Cold Start on Online Advertising Platforms: Data-Driven Algorithms and Field Experiments

2020 ◽  
Author(s):  
Zikun Ye ◽  
Dennis Zhang ◽  
Heng Zhang ◽  
Renyu Zhang ◽  
Xin Chen ◽  
...  
Keyword(s):  
Field Experiments ◽  
Online Advertising ◽  
Cold Start ◽  
Data Driven

scholarly journals Evaluating Stochastic Seeding Strategies in Networks

2021 ◽  
Author(s):  
Alex Chin ◽  
Dean Eckles ◽  
Johan Ugander
Keyword(s):  
Field Experiments ◽  
Random Network ◽  
Data Driven ◽  
Large Field ◽  
Special Issue ◽  
Valid Inference ◽  
Nonparametric Estimators ◽  
Prescriptive Analytics ◽  
Marketing Intervention ◽  
Random Individual

When trying to maximize the adoption of a behavior in a population connected by a social network, it is common to strategize about where in the network to seed the behavior, often with an element of randomness. Selecting seeds uniformly at random is a basic but compelling strategy in that it distributes seeds broadly throughout the network. A more sophisticated stochastic strategy, one-hop targeting, is to select random network neighbors of random individuals; this exploits a version of the friendship paradox, whereby the friend of a random individual is expected to have more friends than a random individual, with the hope that seeding a behavior at more connected individuals leads to more adoption. Many seeding strategies have been proposed, but empirical evaluations have demanded large field experiments designed specifically for this purpose and have yielded relatively imprecise comparisons of strategies. Here we show how stochastic seeding strategies can be evaluated more efficiently in such experiments, how they can be evaluated “off-policy” using existing data arising from experiments designed for other purposes, and how to design more efficient experiments. In particular, we consider contrasts between stochastic seeding strategies and analyze nonparametric estimators adapted from policy evaluation and importance sampling. We use simulations on real networks to show that the proposed estimators and designs can substantially increase precision while yielding valid inference. We then apply our proposed estimators to two field experiments, one that assigned households to an intensive marketing intervention and one that assigned students to an antibullying intervention. This paper was accepted by Gui Liberali, Special Issue on Data-Driven Prescriptive Analytics.

Native Advertising in Online News: Trade-Offs Among Clicks, Brand Recognition, and Website Trustworthiness

2019 ◽  
Vol 57 (1) ◽  
pp. 20-34 ◽  
Author(s):  
Anocha Aribarg ◽  
Eric M. Schwartz
Keyword(s):  
Field Experiments ◽  
Online Advertising ◽  
Online News ◽  
Editorial Content ◽  
Form And Function ◽  
Brand Recognition ◽  
Trade Offs ◽  
Conflicting Objectives ◽  
And Function ◽  
Click Through Rate

Native advertising is a type of online advertising that matches the form and function of the platform on which it appears. In practice, the choice between display and in-feed native advertising presents brand advertisers and online news publishers with conflicting objectives. Advertisers face a trade-off between ad clicks and brand recognition, whereas publishers need to strike a balance between ad clicks and the platform’s trustworthiness. For policy makers, concerns that native advertising confuses customers prompted the U.S. Federal Trade Commission to issue guidelines for disclosing native ads. This research aims to understand how consumers respond to native ads versus display ads and to different styles of native ad disclosures, using randomized online and field experiments combining behavioral clickstream, eye movement, and survey response data. The results show that when the position of an ad on a news page is controlled for, a native ad generates a higher click-through rate because it better resembles the surrounding editorial content. However, a display ad leads to more visual attention, brand recognition, and trustworthiness for the website than a native ad.

scholarly journals Vision-based positioning system for auto-docking of unmanned surface vehicles (USVs)

2021 ◽  
Author(s):  
Øystein Volden ◽  
Annette Stahl ◽  
Thor I. Fossen
Keyword(s):  
Stereo Vision ◽  
Field Experiments ◽  
Low Cost ◽  
Reference Points ◽  
Outdoor Environment ◽  
Data Driven ◽  
Positioning System ◽  
Localization Task ◽  
Detection Model ◽  
Cost System

AbstractThis paper presents an independent stereo-vision based positioning system for docking operations. The low-cost system consists of an object detector and different 3D reconstruction techniques. To address the challenge of robust detections in an unstructured and complex outdoor environment, a learning-based object detection model is proposed. The system employs a complementary modular approach that uses data-driven methods, utilizing data wherever required and traditional computer vision methods when the scope and complexity of the environment are reduced. Both, monocular and stereo-vision based methods are investigated for comparison. Furthermore, easily identifiable markers are utilized to obtain reference points, thus simplifying the localization task. A small unmanned surface vehicle (USV) with a LiDAR-based positioning system was exploited to verify that the proposed vision-based positioning system produces accurate measurements under various docking scenarios. Field experiments have proven that the developed system performs well and can supplement the traditional navigation system for safety-critical docking operations.

