Real Time Bidding (RTB) is a novel business model of online computational advertising, developing rapidly with the integration of Internet economy and big data analysis. It evolves the business logic ...of online ad-delivery from buying “ad-impressions” in websites or ad slots to directly buying the best-matched “target audiences”, and thus can help advertisers achieve the precision marketing. As a critical part of RTB advertising markets, Demand Side Platforms (DSPs) play a central role in matching advertisers with their target audiences via cookie-based data analysis and market segmentation, and their segmentation strategies (especially the choice of granularity) have key influences in improving the effectiveness and efficiency of RTB advertising markets. Based on a mathematical programming approach, this paper studied DSPs’ strategies for market segmentation, and established a selection model of the granularity for segmenting RTB advertising markets. With the computational experiment approach, we designed three experimental scenarios to validate our proposed model, and the experimental results show that: 1) market segmentation has the potential of improving the total revenue of all the advertisers; 2) with the increasing refinement of the market segmentation granularity, the total revenue has a tendency of a rise first and followed by a decline; 3) the optimal granularity of market segmentation will be significantly influenced by the number of advertisers on the DSP, but less influenced by the number of ad requests. Our findings show the crucial role of market segmentation on the RTB advertising effect, and indicate that the DSPs should adjust their market segmentation strategies according to their total number of advertisers. Our findings also highlight the importance of advertisers as well as the characteristics of the target audiences to DSPs’ market segmentation decisions.
Increasingly detailed consumer information makes sophisticated price discrimination possible. At fine levels of aggregation, demand may not obey standard regularity conditions. We propose a new ...randomized sales mechanism for such environments. Bidders can “buy-it-now” at a posted price, or “take-a-chance” in an auction where the top
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> 1 bidders are equally likely to win. The randomized allocation incentivizes high-valuation bidders to buy-it-now. We analyze equilibrium behavior and apply our analysis to advertiser bidding data from Microsoft Advertising Exchange. In counterfactual simulations, our mechanism increases revenue by 4.4% and consumer surplus by 14.5% compared to an optimal second-price auction.
This paper was accepted by Assaf Zeevi, stochastic models and simulation.
Real time bidding is one of the most popular ways of selling impressions in online advertising, where online ad publishers allocate some blocks in their websites to sell in online auctions. In real ...time bidding, ad networks connect publishers and advertisers. There are many available ad networks for publishers to choose from. A possible approach for selecting ad networks and sending ad requests is called Waterfall Strategy, in which ad networks are selected sequentially. The ordering of the ad networks is very important for publishers, and finding the ordering that will provide maximum revenue is a hard problem due to the highly dynamic environment. In this paper, we propose a dynamic ad network ordering method to find the best ordering of ad networks for publishers that opt for Waterfall Strategy to select ad networks. This method consists of two steps. The first step is a prediction model that is trained on real time bidding historical data and provides an estimation of revenue for each impression. These estimations are used as initial values for the Q-table in the second step. The second step is based on Reinforcement Learning and improves the output of the prediction model. By calculating the revenue of our method and comparing that with the revenue of a fixed and predefined ordering method, we show that our proposed dynamic ad network ordering method increases publishers’ revenue.
Real-time bidding (RTB), which holds bids for advertisers to choose when and where to display their advertisements with desired budgets, is one way to improve effectiveness. We proposed supply side ...platform (SSP) and demand side platform (DSP) to integrate supplier side and demand side as solution. The proposed method combines two digital technology service, gamified RTB and mobile location-based analysis (LBA). In order to make target customers make interactions actively with the advertisement contents, the idea of gamification is employed to increase customer participation. By the coupons in the gamifying RTB App, it have increased the coupon usage rate of 16.7% and 11.6% in the restaurant and apparel industries. It is a successfully digital transformation marketing service for retail industries. The gamified RTB plays an important role in making old business circles accept new technologies for the renewal of promotion strategies and help find the business directions in the future.
Previous real-time bidding (RTB) strategies offer a bidding price for each incoming bid request based on its individual predicted click through rate (CTR). However, this pricing mechanism could be a ...pitfall because the large and sparse feature space often leads to inaccurate individual CTR predictions. Furthermore, our observations in a real-world online advertising environment indicate that the predicted CTR could be uncorrelated to the empirical CTR. In this paper, we introduce a new evaluation metric, cluster expected win rate (CEWR) , and propose a novel framework Cluster-aware Ranking-based Bidding Strategy (CARBS) that leverages CEWR to cope with the above issue. CEWR quantifies the worthiness of each bid request based on a group of bid requests having similar expected performance. First, a two-step clustering method aggregates bid requests with similar predicted CTRs into clusters to gather similar information. Second, CARBS ranks the clusters and sets the Affordability Threshold in order to spend budgets smartly. CEWR summarizes the above results and hence better correlates to the click performance in our observations, causing the robustness superior to the inaccurate individual CTR predictions. Finally, a reinforcement learning-based bidding strategy is conducted to adjust the bid request expected win rate (BEWR) jointly based on CEWR and the dynamic market for deriving the final bid prices. The experimental results on three real ad campaigns manifest that CARBS outperforms state-of-the-art bidding strategies in terms of click acquisition. In a poorly predicted campaign (AUC: 0.73) with an extremely tight budget, the improvement is 32.5%, showing the robustness of CARBS . The code to reproduce our results is publicly available on GitHub: https://reurl.cc/0ZoVEl.
