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Incentive Problems in Performance-Based Online Advertising Pricing: Cost per Click vs. Cost per Action
Abstract
The multibillion-dollar online advertising industry continues to debate whether to use the cost per click (CPC) or cost per action (CPA) pricing model as an industry standard. This paper applies the economic framework of incentive contracts to study how these pricing models can lead to risk sharing between the publisher and the advertiser and incentivize them to make efforts that improve the performance of online ads. We find that, compared with the CPC model, the CPA model can better incentivize the publisher to make efforts that can improve the purchase rate. However, the CPA model can cause an adverse selection problem: the winning advertiser tends to have a lower profit margin under the CPA model than under the CPC model. We identify the conditions under which the CPA model leads to higher publisher (or advertiser) payoffs than the CPC model. Whether publishers (or advertisers) prefer the CPA model over the CPC model depends on the advertisers’ risk aversion, uncertainty in the product market, and the presence of advertisers with low immediate sales ratios. Our findings indicate a conflict of interest between publishers and advertisers in their preferences for these two pricing models. We further consider which pricing model offers greater social welfare.
This paper was accepted by J. Miguel Villas-Boas, marketing.
... By considering a value-oriented approach, construction firms should align their project selection processes with their business models while aligning business models with strategy [67]. On the other hand, AEC industry firms are exploring projects that can be offered by a product-based approach, which needs investment in different project phases, including design and digitalization investment [25]. ...
... In this model, to attract advertisers and generate revenue, firms can develop products or services and use the availability of potential clients [66]. Two pricing models are available for this revenue model: cost-per-click and cost-per-action [67]. It is possible to combine distinct revenue models, such as advertising and pay-per-use [68]. ...
... The next revenue model is advertising. To generate revenue through advertising [21], advertisers pay per click or action [67] only for smaller groups that are actual clients. Offering services or products to have access to many potential clients [66] is essential to generating revenue through the advertising model by targeting actual clients as a smaller group. ...
... In this model, to attract advertisers and generate revenue, firms can develop products or services and use the availability of potential clients [66]. Two pricing models are available for this revenue model: cost-per-click and cost-per-action [67]. It is possible to combine distinct revenue models, such as advertising and pay-per-use [68]. ...
... The next revenue model is advertising. To generate revenue through advertising [21], advertisers pay per click or action [67] only for smaller groups that are actual clients. Offering services or products to have access to many potential clients [66] is essential to generating revenue through the advertising model by targeting actual clients as a smaller group. ...
The AEC industry, including construction consulting, is experiencing digitalization through technologies like Building Information Modeling (BIM), impacting firms’ business models. The traditional hourly-based model does not align with shortened project timelines driven by digitalization, design automation, and product-based approaches. This study uses a case study approach and explores and evaluates four selected alternative revenue models (freemium, subscription, out-come-based, and performance-based) for value creation using a digital tool developed by the consulting firm. The freemium model enhances market reach but requires a high conversion rate. The subscription model offers steady recurring revenue and supports client relationships. Out-come-based and performance-based models link payments to client outcomes and performance metrics, fostering efficiency and competitive advantages. The study identified multiple target markets for this tool and emphasized the importance of a portfolio revenue model to meet specific client needs in the digital era. Implementing alternative revenue models aids return on investments and avoids the digitalization paradox. Construction consulting firms must adeptly manage these models’ complexities to address diverse client needs and leverage digital transformation opportunities.
... Both studies argued that the performance-based CPC model can incentivize publishers and advertisers to enhance advertising performance. Agarwal et al. [1] differentiated CPA from CPC. Hu et al. [9] delved deeper into the trade-offs between CPA and CPC, focusing on incentive issues. Zhu et al. [26] and Liu et al. [15] investigated hybrid auctions that allow advertisers to select their preferred pricing models. ...
... Previous research on pricing models typically uses a few representative advertisers to capture the essence of the focused pricing models while greatly simplifying the analysis. 1 For instance, [2] evaluates CPC and CPM by one agent advertiser; [9] compares CPA and CPC by two advertisers with distinct types. We follow this trend while discussing various extensions to more realistic settings in Appendix A.10. ...
In recent years, Optimized Cost Per Click (OCPC) and Optimized Cost Per Mille (OCPM) have emerged as the most widely adopted pricing models in the online advertising industry. However, the existing literature has yet to identify the specific conditions under which these models outperform traditional pricing models like Cost Per Click (CPC) and Cost Per Action (CPA). To fill the gap, this paper builds an economic model that compares OCPC with CPC and CPA theoretically, which incorporates out-site scenarios and outside options as two key factors. Our analysis reveals that OCPC can effectively replace CPA by tackling the problem of advertisers strategically manipulating conversion reporting in out-site scenarios where conversions occur outside the advertising platform. Furthermore, OCPC exhibits the potential to surpass CPC in platform payoffs by providing higher advertiser payoffs and consequently attracting more advertisers. However, if advertisers have less competitive outside options and consistently stay in the focal platform, the platform may achieve higher payoffs using CPC. Our findings deliver valuable insights for online advertising platforms in selecting optimal pricing models, and provide recommendations for further enhancing their payoffs. To the best of our knowledge, this is the first study to analyze OCPC from an economic perspective. Moreover, our analysis can be applied to the OCPM model as well.
... Another stream of literature explored the mechanism of online advertising influencing economic outcomes. Some focused on the pricing design of online advertising (Feng & Xie, 2012;Hu et al., 2016). For instance, Feng & Xie (2012) proposed a performance-based advertising model and discovered that adopting this advertising scheme profoundly impacted one fundamental function of advertising (i.e., signaling product quality). ...
... For instance, Feng & Xie (2012) proposed a performance-based advertising model and discovered that adopting this advertising scheme profoundly impacted one fundamental function of advertising (i.e., signaling product quality). Hu et al., (2016) compared the cost per click (CPC) with cost per action (CPA) pricing schema to study the economic impacts, such as purchase rates, profits, etc. Some also explored the design of keyword auction mechanisms (Du et al., 2017;Liu et al., 2010). ...
Despite the popular use of online advertising by marketers, little is known about its role in the real estate market. This research aims to investigate how online advertising affects the sales of new houses and explores potential contingent factors and the underlying mechanism. Based on a rich secondary data set and econometric models, we find that online advertising increases the sales of new houses, and the effect is stronger with lower housing prices, higher residential incomes, and lower-tier cities. Additionally, the mechanism is that advertising generates an impact via processes, such as lowering housing prices, attracting more immigrants, and reducing emigration. Lastly, the spillover effect of online advertising may not exist in the housing market. We discuss theoretical contributions and important implications to practitioners and policy makers.
... Contracts in the digital ad industry have evolved so that ad platforms only receive payment when specific user behavior-usually a click or purchase-can be linked to an ad impression (Feng and Xie 2012). Advertisers have moved from paying for ad impressions (CPM) to paying only for when consumers click on ads to reach their websites (CPC) to eventually paying ad platforms only when consumers addressed with ads conduct a predefined target action (CPA), usually a purchase (Hu et al. 2016). 1 Firms have now commonly implemented CPAbased contracts, in which they set their willingness to pay for each purchase from a consumer addressed with advertising. However, not all purchases by consumers targeted with ads correspond to actual value created through advertising: some consumers would purchase even without being served an ad. ...
... Previous research provides theoretical insights on whether firms should choose CPM-or CPC-based contracts (Asdemir et al. 2012) or on the social welfare implications of CPC-and CPA-based contracts for firms and publishers (Hu et al. 2016). In contrast, we focus on the implications of CPA-based contracts for firms and ad platforms from both a theoretical and empirical perspective. ...