Cold Start Approach for Data-Driven Fault Detection

2013 ◽  
Vol 9 (4) ◽  
pp. 2264-2273 ◽  
Author(s):  
Mihajlo Grbovic ◽  
Weichang Li ◽  
Niranjan A Subrahmanya ◽  
Usadi ◽  
Slobodan Vucetic
Keyword(s):  
Fault Detection ◽  
Cold Start ◽  
Data Driven

scholarly journals Artificial Intelligence as the New Realm for Online Advertising

2021 ◽  
Author(s):  
Nouran Tahoun ◽  
Ahmed Taher
Keyword(s):  
Artificial Intelligence ◽  
Significant Relationship ◽  
Process Model ◽  
Online Survey ◽  
Online Advertising ◽  
Data Driven ◽  
Perceived Effectiveness ◽  
Media Planning ◽  
Client Side ◽  
The Impact

This study explores the utilization of Artificial Intelligence (AI) in the online advertising process and the impact of using AI in each stage on overall perceived effectiveness. It also provides a better understanding of the magnitude of using AI in the four stages of advertising online: namely, consumer insights, ad creation, media planning and buying, and ad evaluation. <i>The Process model of AI utilization in online advertising </i>is the study's conceptual model developed based on the literature. An online survey is conducted with digital advertisers worldwide from both agency and client-side. The findings showed that AI is emerging progressively in the four stages of the data-driven online advertising process. Moreover, it showed a significant relationship between AI utilization in each stage and the following one. Using AI in each advertising stage promotes the perceived effectiveness of the overall online ad process.

The Double-Edged Effects of E-Commerce Cart Retargeting: Does Retargeting Too Early Backfire?

2020 ◽  
pp. 002224292095904
Author(s):  
Jing Li ◽  
Xueming Luo ◽  
Xianghua Lu ◽  
Takeshi Moriguchi
Keyword(s):  
Field Experiment ◽  
Conceptual Framework ◽  
Short Message Service ◽  
Field Experiments ◽  
Negative Impact ◽  
Positive Impact ◽  
Online Advertising ◽  
Short Message ◽  
Message Service ◽  
Customer Purchases

Consumers often abandon e-commerce carts, so companies are shifting their online advertising budgets to immediate e-commerce cart retargeting (ECR). They presume that early reminder ads, relative to late ones, generate more click-throughs and web revisits. The authors develop a conceptual framework of the double-edged effects of ECR ads and empirically support it with a multistudy, multisetting design. Study 1 involves two field experiments on over 40,500 customers who are randomized to either receive an ECR ad via email and app channels (treatment) or not receive it (control) across different hourly blocks after cart abandonment. The authors find that customers who received an early ECR ad within 30 minutes to one hour after cart abandonment are less likely to make a purchase compared with the control. These findings reveal a causal negative incremental impact of immediate retargeting. In other words, delivering ECR ads too early can engender worse purchase rates than without delivering them, thus wasting online advertising budgets. By contrast, a late ECR ad received one to three days after cart abandonment has a positive incremental impact on customer purchases. In Study 2, another field experiment on 23,900 customers not only replicates the double-edged impact of ECR ads delivered by mobile short message service but also explores cart characteristics that amplify both the negative impact of early ECR ads and positive impact of late ECR ads. These findings offer novel insights into customer responses to online retargeted ads for researchers and managers alike.

Alibaba's International Digital Marketing Practices and Strategies

2021 ◽  
pp. 80-98
Author(s):  
Sue Claire Berning
Keyword(s):  
Business Models ◽  
Online Advertising ◽  
Data Driven ◽  
Digital Marketing ◽  
Study Approach ◽  
Use Of Data ◽  
Adaptive Capabilities ◽  
Chinese Company ◽  
Main Relationship

In this chapter, the main relationship between a company's use of data-driven methods and its international digital marketing strategies are examined. In particular, the question of how data-driven methods, like consumer analytics, helped the company in its internationalization efforts are outlined. By following the case study approach, the diverse digital business models, online advertising campaigns, and international digital marketing practices of the Chinese company Alibaba are investigated. As China's e-commerce market currently became one of the most dynamic ones in the world, and as Alibaba is one of the leading internet and e-commerce corporations worldwide, valuable insights are provided. Moreover, Alibaba's international digital marketing practices, underlying strategies, as well as adaptive capabilities are systematically analyzed. In addition, Alibaba's competitive behavior is investigated and compared with international companies and peers. In this context, the standardization versus adaptation paradigm is also revisited.

scholarly journals Artificial Intelligence as the New Realm for Online Advertising

2021 ◽  
Author(s):  
Nouran Tahoun ◽  
Ahmed Taher
Keyword(s):  
Artificial Intelligence ◽  
Significant Relationship ◽  
Process Model ◽  
Online Survey ◽  
Online Advertising ◽  
Data Driven ◽  
Perceived Effectiveness ◽  
Media Planning ◽  
Client Side ◽  
The Impact

This study explores the utilization of Artificial Intelligence (AI) in the online advertising process and the impact of using AI in each stage on overall perceived effectiveness. It also provides a better understanding of the magnitude of using AI in the four stages of advertising online: namely, consumer insights, ad creation, media planning and buying, and ad evaluation. <i>The Process model of AI utilization in online advertising </i>is the study's conceptual model developed based on the literature. An online survey is conducted with digital advertisers worldwide from both agency and client-side. The findings showed that AI is emerging progressively in the four stages of the data-driven online advertising process. Moreover, it showed a significant relationship between AI utilization in each stage and the following one. Using AI in each advertising stage promotes the perceived effectiveness of the overall online ad process.

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