In recent years, European regulators have debated restricting the time an online tracker can track a user to protect consumer privacy better. Despite the significance of these debates, there has been ...a noticeable absence of any comprehensive cost-benefit analysis. This article fills this gap on the cost side by suggesting an approach to estimate the economic consequences of lifetime restrictions on cookies for publishers. The empirical study on cookies of 54,127 users who received ∼128 million ad impressions over ∼2.5 years yields an average cookie lifetime of 279 days, with an average value of €2.52 per cookie. Only ∼13 % of all cookies increase their daily value over time, but their average value is about four times larger than the average value of all cookies. Restricting cookies’ lifetime to one year (two years) could potentially decrease their lifetime value by ∼25 % (∼19 %), which represents a potential decrease in the value of all cookies of ∼9 % (∼5%). Most cookies, however, would not be affected by lifetime restrictions of 12 or 24 months as 72 % (85 %) of the users delete their cookies within 12 (24) months. In light of the €10.60 billion cookie-based display ad revenue in Europe, such restrictions would endanger €904 million (€576 million) annually, equivalent to €2.08 (€1.33) per EU internet user. The article discusses these results' marketing strategy challenges and opportunities for advertisers and publishers.
Our research question is motivated by a real problem faced by an existing demand aggregator. The aggregator represents multiple advertisers, each of whom signs one of two types of contracts with the ...aggregator for bidding on an RTB (real‐time bidding) platform. A quality contract occurs on a cost‐per‐impression (CPM, i.e., cost per thousand impressions) basis. The advertiser is promised a minimum number of impressions and pays a CPM that is a function of the targeting quality as measured by the realized click‐through rate (CTR). In a performance contract the advertiser pays on a CPC (cost‐per‐click) basis constrained by a budget. We develop and solve a generalized profit maximization problem that jointly optimizes the aggregator's bidding and allocation decisions. The allocation policy optimally assigns each arriving bidding opportunity to a specific advertiser. The bidding policy then computes a bid amount for that allocation based on the estimated click probability of the opportunity. Our solution has nice theoretical properties. First, neither policy depends on the memory carried in the system, that is, the sequence of previous states and decisions, making the solution easy to implement. Second, the allocation policy is shown to have a threshold structure. This enables the assignment of arriving opportunities into one of two distinct sets, each corresponding to a specific advertising contract type. The assessment of the impact of the change in various parameters on the solution is used to derive several interesting and important implications for the management of advertising contracts.
Real-time bidding (RTB) has become a critical way for online advertising. It allows advertisers to display their ads by bidding on ad impressions. Therefore, advertisers in RTB always seek an optimal ...bidding strategy to improve their cost-efficiency. Unfortunately, it is challenging to optimize the bidding strategy at the granularity of impression due to the highly dynamic nature of the RTB environment. In this paper, we focus on optimizing the single advertiser’s bidding strategy using a stochastic reinforcement learning (RL) algorithm. Firstly, we utilize a widely adopted linear bidding function to compute every impression’s base price and optimize it with a mutable adjustment factor, thus making the bidding price conform to not only the impression’s value to the advertiser but also the RTB environment. Secondly, we use the maximum entropy RL algorithm (Soft Actor-Critic) to optimize every impression’s adjustment factor to overcome the deterministic RL algorithm’s convergence problem. Finally, we evaluate the proposed strategy on a benchmark dataset (iPinYou), and the results demonstrate it obtained the most click numbers in 9 of 12 experiments compared to baselines.
This research provides an experimental analysis and comparison of several RTB (real-time bidding) methods. These experiments are based on practical validation through the utilization of real-world ...datasets, with the primary attention being placed on the dynamic and complex nature of the RTB ecosystem. Simulations are run on various real-world advertising campaigns as part of the experimentation. These simulations take into account different campaign budgets, bid request dynamics, and user interaction pattern variations. The efficiency of each algorithm is evaluated based on performance indicators such as click-through rates (CTRs), conversion rates (CR), return on investment (ROI), win rates, cost per mille (CPM), and effective cost per click (E-CPC). The results provide useful insights into the advantages and disadvantages of each technique used, and the experimental analysis indicates the Constrained Markov Decision Process (CMDP)-based model as a promising and superior technique for RTB optimization. It provides valuable information on applications of reinforcement learning in the dynamic RTB ecosystem. Hence, via these comparisons, this paper aims to contribute to the advancement of RTB methodologies and proposes a viable route for future research.
Real-time bidding (RTB) based display advertising has become one of the key technological advances in computational advertising. RTB enables advertisers to buy individual ad impressions via an ...auction in real-time and facilitates the evaluation and the bidding of individual impressions across multiple advertisers. In RTB, the advertisers face three main challenges when optimizing their bidding strategies, namely (i) estimating the utility (e.g., conversions, clicks) of the ad impression, (ii) forecasting the market value (thus the cost) of the given ad impression, and (iii) deciding the optimal bid for the given auction based on the first two. Previous solutions assume the first two are solved before addressing the bid optimization problem. However, these challenges are strongly correlated and dealing with any individual problem independently may not be globally optimal. In this paper, we propose Bidding Machine, a comprehensive learning to bid framework, which consists of three optimizers dealing with each challenge above, and as a whole, jointly optimizes these three parts. We show that such a joint optimization would largely increase the campaign effectiveness and the profit. From the learning perspective, we show that the bidding machine can be updated smoothly with both offline periodical batch or online sequential training schemes. Our extensive offline empirical study and online A/B testing verify the high effectiveness of the proposed bidding machine.
