Hasil untuk "Advertising"

A. Greenwald, Clark Leavitt

R. Batra, M. L. Ray

Gerard R. Butters

Margaret C. Campbell, Kevin Lane Keller

Hairong Li, T. Daugherty, F. Biocca

Chang-Hoan Cho, University of Texas at Austin) is an as-

R. Pieters, M. Wedel, R. Batra

George E. Belch, Michael Belch

Clinton Amos, Gary R. Holmes, D. Strutton

Avi Goldfarb, Catherine Tucker

Advertisers use online customer data to target their marketing appeals. This has heightened consumers' privacy concerns, leading governments to pass laws designed to protect consumer privacy by restricting the use of data and by restricting online tracking techniques used by websites. We use the responses of 3.3 million survey-takers who had been randomly exposed to 9,596 online display (banner) advertising campaigns to explore how privacy regulation in the European Union has influenced advertising effectiveness. This privacy regulation restricted advertisers' ability to collect data on web users in order to target ad campaigns. We find that on average, display advertising became far less effective at changing stated purchase intent after the EU laws were enacted, relative to display advertising in other countries. The loss in effectiveness was more pronounced for websites that had general content (such as news sites), where non-data-driven targeting is particularly hard to do. The loss of effectiveness was also more pronounced for ads with a smaller presence on the webpage and for ads that did not have additional interactive, video, or audio features.

Marianne Bertrand, D. Karlan, S. Mullainathan et al.

Terence A. Shimp

H. Sexton

on advertising, I will give you my ideas regarding it. These ideas have been developed and confirmed in my mind during the nine years I have practiced. Experience is everywhere recognized as a thorough teacher, and my ideas, I do not hesitate to say, were to a great extent gained by experience. Therefore, in considering my paper, I hope that your attention will rest on the thoughs introduced, and not upon the literary dressing of the production, for as an essay, considered purely from a literary standpoint, you will find it very imperfect. I will speak first of that mass of advertising which

Gagan Aggarwal, Yifan Wang, Mingfei Zhao

Online advertising platforms must decide how to allocate multiple ads across limited screen real estate, where each ad's effectiveness depends not only on its own placement but also on nearby ads competing for user attention. Such spatial externalities - arising from proximity, clutter, or crowding - can significantly alter welfare and revenue outcomes, yet existing auction and allocation models typically treat ad slots as independent or ordered along a single dimension. We introduce a new framework for spatial externalities in online advertising, in which the value of an ad depends on both its slot and the configuration of surrounding ads. We model ad slots as points in a metric space, and model an advertiser's value as a function of both their bid and a discount factor determined by the configuration of other displayed ads. Within this framework, we analyze two natural models. For the Nearest-Neighbor model, where the value suppression depends only on the closest neighboring ad, we present a polynomial-time algorithm that achieves a constant approximation for the general case. We show that the allocation rule is monotone and can be implemented as a truthful mechanism. For a structured setting of 2D Euclidean space, we provide a PTAS. In contrast, for the Product-Distance model, where interference is aggregated multiplicatively across all neighbors, we establish a strong (and nearly-tight) hardness of approximation - no polynomial-time algorithm can achieve any polynomial-factor approximation unless P=NP, via a reduction from Max-Independent-Set. Our results provide a foundation for reasoning about spatial externalities in ad allocation and for designing efficient, truthful mechanisms under such interactions.

Jinfang Wang, Jiajie Liu, Jianwei Wu et al.

In online advertising, advertising text plays a critical role in attracting user engagement and driving advertiser value. Existing industrial systems typically follow a two-stage paradigm, where candidate texts are first generated and subsequently aligned with online performance metrics such as click-through rate(CTR). This separation often leads to misaligned optimization objectives and low funnel efficiency, limiting global optimality. To address these limitations, we propose RELATE, a reinforcement learning-based end-to-end framework that unifies generation and objective alignment within a single model. Instead of decoupling text generation from downstream metric alignment, RELATE integrates performance and compliance objectives directly into the generation process via policy learning. To better capture ultimate advertiser value beyond click-level signals, We incorporate conversion-oriented metrics into the objective and jointly model them with compliance constraints as multi-dimensional rewards, enabling the model to generate high-quality ad texts that improve conversion performance under policy constraints. Extensive experiments on large-scale industrial datasets demonstrate that RELATE consistently outperforms baselines. Furthermore, online deployment on a production advertising platform yields statistically significant improvements in click-through conversion rate(CTCVR) under strict policy constraints, validating the robustness and real-world effectiveness of the proposed framework.

Briti Gangopadhyay, Zhao Wang, Alberto Silvio Chiappa et al.

