness”

2011 ◽  
Vol 30 (3) ◽  
pp. 413-415 ◽  
Author(s):  
Avi Goldfarb ◽  
Catherine Tucker
Keyword(s):  
Display Advertising

Efficient Large-Scale Internet Media Selection Optimization for Online Display Advertising

2018 ◽  
Vol 55 (4) ◽  
pp. 489-506 ◽  
Author(s):  
Courtney Paulson ◽  
Lan Luo ◽  
Gareth M. James
Keyword(s):  
Media Coverage ◽  
Large Scale ◽  
Budget Allocation ◽  
Media Selection ◽  
Demand Side ◽  
Rule Based ◽  
Target Frequency ◽  
Display Advertising ◽  
Internet Media ◽  
Optimal Bidding

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.

Measurement of Brand Lift from a Display Advertising Campaign

2012 ◽  
pp. 208-222
Author(s):  
Jagdish Chand
Keyword(s):  
Business Intelligence ◽  
Brand Awareness ◽  
Advertising Campaign ◽  
Display Advertising ◽  
Intelligence System

In this chapter we will describe an advanced business intelligence system we built at Yahoo to measure the lift in brand awareness driven from the display advertising campaigns on Yahoo network. It helped us to show to the advertisers that display advertising is working in lifting awareness and brand affinity.

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