In programmatic advertising, firms outsource the bidding for ad impressions to ad platforms. Although firms are interested in targeting consumers that respond positively to advertising, ad platforms are usually rewarded for targeting consumers with high overall purchase probability. We develop a theoretical model that shows if consumers with high baseline purchase probability respond more positively to advertising, then firms and ad platforms agree on which consumers to target. If, conversely, consumers with low baseline purchase probability are the ones for which ads work best, then ad platforms target consumers that firms do not want to target—the incentives are misaligned. We conduct a large-scale randomized field experiment, targeting 208,538 individual consumers, in a display retargeting campaign. Our unique data set allows us to both causally identify advertising effectiveness and estimate the degree of incentive misalignments between the firm and ad platform. In accordance with the contracted incentives, the ad platform targets consumers that are more likely to purchase. Importantly, we find no evidence that ads are more effective for consumers with higher baseline purchase probability, rendering the ad platform’s bidding suboptimal for the firm. A welfare analysis suggests that the ad platform’s bidding optimization leads to a loss in profit for the firm and an overall decline in welfare. To remedy the incentive misalignment, we propose a solution in which the firm restricts the ad platform to target only consumers that are profitable based on individual consumer-level estimates for baseline purchase probability and ad effectiveness.
This paper was accepted by Anandhi Bharadwaj, information systems.
... Contrastingly, in the traffic purchase mode, developers first decide the number of users to acquire from platforms. The fee settlement between developers and platforms is based on user actions like app downloads, registrations, and recharges, a pricing model known as cost per action (CPA) (Hu et al. 2016). Developers then entrust platforms to place information flow advertisements. ...
Against the backdrop of rapid mobile internet evolution, managing the virtual supply chain of mobile applications (apps) is increasingly critical. This study compares the impacts of two operational modes—traditional channel distribution and the rapidly emerging traffic purchase mode, fueled by the prevalence of information flow advertising—on mobile app market performance. It considers two types of mobile apps: one-time payment and continuous payment models. By constructing and deriving mathematical models, this research explores the game equilibrium between mobile app developers, channel distributors, and traffic purchase platforms under both channel distribution and traffic purchase modes. We also analyze the performance of different virtual supply chain operation modes for varying types of mobile apps. Furthermore, this study investigates the role of app quality and user retention rate in supply chain decision-making. The results indicate that app pricing is lower under the channel distribution mode compared to the traffic purchase mode. Developers should opt for the traffic purchase mode when consumer retention rates or quality preferences are low; otherwise, the channel distribution mode is preferable. By considering different operational modes' game structures, this study provides valuable managerial insights for practitioners in the mobile app field.
... In practice, sellers do not "buy" or explicitly pay for recommendations, which is a key distinction between recommendations and traditional advertising [7]. Even in cases where sellers pay for recommendations, the payment is typically performance-based, such as costper-purchase [63]. In this context, the variable costs for recommendations are proportional to product demand, since in our model, consumers purchase the products recommended to them by the platform. ...
Retail platforms have widely implemented recommender systems to provide personalized recommendations to consumers, influencing sales significantly. However, under the hybrid selling mode where platforms offer both their products and third-party sellers’ products, the profitability of a recommender system and the optimal allocation of recommendations become critical considerations. This paper introduces a game-theoretic model to investigate these issues and unveil how a recommender system and its characteristics influence prices and profits. A key finding is that the recommender system increases prices and profits only if the commission rate is high and the system is profit-oriented or inaccurate. Surprisingly, higher recommendation accuracy does not always translate into higher profits; it is advantageous only in a consumer-oriented system. Moreover, the retail platform tends to allocate more recommendations to its own product than to the third-party seller’s product, a strategy known as self-preferencing. This strategy gives the platform a competitive edge and boosts its profit compared to the third-party seller. Furthermore, the degree of self-preferencing varies with the accuracy and orientation of the recommendation system. Specifically, in a consumer-oriented system, self-preferencing increases with accuracy, while in a profit-oriented system, it decreases with accuracy.
... The CTR is the probability that a user clicks the ad when it is shown. Advertisers submit cost-perclick (CPC) bids [10,42] to show how much they are willing to pay if a user clicks. The common practice for advertisement platforms is combining predicted click-through rates (pCTR) and CPC bid to calculate effective cost per mille (eCPM) for rank to maximize the expected revenue [5,18]. ...
Click-Through Rate (CTR) prediction is a fundamental technique for online advertising recommendation and the complex online competitive auction process also brings many difficulties to CTR optimization. Recent studies have shown that introducing posterior auction information contributes to the performance of CTR prediction. However, existing work doesn't fully capitalize on the benefits of auction information and overlooks the data bias brought by the auction, leading to biased and suboptimal results. To address these limitations, we propose Auction Information Enhanced Framework (AIE) for CTR prediction in online advertising, which delves into the problem of insufficient utilization of auction signals and first reveals the auction bias. Specifically, AIE introduces two pluggable modules, namely Adaptive Market-price Auxiliary Module (AM2) and Bid Calibration Module (BCM), which work collaboratively to excavate the posterior auction signals better and enhance the performance of CTR prediction. Furthermore, the two proposed modules are lightweight, model-agnostic, and friendly to inference latency. Extensive experiments are conducted on a public dataset and an industrial dataset to demonstrate the effectiveness and compatibility of AIE. Besides, a one-month online A/B test in a large-scale advertising platform shows that AIE improves the base model by 5.76% and 2.44% in terms of eCPM and CTR, respectively.
... Affiliate marketing adalah memberikan pekerjaan periklanan kepada pihak ketiga (Pawar, 2014). Afiliasi pihak ketiga menerima pembayaran dan mendapatkan keuntungan dari penjualan yang dikonversi melalui upaya promosi mereka (Hu et al., 2016). ...
... Second, there may be misaligned incentives between advertisers and ad platforms depending on the type of contracts used. Theoretical models investigated how cost-per-impression, cost-perclicks or cost-per-action contracts induced different incentives for advertisers and ad platforms (Asdemir et al., 2012 ;Hu et al., 2016). Frick et al., 2022 recently demonstrated that CPA contracts induced ad platforms to target consumers with a high baseline probability to buy the product, independent of the ad. ...
Online advertising significantly lowers the costs of targeting individuals. This thesis studies the contributions and limitations of online advertising through 4 empirical studies based on advertiser data. Chapter 1 shows that sponsored links (search ads) benefit greatly from offline ads. I show that when increasing its offline advertising activity by 1%, a brand generates up to 0.95% additional clicks on its sponsored links. Chapter 2 focuses on the substitutability between offline and digital ads. Using a translog model, I find that offline and digital ads are limited substitutes. Digital ad formats (display and search) are highly substitutable. Chapter 3 focuses on information asymmetries in the placement of online ads. I show that cost-per-impression (CPM) contracts do not provide incentives for advertisers to make ads visible compared to cost-per-view contracts. Programmatic buying - based on advertising intermediaries - exposes the advertiser to a lower visibility and audience quality compared to direct buying. In addition, matching ads with website content results in 69% higher click-through rates than ads that only target consumers regardless of context. Finally, while ads bought from premium inventories are not more clicked, it seems to be driving out low-quality ad spaces from the standard inventories. Context effects are also discussed in Chapter 4. Using differences-in-difference and counterfactual estimations, I show that the circulation of controversial content and the degradation of Facebook’s credibility during the July 2020 boycott altered the value of ads on the platform. From June to
July 2020, Facebook ads recorded 5,000 to 10,000 fewer clicks compared to the brand’s other display campaigns. Their price also dropped. This thesis concludes that online advertising is more a complement to traditional advertising than a substitute. I also advocate for a better contextualization of advertising. This appears to be essential essential as regulation limits the use of personal data for advertising purposes.
... Customers only pay the provider when specific targets are achieved, as in a management-by-objectives system. For example, payments are often linked to certain service levels in the information technology industry, such as server availability, click rates, or response times (Hu, Shin & Tang, 2012). ...
Active price management is a central and strategic marketing instrument. It involves actively designing, steering, and developing prices. Price changes have an immediate effect and are immediately reflected in the company’s demand, sales, and profit. While the other instrumental areas create value (value creation), price captures the value of a product or service (value capture).