The Real Time Bidding (RTB) protocol is by now more than a decade old. During this time, a handful of measurement papers have looked at bidding strategies, personal information flow, and cost of ...display advertising through RTB. In this paper, we present YourAdvalue, a privacy-preserving tool for displaying to end-users in a simple and intuitive manner their advertising value as seen through RTB. Using YourAdvalue, we measure desktop RTB prices in the wild, and compare them with desktop and mobile RTB prices reported by past work. We present how it estimates ad prices that are encrypted, and how it preserves user privacy while reporting results back to a data-server for analysis. We deployed our system, disseminated its browser extension, and collected data from 200 users, including 12000 ad impressions over 11 months. By analyzing this dataset, we show that desktop RTB prices have grown 4.6x over desktop RTB prices measured in 2013, and 3.8x over mobile RTB prices measured in 2015. We also study how user demographics associate with the intensity of RTB ecosystem tracking, leading to higher ad prices. We find that exchanging data between advertisers and/or data brokers through cookie-synchronization increases the median value of display ads by 19%. We also find that female and younger users are more targeted, suffering more tracking (via cookie synchronization) than male or elder users. As a result of this targeting in our dataset, the advertising value (i) of women is 2.4x higher than that of men, (ii) of 25-34 year-olds is 2.5x higher than that of 35-44 year-olds, (iii) is most expensive on weekends and early mornings.
In the era of programmatic advertising, the advertisers have huge amount of first party data to leverage on enabling them to do highly granular re-targeting. Programmatic re-targeting is the ability ...to use data to show an ad to a user who has demonstrated an interest in your product offerings before. Re-targeting ads are a powerful conversion optimization tool and are typically known to outperform conventional targeting in terms of performance. As per 99 Firms, 41% of marketing allocation in 2018 to paid display spend was on re targeting and for most of the websites, only 2% of web-traffic converts on the first time visit. In this paper, a conversion is referred as a purchase made and a converting user is one who made the purchase on the website. The question that arises is - "should we be re-targeting all the users who have landed on the site?". In ad campaigns which has low budgets or in campaigns where the conversion rate is really low even though a huge volume of users visit the site, it may not make complete sense to simply re-target all those users, instead we would want to re-target those who are clearly showing an intent to make a purchase either through their on-site browsing behaviors or their past conversion patterns. Through this paper we present the use of first party privacy preserving data to do predictive programmatic re-targeting of users who are going to make a conversion in the next few days given their past site-browsing and conversion behavior using a structured data science and advanced ML based framework. Additionally, this project allows to tie the model results to real time programmatic activation by the creation of user segments depending on whether the user is going to make a conversion for the first time, or is converting again. The final outputs are these user segments, which are going to be used by in house ad-traders who would be able to bid deferentially for a specified period of time against each of the segments on a demand side platform. We have successfully tested this model on 2 advertising clients and were able to capture 80-85% of the actual converts happening over the next few days of them landing.
Abstract
This article discusses the troubled relationship between contemporary advertising technology (adtech) systems, in particular systems of real-time bidding (RTB, also known as programmatic ...advertising) underpinning much behavioral targeting on the web and through mobile applications. This article analyzes the extent to which practices of RTB are compatible with the requirements regarding a legal basis for processing, transparency, and security in European data protection law.
We first introduce the technologies at play through explaining and analyzing the systems deployed online today. Following that, we turn to the law. Rather than analyze RTB against every provision of the General Data Protection Regulation (GDPR), we consider RTB in the context of the GDPR’s requirement of a legal basis for processing and the GDPR’s transparency and security requirements. We show, first, that the GDPR requires prior consent of the internet user for RTB, as other legal bases are not appropriate. Second, we show that it is difficult—and perhaps impossible—for website publishers and RTB companies to meet the GDPR’s transparency requirements. Third, RTB incentivizes insecure data processing. We conclude that, in concept and in practice, RTB is structurally difficult to reconcile with European data protection law. Therefore, intervention by regulators is necessary.
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