•There are three aspects of our manuscript that we believe will attract general readers of your journal.•First, we propose a novel privacy-preserving real-time bidding protocol to protect the user’s ...data privacy, which to the best of our knowledge, is the first privacy preserving protocol in real-time bidding paradigm. We also provide a cryptographic security proof of our protocol on user profile.•Second, we propose an improved comparing scheme over encrypted data in our scenario, which is of high efficiency and security.•Third, we present a prototype to evaluate the efficiency of our protocol, and demonstrate that our prototype is efficient enough to be applied with a cautious choice of parameters.
Real-time bidding (RTB), one of the major trading mechanisms used for online advertising, allows the advertiser to make an impression-level bid decision. However, the security and privacy concerns of RTB are gaining increasing attention with the recent enforcement of the European Union General Data Protection Regulation (GDPR). In this study, we present a novel privacy-preserving RTB (Pri-RTB) protocol, which aims at preserving both data privacy and utility by performing additively homomorphic encryption on the user profile. To our knowledge, this is the first work to address the privacy issue of the RTB paradigm. We present a formal proof for the security of Pri-RTB under the assumption of the decisional Diffie–Hellman (DDH) problem. We analyze and elaborate the superiority of Pri-RTB over other related works. We also developed a prototype for Pri-RTB and conducted several experiments to evaluate its feasibility and efficiency under different parameters. Our experiments demonstrate that Pri-RTB can work highly efficiently in a practical setting (for example, a scenario with approximately 200 advertisers per auction).
Online display advertising platforms service numerous advertisers by providing real-time bidding (RTB) for the scale of billions of ad requests every day. The bidding strategy handles ad requests ...cross multiple channels to maximize the number of clicks under the set financial constraints, i.e., total budget and cost-per-click (CPC), etc. Different from existing works mainly focusing on single channel bidding, we explicitly consider cross-channel constrained bidding with budget allocation. Specifically, we propose a hierarchical offline deep reinforcement learning (DRL) framework called "HiBid", consisted of a high-level planner equipped with auxiliary loss for non-competitive budget allocation, and a data augmentation enhanced low-level executor for adaptive bidding strategy in response to allocated budgets. Additionally, a CPC-guided action selection mechanism is introduced to satisfy the cross-channel CPC constraint. Through extensive experiments on both the large-scale log data and online A/B testing, we confirm that HiBid outperforms six baselines in terms of the number of clicks, CPC satisfactory ratio, and return-on-investment (ROI). We also deploy HiBid on Meituan advertising platform to already service tens of thousands of advertisers every day.
As a revolutionary auction mechanism for display advertising, real-time bidding (RTB) allows advertisers to purchase individual ad impressions through real-time auctions. In RTB, the demand-side ...platform (DSP) acts as advertisers' bidding agent and aims at developing appropriate bidding strategies to maximize their specific key performance indicators (KPIs). Existing bidding strategies perform well for optimizing profits when the ad budget severely limited. However, when there is sufficient budget, their performance deteriorates. This results in added complexity for advertisers when applying these approaches in practice, hindering wider adoption. To address this challenging limitation, we propose the Adaptive ROI-Aware Bidding (ARAB) approach. It intelligently analyzes the budget setting and auction market conditions, and adjusts the bidding function accordingly to optimize profits. Different from previous studies that only bid based on the ad revenue, our proposed ROI-aware bidding function also takes into account the ad cost at impression-level. By doing so, ARAB dynamically allocates the budget on more cost-effective impressions to increase profits. Through extensive offline experiments on two real-world public datasets, we demonstrate that the proposed ARAB has achieved significant improvements in terms of both profit and ROI compared to state-of-the-art approaches.
Learning the distribution of market prices is an important and challenging issue for demand-side platforms (DSPs) that serve as advertisers’ agents to compete for online advertising placements in ...real-time bidding (RTB) systems. Many existing approaches make an assumption that the market prices follow an unimodal distribution. However, based on analytical insights from real-world datasets, we found the distinct multimodal characteristics underlying the distribution of market prices. Moreover, the impression-level features for each ad are also ignored by these approaches in prediction, reducing the accuracy further. To address these problems, a Gaussian Mixture Model (GMM) is proposed in this paper to describe and discriminate the multimodal distribution of market price by utilizing the impression-level features. To further improve its robustness, GMM is extended into a censored version (CGMM) to handle the right-censored challenge in RTB systems (i.e., the market price is only visible to the winner of the ad auction. Thus, the dataset is always biased). Extensive experiments on two real-world public datasets demonstrate that GMM and CGMM significantly outperform 10 state-of-the-art baselines in terms of Wasserstein distance, KL-divergence, ANLP and MSE. To the best of our knowledge, this paper is the first work to simultaneously deal with the multimodal nature of market price distribution and the right-censored challenge in existing RTB systems. It will enable future RTB systems to develop more realistic bidding strategies to enhance the efficiency of online advertising placement auctioning.
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