... Firms purchase such advertising slots from platforms and/or resellers on a cost-per-click (CPC) or cost-per-action (CPA) basis through near-instantaneous auctions during which automated systems match advertisements with target users depending on a variety of factors with the goal of maximizing utilization of the platform's advertisement bandwidth and achieving the desired effect (clicks or other actions) as fast as possible ( Amaldoss et al., 2021 ). Pricing models based on CPC and/or CPA mechanisms have frequently been used as proxies in academia ( Asdemir, Kumar, & Jacob, 2012;Hu, Shin, & Tang, 2016 ). Similar quadratic cost functions on advertisement have been extensively used in the advertising literature (e.g., Desai, 1997 ;Liu, Cai, & Tsay, 2014 ;Jørgensen & Zaccour, 2014 ;Cai et al., 2022 ). ...
... They studied how corporate advertising hitchhiking behavior affects the advertising budgets of traditional and search ads, using firms with different advertising budgets. Hu et al. [25] used incentive contracts to investigate how pay-per-click and pay-per-display models affect the risk sharing between advertisers and placeholders. ...
The huge traffic of e-commerce platforms provides a wide market for stores, but the fierce competition among stores for onsite traffic increases the cost of onsite promotion for stores, and it also reduces the effectiveness of promotion. Therefore, more and more stores on e-commerce platforms are using content platforms for offsite promotion. In the face of stores’ onsite and offsite promotion behaviors, the choice of competition and cooperation strategies among participants in the promotion process is the key to solving the problem of increasing traffic. Accordingly, this paper constructs an onsite and offsite promotion decision model consisting of an e-commerce platform, a store on the e-commerce platform, and a content platform, and it compares the results based on the decentralized decision situation, centralized decision situation, and promotion investment sharing situation. In addition, some results are presented in more detail in the form of numerical analysis. The results show that the promotion investment sharing does not change the level of onsite promotion investment, but the centralized decision makes the onsite promotion investment decrease; promotion investment sharing and centralized decision scenarios increase the promotion investment of the e-commerce platform and the content platform. When the sum of the parts of promotion investments shared by the store and the content platform for the e-commerce platform is relatively small, each participant will be willing to participate in the promotion investment sharing agreement. We believe that this study will provide important implications for the resolution of stores’ traffic dilemmas on e-commerce platforms.
... Truthful advertisements motivate the audience to purchase the promoted product and give them satisfaction feeling with the "truthful claims" (Hu et al. 2012). Nkongho (2016) emphasized that truth in the creative planning of advertisements results in honesty and get a positive attitude that ultimately makes an advertisement impactful. ...
The financial service sector involves high-risk and uncertain elements that make financial decision-making challenging; thus, information sources act as facilitators. The present study analyzed the effects of financial advertisements on investors’ behavioral intentions with perceived risk acting as a mediator and gender and marital status as moderating variables. The study empirically validates the proposed conceptual model (based on Stimulus–Organism–Response) with the interrelationship between financial advertisement antecedents, perceived risk, and investors’ investment intentions. Data were collected from 481 respondents using the survey method and analyzed using the partial least squares structural equation modeling (PLS-SEM). “Multigroup analysis (MGA)” was conducted to identify the gender and marital status intergroup moderating effects. The study confirms the significant relationship between informativeness and truthfulness with the perceived risk. The result found that credibility and truthfulness are significantly related to investor intention. In addition, the mediating effect of the perceived risk was found to be absent. Gender is moderating the relationship between credibility and investment intentions; informativeness and perceived risk; truthfulness and perceived risk, whereas marital status moderates the relationship between credibility and investment intentions; involvement and perceived risk; and truthfulness and perceived risk. The present study helps to understand millennial investment decision-making when they are exposed to various financial promotions and advertisements. This study provides insights to the government in policy framing regarding the financial advertisements essentials and disclosures that need to be focused on.
... They believe that advertising on online platforms is more beneficial than traditional forms. In addition, many scholars have also paid attention to how to set reasonable prices to attract customers through advertising [21,22]. Therefore, in this paper, we also pay attention to the impact of the advertising effect on the price setting of service providers. ...
In customer-intensive services, advertising can increase customers’ patience and bring more utility to customers. However, customers’ different perceptions of advertising can affect their utility and indirectly affect the decision making of the service provider. Thus, this paper uses the M/M/1 queueing model to study the optimal decision making of customer-intensive service providers in different markets according to the customers’ heterogeneity. We first classify customers into two categories: high sensitivity and low sensitivity, and then we analyze the consumption behavior of these two types of customers in the service system as the potential customer arrival rate increases. Finally, the optimal decisions of the service provider with different demands are determined. We find that the service provider can benefit from making optimal decisions based on market demand as the potential customer arrival rate increases. If the potential arrival rate exceeds a certain threshold, the service provider has more dominance in the market, and relevant decision making is no longer affected by the potential customer arrival rate. Furthermore, it is not always beneficial for the service provider to serve all customers regardless of whether there are low-sensitivity customers in the service system, and advertising can tap more highly sensitive customers and help to further increase the revenue of service providers. The results also show that ignoring the heterogeneity of customers’ sensitivity to advertising very likely leads to losses in revenue.
... Typically, marketing departments of the publishing companies use a metric called "cost-per-download" (CPD) -similar to the cost-per-click (CPC) metric popular in the online advertising practice [17,27]-, for their pricing policy, which is defined as the cost of the annual subscription fee divided by the number of content items downloaded by users at the customer institution. If CPD is too high, customers tend to consider reviewing their expenditure on the resource and ultimately, this may lead to a change in subscription to a lower price tier, or to the cancellation of the subscription altogether. ...
We present the first empirical study on customer churn prediction in the scholarly publishing industry. The study examines our proposed method for prediction on a customer subscription data over a period of 6.5 years, which was provided by a major academic publisher. We explore the subscription-type market within the context of customer defection and modelling, and provide analysis of the business model of such markets, and how these characterise the academic publishing business. The proposed method for prediction attempts to provide inference of customer's likelihood of defection on the basis of their re-sampled use of provider resources -in this context, the volume and frequency of content downloads. We show that this approach can be both accurate as well as uniquely useful in the business-to-business context, with which the scholarly publishing business model shares similarities. The main findings of this work suggest that whilst all predictive models examined, especially ensemble methods of machine learning, achieve substantially accurate prediction of churn, nearly a year ahead, this can be furthermore achieved even when the specific behavioural attributes that can be associated to each customer probability to churn are overlooked. Allowing as such highly accurate inference of churn from minimal possible data. We show that modelling churn on the basis of re-sampling customers' use of resources over subscription time is a better (simplified) approach than when considering the high granularity that can often characterise consumption behaviour.
... For example, Asdemir et al. [4] discussed two payment mechanisms for CPC (cost-per-click) and CPM (cost-per-thousand-impressions) for online advertising. Hu et al. [14] compared the two payment mechanisms of online advertising CPC and CPA (cost-per-action), while Liu and Viswanathan [23] compared the advantages and disadvantages of the three payment mechanisms (CPC, CPM and CPA). ...
... Cost Per Thousand Impressions (CPM). In the early days, online advertisers and publishers had simply used this model, standard to traditional media advertising, and advertisers paid according to the number of times their advertisement got delivered to consumers [28]. Cost Per Click (CPC). ...
Most knowledge payment platforms are keen to adopt a business model based on transaction revenue and advertising revenue. However, challenge with platforms has been to balance the interests of advertisers as well as their consumers. Increasing of advertising revenue, by having more advertisers may lead to higher consumers’ aversion. To address these queries, we develop a three-sided model to study the optimal pricing strategy of the knowledge payment platform. Specifically, we discuss the impact of the proportion of transaction fees charged by the platform on platform’s strategy under which the platform charges transaction fees from consumers and advertising fees from advertisers. We find that when the proportion of transaction fees charged by the platform is at a low level, the platform’s profit reaches the maximum. Once the proportion of transaction fees charged by the platform is high, which will have a negative impact on the platform’s profit although the platform adopts a strategy of increasing advertising price for advertisers. Besides, we analyze the changes of pricing strategy of knowledge payment platform under different level of network externalities. We find that with the increase of proportion of transaction fees charged by the platform and network externalities, both of the advertising price and platform’s profit are more sensitive to the proportion of transaction fees charged by the platform. On the contrary, as the degree of consumers’ aversion to advertisements increases, the platform’s profit is less sensitive to the proportion of transaction fees charged by the platform.
