Optimal Bidding Strategy for Brand Advertising

Maehara, Takanori; Narita, Atsuhiro; Baba, Jun; Kawabata, Takayuki

doi:10.24963/ijcai.2018/59

Cited by 13 publications

(13 citation statements)

References 10 publications

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“…Then, smart pacing dynamically adjusted the group pacing rate according to the current budget spending rate to control the budget spending rate to fulfill the budget spending plan. Based on psychology theory, Maehara et al [33] defined a different objective for branding campaign as maximizing the number of audiences who remember the ad after watching for a period of time. To acquire more impressions, Shih and Huang [4] introduced the concept of expected win rate which indicated how eager to win this bid request under the current remaining resources.…”

Section: The Bidding Strategies For Branding Campaignsmentioning

confidence: 99%

An Expected Win Rate-Based Real Time Bidding Strategy for Branding Campaign by the Model-Free Reinforcement Learning Model

Shih

Tsai

et al. 2020

IEEE Access

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The bidding strategy plays the most important role to help the Demand Side Platforms (DSPs) making bidding decisions on a large number of bid requests in Real Time Bidding (RTB) to satisfy the different objectives of campaigns under the lifetime and budget constraints. In this paper, we focus on branding campaign whose objective is to obtain as many impressions as possible under the lifetime and budget constraints. To achieve the objectives of branding campaigns, we propose a novel expected win rate-based bidding strategy for branding campaign under the lifetime and budget constraints by utilizing a model-free reinforcement learning model. Specifically, to prevent missing good opportunities resulting from submitting extremely low bid prices, the concept of the base winning price is introduced to determine the lower bound of expected winning price. In addition, to obtain more impressions, the concept of the DSP-specified budget spending plan is proposed to determine the proper winning prices. The base expected win rate is then calculated based on the base winning price and the winning price determined by the DSPspecified budget spending plan. Since RTB is a dynamic environment, we propose a novel expected win ratebased bidding strategy named EWDQN which utilizes Deep Q Network (DQN) to dynamically determine the expected win rate according to the base expected win rate and the current status of the RTB market, and then determines the bid price according to the expected win rate. To the best of our knowledge, this is the first research applying the reinforcement learning technique on the bidding strategies for branding campaign. To measure the performance of EWDQN, several experiments are conducted on two real datasets. Experimental results show that EWDQN outperforms the-state-of-the-art bidding strategies for branding campaign in terms of the number of obtained impressions and CPM (cost per thousand impressions).

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Section: The Bidding Strategies For Branding Campaignsmentioning

confidence: 99%

An Expected Win Rate-Based Real Time Bidding Strategy for Branding Campaign by the Model-Free Reinforcement Learning Model

Shih

Tsai

et al. 2020

IEEE Access

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“…The formulation in Eq. (18) demonstrates that the control signal of p and q should be a linear combination of the changes of cost and CPC. Therefore, we define the model predictive module by Eq.…”

Section: Multivariable Controlmentioning

confidence: 99%

Bid Optimization by Multivariable Control in Display Advertising

Yang

Wang

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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Real-Time Bidding (RTB) is an important paradigm in display advertising, where advertisers utilize extended information and algorithms served by Demand Side Platforms (DSPs) to improve advertising performance. A common problem for DSPs is to help advertisers gain as much value as possible with budget constraints. However, advertisers would routinely add certain key performance indicator (KPI) constraints that the advertising campaign must meet due to practical reasons. In this paper, we study the common case where advertisers aim to maximize the quantity of conversions, and set cost-per-click (CPC) as a KPI constraint. We convert such a problem into a linear programming problem and leverage the primal-dual method to derive the optimal bidding strategy. To address the applicability issue, we propose a feedback control-based solution and devise the multivariable control system. The empirical study based on real-word data from Taobao.com verifies the effectiveness and superiority of our approach compared with the state of the art in the industry practices.

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“…from a prior distribution p X (x x x). Correspondingly, the advertising effect is commonly measured by the audience's historical feedbacks, such as ad audience's CTR/CVR [2], [6], desired action rate lift [25] or memory retention of displayed ads [29]. Without loss of generality, in this paper, we adopt ad audience's CVR to evaluate the advertising effect since it is more relevant to revenue.…”

Section: Preliminaries and Notationsmentioning

confidence: 99%

A Revenue-Maximizing Bidding Strategy for Demand-Side Platforms

Wang

Yang

et al. 2019

IEEE Access

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In real-time bidding (RTB) systems for display advertising, a demand-side platform (DSP) serves as an agent for advertisers and plays an important role in competing for online advertising spaces by placing proper bidding prices. A critical function of the DSP is formulating proper bidding strategies to maximize key performance indicators, such as the number of clicks and conversions. However, many small and medium-sized advertisers' main goal is to maximize revenue with an acceptable return on investment (ROI), rather than simply increase clicks or conversions. Most existing approaches are inapplicable of satisfying the revenue-maximizing goals directly. To solve this problem, we first theoretically analyze the relationships among the conversion rate, ROI, and ad cost, and how they affect revenue. By doing so, we reveal that it is a challenge to increase revenue by relying solely on improving ROI without considering the impact of the ad cost. Based on this insight, the maximal revenue (MR) bidding strategy is proposed to maximize revenue by maximizing the ad cost with a desirable ROI constraint. Unlike previous studies, the proposed MR first distinguishes bid prices from ad costs explicitly, which makes it more applicable to the real second-price auction (GSP) auction mechanism in RTB systems. Then, the winning function is empirically defined in the form of tanh that provides a promising solution for estimating ad costs by jointly considering ad costs with the winning function. The experimental results based on two real-world public datasets demonstrate that the MR significantly outperforms five state-of-the-art models in terms of both revenue and ROI. INDEX TERMS Bid landscape forecasting, bidding strategy optimization, demand-side platform, real-time bidding.

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Optimal Bidding Strategy for Brand Advertising

Cited by 13 publications

References 10 publications

An Expected Win Rate-Based Real Time Bidding Strategy for Branding Campaign by the Model-Free Reinforcement Learning Model

An Expected Win Rate-Based Real Time Bidding Strategy for Branding Campaign by the Model-Free Reinforcement Learning Model

Bid Optimization by Multivariable Control in Display Advertising

A Revenue-Maximizing Bidding Strategy for Demand-Side Platforms

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