Effective budget allocation is crucial for optimizing the performance of digital advertising campaigns. However, the development of practical budget allocation algorithms remain limited, primarily due to the lack of public datasets and comprehensive simulation environments capable of verifying the intricacies of real-world advertising. While multi-armed bandit (MAB) algorithms have been extensively studied, their efficacy diminishes in non-stationary environments where quick adaptation to changing market dynamics is essential. In this paper, we advance the field of budget allocation in digital advertising by introducing three key contributions. First, we develop a simulation environment designed to mimic multichannel advertising campaigns over extended time horizons, incorporating logged real-world data. Second, we propose an enhanced combinatorial bandit budget allocation strategy that leverages a saturating mean function and a targeted exploration mechanism with change-point detection. This approach dynamically adapts to changing market conditions, improving allocation efficiency by filtering target regions based on domain knowledge. Finally, we present both theoretical analysis and empirical results, demonstrating that our method consistently outperforms baseline strategies, achieving higher rewards and lower regret across multiple real-world campaigns.

Langming Liu, Wanyu Wang, Chi Zhang et al.

Online advertising in recommendation platforms has gained significant attention, with a predominant focus on channel recommendation and budget allocation strategies. However, current offline reinforcement learning (RL) methods face substantial challenges when applied to sparse advertising scenarios, primarily due to severe overestimation, distributional shifts, and overlooking budget constraints. To address these issues, we propose MTORL, a novel multi-task offline RL model that targets two key objectives. First, we establish a Markov Decision Process (MDP) framework specific to the nuances of advertising. Then, we develop a causal state encoder to capture dynamic user interests and temporal dependencies, facilitating offline RL through conditional sequence modeling. Causal attention mechanisms are introduced to enhance user sequence representations by identifying correlations among causal states. We employ multi-task learning to decode actions and rewards, simultaneously addressing channel recommendation and budget allocation. Notably, our framework includes an automated system for integrating these tasks into online advertising. Extensive experiments on offline and online environments demonstrate MTORL's superiority over state-of-the-art methods.

Lu Zhang

With the acceleration of digitalization, digital media and streaming platforms have driven the rapid development of advertising placement business models in the film and television industry. Producers increasingly depend on advertising revenue, advertisers prioritize return on investment, and viewers’ grow more resistant to advertising interruptions, intensifying the tension among stakeholders. Most existing studies focus on bilateral relationships and neglect the strategic behavior of viewers, which limits their ability to explain persistent cooperation failures in real-world advertising ecosystems. To address this, this study develops a three-party stochastic evolutionary game model involving film producers, advertisers, and viewers, incorporating key variables such as advertising dissemination effectiveness, content quality, and advertising costs to simulate strategy evolution under uncertainty. Simulation results indicate that improving content quality from 100 to 200 increases viewers’ ad acceptance from 0.32 to 0.84 and raises producer cooperation willingness by more than 70 percent. In contrast, when embedded advertising costs rise from 100,000 to 500,000 RMB, cooperation willingness among both producers and advertisers drops below 0.1. While increased returns from inserted ads may briefly raise producer engagement, they have minimal effect on viewers’ acceptance and tend to destabilize the system. This study identifies a structural mismatch in stakeholder incentives and introduces a dynamic modeling approach that captures nonlinear interactions and adaptive behavior using continuous strategies and stochastic disturbances. The findings suggest that technical improvements or revenue redistribution alone are insufficient to ensure sustainable cooperation. Enhancing content quality is the only effective lever for aligning stakeholder interests, breaking low-cooperation equilibria, and promoting long-term system stability, offering both theoretical contributions and practical guidance for platform governance and advertising strategy design.

Shintaro Okazaki, C. R. Taylor

Zhenbang Du, Wei Feng, Haohan Wang et al.

In the e-commerce realm, compelling advertising images are pivotal for attracting customer attention. While generative models automate image generation, they often produce substandard images that may mislead customers and require significant labor costs to inspect. This paper delves into increasing the rate of available generated images. We first introduce a multi-modal Reliable Feedback Network (RFNet) to automatically inspect the generated images. Combining the RFNet into a recurrent process, Recurrent Generation, results in a higher number of available advertising images. To further enhance production efficiency, we fine-tune diffusion models with an innovative Consistent Condition regularization utilizing the feedback from RFNet (RFFT). This results in a remarkable increase in the available rate of generated images, reducing the number of attempts in Recurrent Generation, and providing a highly efficient production process without sacrificing visual appeal. We also construct a Reliable Feedback 1 Million (RF1M) dataset which comprises over one million generated advertising images annotated by human, which helps to train RFNet to accurately assess the availability of generated images and faithfully reflect the human feedback. Generally speaking, our approach offers a reliable solution for advertising image generation.