... A small inverse hazard rates environment is likely to arise when sellers' marginal profit through recommender systems are high relative to their costs. For example, when online targeted advertisements often have better returns than other media (Hu, Shin, and Tang (2016)) or generate more revenue per ad and higher conversion rates than non-targeted ads (Howard (2010)), the environment is likely to have small inverse hazard rates. ...
This dissertation consists of two essays that examine issues related to data - how data is generated, used and monetized. In Chapter 1, I study how intermediaries such as Amazon and Google recommend products and services to consumers for which they receive compensation from the recommended sellers. Consumers will find these recommendations usefulonly if they are informative about the quality of the match between the sellers’ offerings and the consumer’s needs. The intermediary would like the consumer to purchase the product from the recommended seller, but is constrained because consumers need not follow the recommendation. I frame the intermediary’s problem as a mechanism design problem in which the mechanism designer cannot directly choose the outcome, but must encourage the consumer to choose the desired outcome. I show that in the optimal mechanism, the recommended seller has the largest non-negative virtual willingness to pay adjusted for the cost of persuasion. The optimal mechanism can be implemented via a handicap auction. I use this model to provide insights for current policy debates. In Chapter 2, in the joint work with Mallesh Pai and Rakesh Vohra, we propose a statistical test for identifying whether a policy or an algorithm is designed by a principal with discriminatory tastes. The test can be used for identifying, for example, whether predictive policing algorithms are discriminatory against minority neighborhoods. We also argue that the marginal outcome test (Becker (1993)), the most popular test of taste-based discrimination, fails for policies. We consider a canonical setup where the principal designs a policy (algorithm) that maps signals (data) to decisions for each group, such as whether to patrol or not for each area. The principal commits to the policy, which in turn affects agents’ incentives to take action, such as whether to commit a crime. In this environment, the marginal outcome test fails because the principal not only cares about the marginal benefitof catching a criminal but how patrolling changes agents’ incentive to commit a crime. We propose a new statistical test that deviates from the marginal outcome test precisely as much as the incentive effect.
... For , the same procedure as online bidding system ensures the on forecast results naturally. However, the method above only works on Cost-Per-Mille(CPM) bidding for forecasting winning impressions and budget spend, while advertisers usually concern more about clicks or conversions in Cost-Per-Click(CPC) [2] bidding and Cost-Per-Action(CPA) [10] bidding. As for academic works, there is few work on this problem to the best of out knowledge. ...
Advertisers usually enjoy the flexibility to choose criteria like target audience, geographic area and bid price when planning an campaign for online display advertising, while they lack forecast information on campaign performance to optimize delivery strategies in advance, resulting in a waste of labour and budget for feedback adjustments. In this paper, we aim to forecast key performance indicators for new campaigns given any certain criteria. Interpretable and accurate results could enable advertisers to manage and optimize their campaign criteria. There are several challenges for this very task. First, platforms usually offer advertisers various criteria when they plan an advertising campaign, it is difficult to estimate campaign performance unifiedly because of the great difference among bidding types. Furthermore, complex strategies applied in bidding system bring great fluctuation on campaign performance, making estimation accuracy an extremely tough problem. To address above challenges, we propose a novel Campaign Performance Forecasting framework, which firstly reproduces campaign performance on historical logs under various bidding types with a unified replay algorithm, in which essential auction processes like match and rank are replayed, ensuring the interpretability on forecast results. Then, we innovatively introduce a multi-task learning method to calibrate the deviation of estimation brought by hard-to-reproduce bidding strategies in replay. The method captures mixture calibration patterns among related forecast indicators to map the estimated results to the true ones, improving both accuracy and efficiency significantly. Experiment results on a dataset from Taobao.com demonstrate that the proposed framework significantly outperforms other baselines by a large margin, and an online A/B test verifies its effectiveness in the real world.
... The economic costs of organic keywords, which is referred to in the following simply as 'cost per click' (CPC) and has an important role in this research, is understood as the implicit economic cost of SEO keywords, that is, the opportunity cost of using paid advertising. Partitioning from the SEM literature, the pricing of paid keywords has been approached through different cost models with the performance-based CPC approach, adopted by Google, being the most common pricing mechanism (Hu et al., 2016). In order to collect, measure and analyze data on search engine costs for the firm, a multitude of web analytics tools provide firms with metrics on search engine results (Gudivada et al., 2015), with SEMrush, used in this analysis, among others. ...
In digital marketing planning, keyword choice for search engine positioning is crucial for customer attraction, and the associated investment is an increasingly relevant part of the marketing budget. To what extent is it possible to optimize over time the resources allocated to positioning a brand on search engines? Often keyword costs are understood as monetary costs and the literature on keyword auctions suggests convergence towards a long-run equilibrium expenditure path. However, search engine optimization techniques, associated with implicit costs, have mainly focused on the short run. This paper suggests drawing long-term inferences from estimated cost per click, as the economic cost of organic keywords.
Web analytics software is used to gather these data for leading fashion e-commerce. A time series test of transition dynamics identifies convergence of economic keyword costs between branded and generic keywords or catch-up, as an early state of convergence, for almost all considered e-commerce.
It is within a company’s power to utilize social media in an efficient manner in order to reach their target audience. By creating targeted content and sharing it on social media platforms such as Facebook, Instagram, LinkedIn, YouTube, or TikTok, businesses can reach potential customers and expand their reach in general. One essential strategy for success in this context is the use of buyer personas. Customer profiles provide detailed insights into target groups, enabling companies to create tailored advertising messages. By identifying key demographic characteristics, interests, and behaviors, companies can create content that is relevant to potential customers and captures their interest. To gauge the efficacy of their social media advertising campaigns, companies must assess a range of metrics. By continually monitoring this data, companies can refine and enhance their strategies to attain superior outcomes. Given that different social media platforms resonate with distinct target groups, it is vital to tailor these buyer personas to align with their preferences. A holistic marketing strategy founded on comprehensive buyer personas is the cornerstone of long-term success. When coupled with rigorous performance measurement, this enables companies to disseminate their messages more effectively and foster enduring relationships with their customers.
This chapter explores how the AI-driven mechanism design framework can be applied to optimize dynamic auctions. In real-world applications such as online advertising, the platform sells ad impressions in a repeated fashion, where the buyers’ action space grows exponentially with respect to the number of auction rounds. Consequently, the design space is significantly larger, posing serious challenges in designing desirable auctions. We consider two repeated auction settings in this chapter. In the first setting (Sect. 3.1), we aim to design the so-called cost-per-action auctions, where each advertiser only needs to pay after the user has done specific actions (e.g., purchases). We design a “credit” mechanism, which has both a learning nature and an economic interpretation. Our mechanism is easy to implement and has desirable theoretic guarantees. In the second setting (Sect. 3.2), we aim to tackle the so-called second-order effect, where the advertisers change their strategies in reaction to new mechanisms. We formulate the dynamic mechanism design problem as a Markov decision process and use reinforcement learning techniques to find a solution. This framework has already been adopted by the major Chinese search engine Baidu and was highlighted in Baidu’s Q1 Financial Report of 2018 (Baidu Inc. (2018) first quarter 2018 financial reports).
Under performance-based advertising, firms pay only for measurable consumer actions, such as clicks or sales. However, these actions may result from factors other than effective advertising. For example, a high-reputation seller is more likely to generate more actions than a low-reputation seller. If such consumer actions are counted toward advertising performance, a high-quality firm is penalized because it has to pay more than necessary to signal its quality. Without addressing these paradoxes, it becomes increasingly challenging for a high-quality firm to signal its quality under the performance-based advertising scheme. Our research extends advertising signaling theory, which primarily focuses on new products without an established reputation and assumes that the advertising reaches the entire market (e.g., Milgrom and Roberts 1986 , Feng and Xie 2012 ). Instead, we consider products that may have an established reputation (e.g., newer versions of the iPhone) and allow for the scenario that not every market segment can be reached by the advertising. We identify two paradoxes that arise from performance-based advertising and discuss methods to mitigate these paradoxes by proposing different ways of measuring advertising performance.
Increasing sales and profits requires dynamical allocation of advertising budgets as online advertising demand changes over time. Online platforms offer free and subscription advertising. The impact of subscription-based pricing and free platform services on supply chain (SC) profit and advertiser profit under uncertain demand has not been extensively studied. Moreover, there is no literature addressing how subscription platforms can benefit advertisers and the supply chain. To overcome this issue, we suggest a two-stage supply chain. Three situations are contrasted using game theory: (i) when both platforms give free service (Model F); (ii) where one platform offers subscription-based service while the other offers free service (Model S); and (iii) where the subscription-based platform and the advertiser are integrated (Model I). Compared to subscription-based advertising, free advertising is more profitable for advertiser. Users who move from free platforms to subscription-based platforms earn more profit. A case study illustrates our analytical findings.
This paper studies publishers’ tradeoffs in disclosing information to advertisers in the presence of agencies, through which advertisers may coordinate bids.
In response to increased avoidance of traditional banner advertising, publishers have turned to a subtler form of display advertising called native advertising. Unlike traditional banner ads, native ads are intentionally designed to be cohesive with editorial content and assimilated into the design of the publisher’s website. We examine the performance of native advertising placements across three studies. In Study 1, we use a large dataset from a native advertising platform to examine the interplay of ad placement and ad content. We find that clicks are higher when ads are (1) delivered in-feed and (2) contain lower levels of selling intent, highlighting the interplay between the ad content and delivery. Study 2 confirms that in-feed placements experience higher clicks, but they also result in more bounces relative to in-ad placements. As a result, their effect on net visits is similar to in-ad placements at a higher cost. To further understand this phenomenon, we conducted a lab study (Study 3), which shows that when consumers are redirected to an advertiser’s site from an in-feed (versus in-ad) placement they experience higher annoyance and, ultimately, higher bounce intentions and reduced advertiser purchase intentions.
Internet protocol television (IPTV) advertising has the characteristics of both traditional TV and online advertising and mainly sells impressions through signed guaranteed contracts with advertisers. In this study, we applied the reach and frequency (R&F) contract to the IPTV sales model. Through the R&F contract, advertisers can specify the expected number of unique individuals reached and the frequency cap of the advertisements seen by each user. Considering the lack of user demographic information, we propose using viewing program information instead as the basis for user classification in targeted advertising. Advertising allocation models for two-layer optimization are established. In the upper-layer optimization, the advertising allocation proportion is optimized for all user types to minimize the publisher's under-delivery and non-representativeness of advertising delivery among user types. In the lower-layer optimization, the impact of repeated advertising on user purchase probability is considered. The specific allocation of advertisements is optimized for each user type to maximize the expected number of buyers without violating the optimization results of the upper-layer optimization. A repeated exposure-based hierarchical column generation (REHCG) algorithm is used to address the IPTV advertising scheduling problem. Numerical experiments based on actual data from the IPTV industry show that the REHCG algorithm obtains high-quality results for IPTV advertising scheduling.
Generally, static and dynamic price management strategies are distinguished.
The European Union (EU)’s General Data Protection Regulation (GDPR), with its explicit consent requirement, may restrict the use of personal data and shake the foundations of online advertising. The ad industry predicted drastic loss of revenue from GDPR compliance and has been seeking alternative ways of targeting. Taking advantage of an event created by an ad publisher’s request for explicit consent from users with EU IP addresses, the authors find that for a publisher that uses a pay-per-click model, has the capacity to leverage both user behavior and webpage content information for advertising, and observes high consent rates, GDPR compliance leads to modest negative effects on ad performance, bid prices, and ad revenue. The changes in ad metrics can be explained by temporal variations in consent rates. The impact is most pronounced for travel and financial services advertisers and least pronounced for retail and consumer packaged goods advertisers. The authors further find that webpage context can compensate for the loss of access to users’ personal data, as the GDPR’s negative impact is less pronounced when ads are posted on webpages presenting relevant content. The results suggest that publishers and advertisers should leverage webpage-content-based targeting in a post-GDPR world.
Problem definition: Participant retention is one of the significant issues faced by clinical studies. This paper analyzes the economic impact of combining two mechanisms (monetary payments to participants and effort exerted during a clinical study) observed in practice to improve retention. Methodology/results: Given an incentive scheme, under full information and information asymmetry regarding participants’ characteristics, we model the problem of identifying optimal payment and effort to improve retention for a clinical study using a nonlinear integer program. We propose polynomial-time algorithms to solve the problem under full information for a participant-specific linear payment scheme and two commonly observed incentive schemes: Fixed Payment (FP) and Logistics Reimbursement (RE). We also provide exact methods to solve the problem under information asymmetry for the FP and RE schemes. We conduct a comprehensive computational study to gain insights into the relative performance of these schemes. Under full information, the participant-specific scheme can reduce the retention cost by about 46%, on average, compared with that under the RE and FP schemes. Information asymmetry causes the RE scheme to be more favorable than the FP scheme in a wider variety of clinical studies. Further, the value of acquiring participants’ characteristics information is significant under the FP scheme compared with that under the RE scheme. Managerial implications: The determination of monetary payments is ad hoc in practice. Further, an economic analysis of the two mechanisms for improving retention in clinical studies is absent. Given the participants and the clinical study characteristics under full information and information asymmetry, our analysis enables a decision maker to identify an optimum incentive scheme, monetary payment, and effort level for improving retention. Further, our analysis allows a clinical study decision maker to assess budget requirements to improve retention and adapt the incentive payments to Institutional Review Board guidelines, if any.
Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1184 .
Researchers in the marketing and operations management literature have investigated the issue of making optimal media planning decisions for advertising. Brand marketers have recently begun using online intent advertising, which is online advertising such as search or display advertising targeted at consumer intent, for brand‐building. Unlike traditional media advertising, online intent advertising is sold through an auction of indicators of consumer intent such as keyword phrases. This practice raises questions about how media planners should incorporate online intent advertising in their decision‐making. We analyze this critical issue using a model of horizontally differentiated competing firms. Our analysis finds that traditional advertising spending strategically affects online ad bids in a way that is new to the literature. We find that even when traditional and online intent advertising have similar benefits, a firm may optimally bid more for online intent advertising per consumer reached than the maximum cost it would pay for reaching a consumer in a world with only traditional media. We also find that interestingly, a firm may be willing to pay more for traditional advertising when online intent advertising is an option than when it is unavailable. This article is protected by copyright. All rights reserved
Ad targeting technology has enabled a highly personalized delivery of online ads. Behind this development is the belief that better targeting will lead to more relevant ads. This paper challenges this lay belief by showing that irrelevant advertising can arise not necessarily from technological imperfection but also from the incentive problem embedded in the ad agency-advertisers relationship. We first demonstrate that the ad agency serving multiple advertisers may strategically allocate an ad impression to a lesser-matched, sometimes totally irrelevant, niche advertiser because future impressions can match better with the mainstream advertiser. We further find that, without a contractual obligation to serve both advertisers, the agency may not deliver completely irrelevant ads to consumers. However, another type of inefficiency can arise where the agency may not send any ad to potentially interested consumers who have a strictly positive match probability with advertisers. These inefficiencies arise due to contractual restrictions, either contractual obligations or budget constraints, when the agency serves multiple advertisers. As such, we endogenize the advertisers’ contractual requirement choices and show how the contractual obligation(s) can arise in equilibrium. Finally, we show that irrelevant ads will not disappear simply because more impressions are available in the market. Our analysis suggests that as the number of impressions increases, the irrelevant ads can persist, but the probability of receiving irrelevant ads decreases.
This paper was accepted by Dmitri Kuksov, marketing.
Funding: W. Shin gratefully acknowledges financial support from the Brian R. Gamache Endowed Professorship at the University of Florida.
Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4605 .
This paper studies the emergence of private exchanges and its impact on the display ad ecosystem.
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 CPM (cost per thousand impressions) basis. The advertiser is promised a minimum number of impressions and pays a cost‐per‐impression 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, i.e., 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. This article is protected by copyright. All rights reserved
Dijital teknolojilerin yükselişi, geleneksel medyanın reklam gelirlerinde yaşanan düşüşler, içeriğe para ödeme isteğinin azalması, kullanıcı profillerinin değişmesi, çevrimiçi üretim ve tüketimin artması, sosyal medya platformlarının yaygınlaşması gazeteciler için birtakım değişim ve dönüşümleri beraberinde getirmiştir. Bu değişim ve dönüşümler sonucunda gazeteciler yeni iş modelleri geliştirmek, yeni ürünler ortaya çıkarmak ve bunlara uygun yeni gelir modelleri oluşturmaya başlamıştır. Çalışmamızın amacı 2000’li yılların başından beri tartışılan ancak ülkemizde yeni yeni gündeme gelmeye başlayan Entrepreneurial Journalism/Girişimci gazetecilik konusunda betimsel/tanımsal bir çalışma yaparak kavramı Türkiye’deki örnekler üzerinden açıklamak ve örneklendirmektir. Bir diğer amacı ise Türkiye’de çeşitli platformlar üzerinden yayıncılık yapmaya başlayan bireysel yayıncıların faaliyetlerini, içerik türlerini, istihdam durumunu, gelir modellerini belirleyerek girişimci gazetecilik literatürüne katkı yapabilmektir. Girişimci gazetecilik kavramını açıklamak ve var olan ekosistemi tanımlamak için nitel araştırma yöntemlerinin desenlerinden olan durum çalışması deseni kullanılmıştır. Çalışma sonucunda kavramsallaştırma yapılmış ve girişimci gazeteciliğin bireysel, kurumsal ve vakıf destekli örneklerinin haritalandırılması yapılmıştır. Bunun yanında Türkiye’de yayıncılık yapan bireysel gazetecilerin yayıncılık platformları, içerik türleri, gelir modelleri, istihdam durumları incelenmiştir. Sonuç olarak girişimci gazeteciliğin Türkiye ekosisteminde bireysel yayıncıların hakimiyetinde geliştiği belirlenmiştir. Bunun yanında yaygın olarak kullanılan platform ve gelir modelleri de belirlenerek uluslararası örneklerden farklılıkları da belirlenmiştir.
We apply a stylized model to investigate the optimal revenue model for a monopolistic online platform that offers social networking services. There are three possible revenue models: an ad-sponsored model that offers a basic service, a SM that offers a premium service, and a hybrid model of both. We study a scenario in which the unit misfit cost of the premium service is lower than that of the basic service (the easy-to-use premium-service case), and a scenario in which the relationship is inverted (the hard-to-use premium-service case). We find that the ad-sponsored revenue model is always dominated by the hybrid model. When the ratio between the quality of the premium service and that of the basic service is too low (high), the hybrid (subscription-based) model is optimal, regardless of whether the unit misfit cost of a premium service is less or more than that of a basic service. Interestingly, we find that, in the easy-to-use premium-service case, as the unit misfit cost of the premium service increases, the hybrid model is more likely to become the optimal revenue model. However, in the hard-to-use premium-service case, when the unit misfit cost of the premium service is below (above) a threshold, as it increases, the subscription-based (hybrid) revenue model is more likely to become the optimal revenue model.
With increasing uncertainties in the global economy, companies are facing fierce competition. Blockchain has the potential to enhance companies’ competitiveness by streamlining processes, improving productivity and reducing costs. It is meaningful to study whether and when companies would adopt blockchain and under what conditions. We develop a game theoretic model to analyse the introduction of blockchain to companies from a big customer with bargaining power. In this study, ship operators need to decide their optimal adoption time when facing a request from a big shipper to adopt blockchain with a threat of substitution policy from the shipper. An algorithm is developed to obtain the numerical solutions of ship operators’ optimal adoption time. Our analysis suggests that 1) the substitution policy only matters to small companies and is only necessary under fixed pricing model; 2) a threshold applies for substitution ratio and cut-off time to effectively induce small companies to adopt blockchain early; 3) blockchain developers should consider mixed pricing model instead of fixed pricing model for faster and wider blockchain adoption; 4) blockchain initiators should focus more on improving the technology’s cost-effectiveness rather than rely heavily on externalities like substitution policies to promote blockchain adoption.
Ad-supported media platforms often engage price-sensitive consumers by offering content for free while reaping revenue from advertisers. Many consumers are also sensitive to advertising, however, so platforms need to carefully balance their advertising quantities with the prices they offer to consumers. Several platforms use menu pricing to achieve this goal: they offer consumers a choice between a free-use/ad-supported option or a paid-use/no-ad option. In “Optimal Price/Advertising Menus for Two-Sided Media Platforms,” DeValve and Pekeč use Lagrangian duality to establish the optimality of such menus in a model of consumers with heterogeneous advertising sensitivity, offering both theoretical justification for their use in practice as well as guidance on when and how to set positive prices. Moreover, the optimal menu characterization implies that platforms may have more of an incentive to offer menu pricing under competition (further explaining their prevalence in the competitive digital media market) and platforms primarily cater to consumers with low advertising sensitivity (suggestive of the proliferation of online advertising observed in practice).
With advance machine learning and artificial intelligence models, the capability of online trading platforms to profile consumers to identify and understand their needs has substantially increased. In this study, we use an analytical model to study whether these platforms have an incentive to profile their customers as accurately as possible. We find that “payments-for-transactions” platforms (i.e., platforms that charge for transactions that occur on the platform) indeed have such incentives to accurately profile the customers. However, surprisingly, “payments-for-discoveries” platform (i.e., platforms that charge customers for discoveries) have a perverse incentive to deviate from accurate consumer profiling. Our study provides insights into underlying mechanisms that drive this perverse incentive and discuss circumstances that lead to such a perverse incentive.
This study seeks to provide an initial understanding of the effect of behaviorally targeted advertising on advertising rates and revenues. A survey of twelve ad networks was conducted to obtain quarterly data on pricing (CPM data), conversion rates, and revenues across various types of ad segments (run of network advertising and behavioral advertising). The survey results reveal three key findings: (1) Advertising rates are significantly higher for behaviorally targeted ads. The average CPM for behaviorally targeted advertising is just over twice the average CPM for run-of-network advertising. On average across participating networks, the price of behaviorally targeted advertising in 2009 was 2.68 times the price of run of network advertising. (2) Advertising using behavioral targeting is more successful than standard run of network advertising, creating greater utility for consumers from more relevant advertisements and clear appeal for advertisers from increased ad conversion. (3) Finally, a majority of network advertising revenue is spent acquiring inventory from publishers, making behavioral targeting an important source of revenue for online content and services providers as well as third party ad networks. This study was sponsored by the Network Advertising Initiative (NAI). The NAI is a coalition of more than 40 leading online marketing companies committed to building consumer awareness and reinforcing responsible business and data management practices and standards. For a description of the NAI and a list of its members, see http://www.networkadvertising.org/index.asp (last visited Mar. 3, 2010).
This article focuses on whether banner advertising affects purchasing patterns on the Internet. Using a behavioral database that consists of customer purchases at a Web site along with individual advertising exposure, the authors measure the impact of banner advertising on current customers' probabilities of repurchase, while accounting for duration dependence. The authors model the probability of a current customer making a purchase in any given week (since the last purchase) with a survival model that uses a flexible, piecewise exponential hazard function. The advertising covariates are purely advertising variables and advertising/individual browsing variables. The model is cast in a hierarchical Bayesian framework, which enables the authors to obtain individual advertising response parameters. The results show that the number of exposures, number of Web sites, and number of pages all have a positive effect on repeat purchase probabilities, whereas the number of unique creatives has a negative effect. Returns from targeting are the highest for the number of advertising exposures. The findings also add to the general advertising literature by showing that advertising affects the purchase behavior of current (versus new) customers.
An important question that firms face in advertising is developing effective media strategy. Major improvements in the quality of consumer information and the growth of targeted media vehicles allow firms to precisely target advertising to consumer segments within a market. This paper examines advertising strategy when competing firms can target advertising to different groups of consumers within a market. With targeted advertising, we find that firms advertise more to consumers who have a strong preference for their product than to comparison shoppers who can be attracted to the competition. Advertising less to comparison shoppers can be seen as a way for firms to endogenously increase differentiation in the market. In addition, targeting allows the firm to eliminate “wasted” advertising to consumers whose preferences do not match a product’s attributes. As a result, the targeting of advertising increases equilibrium profits. The model demonstrates how advertising strategies are affected by firms being able to target pricing. Target advertising leads to higher profits, regardless of whether or not the firms have the ability to set targeted prices, and the targeting of advertising can be more valuable for firms in a competitive environment than the ability to target pricing.
Customer satisfaction incentive schemes are increasingly common in a variety of industries. We offer explanations as to how and when incenting employees on customer satisfaction is profitable and offer several recommendations for improving upon current practice. Faced with employee groups (including managers) who may have shorter time horizons than the firm, such systems enable a firm to use customer reaction to monitor implicitly how employees allocate effort between the short and long terms. These systems can be used to encourage employees to make tradeoffs that are in the best interests of the firm.
We derive optimal reward systems for an equilibrium in which the firm maximizes profits, employees maximize their expected utility, and customers choose purchase quantities based on initial reputations, employee efforts (both ephemeral and enduring), and price. The formal model shows how the reliance placed on customer satisfaction in an incentive scheme should depend upon the precision with which customer satisfaction is measured and the extent to which employees focus on the short term. Recommendations for improving upon current practice include: measure customers, former customers, and potential customers; measure satisfaction with competitors' products; disaggregate satisfaction to reflect better the performance of employee groups, and, when different customer segments have different switching costs or they vary in the precision with which their satisfaction can be measured, then measure the segments separately and assign different weights in the incentive plan.
Throughout the paper we interpret the formal results based on our experience with actual firms and the current literature. We close with a brief discussion of on-going research at field sites.
The practice of sponsored search advertising - where advertisers pay a fee to appear alongside particularWeb search results - is now one of the largest and fastest growing source of revenue for Web search engines. We model and compare several mechanisms for allocating sponsored slots, including stylized versions of those used by Overture and Google, the two biggest brokers of sponsored search. The performance of these mechanisms depends on the degree of correlation between providers' willingness to pay and their relevance to the search term. Ranking providers based on the product of relevance and bid price performs well and is robust across varying degrees of correlation. Ranking purely based on bid price fares nearly as well when bids and relevance are positively correlated (the expected regime), and is further enhanced by adding an editorial filter. Regardless of the allocation mechanism, sponsored search revenues are lower when users' attention decays quickly at lower ranks, emphasizing the need to develop better user interfaces and control features. The search engine can address initial inscience of relevance scores by modifying rank allocations over time as it observes clickthroughs at each rank. We propose a rank-revision strategy that weights clicks on lower ranked items more than clicks on higher ranked items. This method is shown to converge to the optimal (maximum revenue) ordering faster and more consistently than other methods.
In this paper we pursue three main objectives: (1) to develop a model of an intermediated search market in which matching between consumers and firms takes place primarily via paid referrals; (2) to address the important managerial question (for a search intermediary) of finding a suitable mechanism for selling referrals to firms; and (3) to characterize and analyze the firms' resulting equilibrium bidding strategies given consumers' equilibrium search behavior. To achieve these objectives we develop a two-stage model of search intermediaries in a vertically differentiated product market. In the first stage an intermediary chooses a search engine design that specifies to which extent a firm's search rank is determined by its bid and to which extent it is determined by the product offering's overall performance. In the second stage, based on the search engine design, competing firms place their open bids to be paid for each referral by the search engine. We find that the revenue-maximizing search engine design bases rankings on a weighted average of relative product performance and bid amount. The firms' bid amounts are generally nonmonotonic in product performance and a nonzero pure-strategy equilibrium of the underlying discontinuous bidding game does not exist. We derive the unique mixed-strategy Nash equilibrium and show that firms of low product performance tend to fully dissipate their rents. These rents are fully appropriated by the search intermediary and the better firm, and are therefore not socially wasteful. However, the intermediary's design choice, by attributing a positive weight to the firms' bids, generally obfuscates search results and reduces overall consumer surplus compared to the socially optimal design of providing fully transparent results ranked purely on product performance.
Advertising revenues have become a critical element in the business plans of most commercial Web sites. Despite extensive research on advertising in traditional media, managers and researchers face considerable uncertainty about its role in the online environment. The benefits offered by the medium notwithstanding, the lack of models to measure and predict advertising performance is a major deterrent to acceptance of the Web by mainstream advertisers. Presently, existing media models based on aggregate vehicle and advertising exposure are being adapted which underutilize the unique characteristics of the medium. What is required are methods that measure how consumers interact with advertising stimuli in ad-supported Web sites, going beyond mere counts of individual "eyeballs" attending to media. Given the enormous potential of this dynamic medium, academic investigation is sorely needed before cost implications and skepticism endanger the ability of the medium to generate and maintain ...
In using the generalized second-price (GSP) auction to sell advertising slots, a search engine faces several challenges. Advertisers do not truthfully bid their valuations, and the valuations are uncertain. Furthermore, advertisers are budget constrained. In this paper we analyze a stylized model of the first-page bid estimate (FPBE) mechanism first developed by Google and demonstrate its advantages in dealing with these challenges. We show why and when the FPBE mechanism yields higher profits for the search engine compared with the traditional GSP auction and the GSP auction with advertiser-specific minimum bid. In the event that a high-valuation advertiser is budget constrained, the search engine can use the FPBE mechanism to alter the listing order with the intent of keeping the high-valuation advertiser in the auction for a longer time. The resulting increase in the search engine’s profits is not necessarily at the expense of the advertisers because the combined profits of the advertisers and the search engine increase.
This paper was accepted by J. Miguel Villas-Boas, marketing.
Online advertisers often utilize multiple publishers to deliver ads to multi-homing consumers. These ads often generate externalities and their exposure is uncertain, which impacts advertising effectiveness across publishers. We analytically analyze the inefficiencies created by externalities and uncertainty when information is symmetric between advertisers and publishers, in contrast to most previous research that assumes information asymmetry. Although these inefficiencies cannot be resolved through publisher side actions, attribution methods that measure the campaign uncertainty can serve as an alternative solution to help advertisers adjust their strategies. Attribution creates a virtual competition between publishers, resulting in a team compensation problem. The equilibrium may potentially increase the aggressiveness of advertiser bidding leading to increased advertiser profits. The popular last-touch method is shown to over-incentivize ad exposures, often resulting in lowering advertiser profits. The Shapley value achieves an increase in profits compared to last-touch. Popular publishers and those that appear early in the conversion funnel benefit the most from advertisers using last-touch attribution. The increase in advertiser profits come at the expense of total publisher profits and often results in decreased ad allocation efficiency. We also find that the prices paid in the market will decrease when more sophisticated attribution methods are adopted.
To attract potential customers, retailers often advertise low prices with appeals such as "Prices start at 449." These appeals are deliberately vague in the sense that they give little information about the product to which the prices refer. The author offers an explanation of how such advertisements can construct a credible price image even with this vagueness. When retailers must incur costs in the process of selling a product, advertising low prices to lure potential consumers can backfire. This is because attracting too many consumers who are less likely to purchase the retailer's higher-priced products on the basis of vague promises imposes unwanted selling costs but yields little extra revenue. Therefore, a store with a relatively high selling cost will be dissuaded from attempting to use such a strategy. The author shows analytically that such advertising can be credible only when there is a substantial difference in retailers' costs or when the selling cost is high.
We investigate how a supply chain involving a risk-neutral supplier and a downside-risk-averse retailer can be coordinated with a supply contract. We show that the standard buy-back or revenue-sharing contracts may not coordinate such a channel. Using a definition of coordination of supply chains proposed earlier by the authors, we design a risk-sharing contract that offers the desired downside protection to the retailer, provides respective reservation profits to the agents, and accomplishes channel coordination.
The Internet is a much more accountable and measurable medium than traditional media. The unique property of the Internet being a medium with bidirectional information flows has enabled performance-based pricing models that tie online advertising payments directly to campaign measurement data such as click-throughs and purchases. These pricing models have become increasingly popular in the online advertising industry. This paper provides explanations as to when and how incorporating performance-based pricing models into advertising deals can be profitable. We argue that the publisher can make non-contractible efforts that may improve the effectiveness of advertising campaigns. These efforts are costly to the publisher. Therefore, performance-based pricing models can be used to give the publisher proper incentives to make its efforts. We derive an optimal contract that maximizes the sum of the advertiser's utility and the publisher's utility, and show that key factors that influence the use of performance-based pricing models are the importance of the publisher's incremental efforts, precision of click-through measurement, and uncertainty in the product market. We also clarify issues that are being debated in the industry, such as how the importance of the advertiser's incremental efforts and existence of non-immediate purchases affect the use of performance-based pricing models.
The article describes how one company improved banner advertising response rates by taking advantage of the medium's rich data to optimize placement. The study identified Web surfers who were frequent visitors to the banner advertiser's site. It then identified 100 Websites that these surfers tended also to visit. These sites were cluster analyzed to yield site genre definitions (affinities). In this manner a model was built to identify a group of affinities whose visitors were disproportionately likely to respond to banner advertising. These predictions were tested by placing banners on sites forecast to perform well. The average cost per response was nine times lower for sites predicted to belong to high affinity groups than low groups. © 2001 John Wiley & Sons, Inc. and Direct Marketing Educational Foundation, Inc.
We reconcile seemingly conflicting hypotheses and evidence that surface in the principal-agent literature. Specifically, we examine the literature that deals with the effect of costly monitoring on retail-organizational form. Our principal-agent model of the optimal relationship between up and downstream firms allows the principal to garner two types of imperfect signals of agent effort: sales data and behavior data. The model yields predictions that we confront with the econometric evidence, which comes from both franchising and sales-force-compensation literatures. We find that, when variation in the informativeness and in the cost of increasing the informativeness of both signals is considered, the evidence is consistent with the theory.
Does risk aversion lead to softer or fiercer competition? This paper provides a framework that accommodates a wide range of alternative assumptions regarding the nature of competition and types of uncertainty. It shows how risk aversion influences firms’ best-response strategies. Only in the case of marginal cost uncertainty does higher risk aversion make competition unambiguously softer. The risk-averse best response strategies depend on the level of fixed costs. This fact is used to analyse strategic investments in capacity and the importance of accumulated profits. The paper concludes with a discussion of ways of empirically testing for risk-averse behaviour in oligopoly.
I analyze the equilibria of a game based on the ad auction used by Google and Yahoo. This auction is closely related to the assignment game studied by Shapley–Shubik, Demange–Gale–Sotomayer and Roth–Sotomayer. However, due to the special structure of preferences, the equilibria of the ad auction can be calculated explicitly and some known results can be sharpened. I provide some empirical evidence that the Nash equilibria of the position auction describe the basic properties of the prices observed in Google's ad auction reasonably accurately.
Abstract Paid placements on search engines reached sales of over $10 billion in the U.S. last year and represent the most rapidly growing form of online advertising today. In its classic form, a search engine sets up an auction for each search word in which competing web sites bid for their sponsored links to be displayed next to the search results. We model this advertising market focusing on two of its key characteristics: (i) the interaction between the list of search results and the list of sponsored links on the search page and, (ii) the inherent dierences in click-through rates between sites. We find that both of these special aspects of search advertising have a significant eect,on sites’ bidding behavior and the equilibrium prices of sponsored links. In three extensions, we also explore (i) heterogenous valuations across bidding sites, (ii) the endogenous choice of the number of sponsored links that the search engine sells, and (iii) a dynamic model where web sites’ bidding behavior is a function of their previous positions on the sponsored list. Our results shed light on the seemingly random order of sites on search engines’ list of sponsored links and their variation over time. They also provide normative insights for both buyers and sellers of search advertising. Keywords: Internet Marketing, Position Auctions, Game Theory.
It is important to realize, however, that this result applies only to cases where each bidder is interested in at most a single unit, and there is no collusion among the bidders. As soon as we consider the more general case where an individual bidder may be interested in securing two or more of the units, while the number of bidders is still too few to produce a fully competitive market, the possibility of so arranging things that the Pareto-optimal result is achieved without impairing the expectations of the seller disappears. It is not possible to consider a buyer wanting up to two units as merely an aggregation of two single-unit buyers: combining the two buyers into one introduces a built-in collusion and community of interest, and the bid offered for the second unit will be influenced by the possible effect of this bid on the price to be paid for the first, even under the first-rejected-bid method. Where individual bidders may buy more than one indivisible unit, we are, in effect, back in a variant of the exclusive marketing-agency case, where the interests of the marketing agency are merged with those of a single monopolistic seller. In such a case, while the marketing agency need have no concern for the amounts above the competitive equilibrium price which the Pareto-optimal marketing scheme of pages 10–12 would require to be paid to itself as seller, it would be concerned for the amounts by which the revenues from the purchasers would fall short of the competitive equilibrium price, or at least the amount by which these receipts fall short of the possibly somewhat smaller revenues which could in fact be secured on the basis of any other method of approximating the efficient allocation under imperfectly competitive conditions. Nor could optimal results be obtained merely by restricting all bids to an offer to take up to a given quantity at any price below a specified price, the final terms being a price equal to the price bid by the first unsuccessful bidder, each bidder bidding more than this being allotted the amount which he specified. Under such a scheme, for any quantity that a bidder might decide to specify, it would be advantageous for him to specify as his bid price the full average value of this quantity to him, since he would prefer this quantity to be allotted at any price lower than this bid rather than be excluded altogether, and a change in his bid price within the range in which he would be successful would not affect the contract price. If a particular bidder is sure that changing the quantity he specifies will not affect the contract price, as would be the case if the change is small enough so as not to change the identity of the first unsuccessful bidder and if his demand curve is linear over the relevant range, his quantity specifications would tend to equal the quantity he would demand at the mean of the prices that he expects to result. To the extent that he is mistaken as to the ultimate price, misallocation will result. Even more serious, the resulting bids do not provide in themselves the information necessary to enable the marketing agency to determine the Pareto-optimal result.
The role of imperfect information in a principal-agent relationship subject to moral hazard is considered. A necessary and sufficient condition for imperfect information to improve on contracts based on the payoff alone is derived, and a characterization of the optimal use of such information is given.
This paper constructs a two-country (Home and Foreign) general equilibrium model of Schumpeterian growth without scale effects. The scale effects property is removed by introducing two distinct specifications in the knowledge production function: the permanent effect on growth (PEG) specification, which allows policy effects on long-run growth; and the temporary effects on growth (TEG) specification, which generates semi-endogenous long-run economic growth. In the present model, the direction of the effect of the size of innovations on the pattern of trade and Home’s relative wage depends on the way in which the scale effects property is removed. Under the PEG specification, changes in the size of innovations increase Home’s comparative advantage and its relative wage, while under the TEG specification, an increase in the size of innovations increases Home’s relative wage but with an ambiguous effect on its comparative advantage.
The authors develop two themes in the theory of incentive schemes. First, one need not always use all of the information available in an optimal incentive contract. Accounting information, which aggregates performance over time, is sufficient for optimal compensation schemes in certain classes of environments. Second, optimal rules in a rich environment must work well in a range of circumstances and cannot, therefore, be complicated functions of the observed outcome. The authors illustrate these ideas in a particular model where the agent has a rich space of controls, showing that the unique optimal compensation scheme is a linear function of profits. Copyright 1987 by The Econometric Society.