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Pricingmodelsforonlineadvertising:CPMvs.CPC

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Information Systems ResearchVol. 23, No. 3, Part 1 of 2, September 2012, pp. 804–822ISSN 1047-7047 (print) � ISSN 1526-5536 (online) http://dx.doi.org/10.1287/isre.1110.0391

© 2012 INFORMS

Pricing Models for Online Advertising:CPM vs. CPC

Kursad AsdemirFaculty of Business and Economics, United Arab Emirates University, Al Ain, United Arab Emirates, kursad.a@uaeu.ac.ae

Nanda Kumar, Varghese S. JacobSchool of Management, University of Texas at Dallas, Richardson, Texas 75083

{nkumar@utdallas.edu, vjacob@utdallas.edu}

Online advertising has transformed the advertising industry with its measurability and accountability. Onlinesoftware and services supported by online advertising is becoming a reality as evidenced by the success of

Google and its initiatives. Therefore, the choice of a pricing model for advertising becomes a critical issue forthese firms. We present a formal model of pricing models in online advertising using the principal–agent frame-work to study the two most popular pricing models: input-based cost per thousand impressions (CPM) andperformance-based cost per click-through (CPC). We identify four important factors that affect the preferenceof CPM to the CPC model, and vice versa. In particular, we highlight the interplay between uncertainty in thedecision environment, value of advertising, cost of mistargeting advertisements, and alignment of incentives.These factors shed light on the preferred online-advertising pricing model for publishers and advertisers underdifferent market conditions.

Key words : online advertising; cost per impression (CPM); cost per click (CPC); pricing models; asymmetricinformation; delegation; principal–agent model

History : Paulo Goes, Senior Editor; Giri Kumar Tayi, Associate Editor. This paper was received on October 10,2007, and was with the authors 17 months for 4 revisions. Published online in Articles in AdvanceDecember 7, 2011.

1. IntroductionWorldwide online advertising spending is projectedto reach $98 billion annually by 2012. In an effort tocapture a piece of this action, Microsoft launched anunsuccessful $47.5 billion hostile takeover bid againstYahoo! on May 3, 2008 (eMarketer 2008b, New YorkTimes 2009). This move by the world’s largest softwaremaker underlines a fundamental shift in informationtechnology from the desktop platform to online soft-ware and services. Online advertising is an indispens-able part of the business models of firms that will pro-vide advertising-supported online services. A basicunderstanding of the economics of online advertisingwill be a critical success factor for these companies.In this paper we develop a game-theoretic model thatsheds light on the fundamental economic incentivesand trade-offs that online advertisers and publishersface in choosing the right pricing model.

There has been intense debate on the most appro-priate pricing model for Internet advertising: cost perthousand impressions (CPM) or cost per click-through(CPC). In the CPM model, an advertiser pays forimpressions. In other words, the advertiser pays thepublisher when a visitor has been given an oppor-tunity to see an advertisement, i.e., an impression.This approach is closer to the traditional magazine

advertising, wherein magazine publishers are com-pensated on the circulation of their magazines. Inthe CPC model, the publisher pays only for click-throughs (click hereafter)—when a visitor clicks on anadvertisement (ad hereafter).

The impact of advertising on sales is hard to mea-sure. John Wanamaker’s famous phrase, “Half myadvertising is wasted, I just don’t know which half,”highlights this fact (Ad Age 2008). Online advertisingdistinguishes itself from traditional media advertis-ing by its measurability and accountability. By mea-surability, we mean that the performance of a cam-paign can be tracked in real time using metrics suchas clicks. By accountability, we mean that the websitescan be compensated based on these metrics. In itsearly days, these features of online advertising werethought to be its major competitive advantage overother media. The following excerpt from Wired mag-azine reflects the beliefs in that period: “The Net isaccountable 0 0 0 0 It is the highway leading marketersto their Holy Grail: single-sourcing technology thatcan definitively tie the information consumers per-ceive to the purchases they make” (Rothenberg 1998).

In fact, in April 1996, Yahoo! agreed with Proc-ter & Gamble to be compensated on the basis ofclicks (Novak and Hoffman 1997). As a result, the

804

Asdemir, Kumar, and Jacob: Pricing Models for Online Advertising: CPM vs. CPCInformation Systems Research 23(3, Part 1 of 2), pp. 804–822, © 2012 INFORMS 805

metric of accountability became the number of clicks.However, after initial successes, click rates starteddropping steadily from levels of 5% to 0.2%, raisingissues about the effectiveness of the Internet as anadvertising medium (Gaffney 2001). This trend forcedpublishers to move away from click as a perfor-mance measure. On July, 2001, CBS MarketWatch.comannounced that it would not report the number ofclicks to its advertisers unless specifically requested(I-Advertising 2001). Publishers retreated from thetotal-accountability ideal and despised click as a suc-cess measure. Not surprisingly, advertisers do notshare the same views with publishers. The followingquote from Business Week illustrates this position:

Michael Sands is tired of hearing how much the Inter-net is like television. But the marketing head of onlinetravel company Orbitz … says … “There’s a notion thataccountability is not the direction to go in,” scoffsSands, who adds: “That’s frightening. Worse, it’s acapitulation.” (Black 2001)

Despite these troubles, the CPC model regainedits prevalence by the tremendous success of searchengine advertising. Search engines invented innova-tive ways to sell advertising using the CPC modelto thousands of advertisers. However, recent indus-try statistics show that the CPM model still gener-ates a large portion of online advertising revenue(IAB 2008). This makes CPM an attractive model evento one of the most successful search engines, Google.Google, which discontinued its CPM-based PremiumSponsorship program in 2003, recently reintroducedanother CPM-based program (Lee 2005a).

Thus, reviews in the trade press and the practicesin the industry highlight a lack of any consensusbetween advertisers and publishers on an appropriatepricing model for Internet advertising. At the heartof this tension lies the fact that advertisers want theirads to be seen by their target consumers. Accordingly,they would like their ads to be placed on Web pageswhose visitors share the characteristics of their targetconsumers. With the websites changing their content(and organization) on a continuous basis, informationpertaining to the characteristics of visitors to differentpages in a publisher’s website can be prohibitivelyexpensive to acquire for an advertiser. However, thepublisher can gather such information with relativeease and use it to enhance the effectiveness of theadvertiser’s campaign. Although advertisers wouldlike the publishers to perform this service, publishersappear to have no incentive to use or acquire suchinformation to enhance the effectiveness of advertis-ing campaigns.

Consequently, one would expect advertisers to pre-fer a performance-based pricing model (CPC) to onethat does not hold the publishers accountable (CPM).

Table 1 Pricing Model Choice on Google.comfor the Search Phrase “Computer” onNovember 13, 2002

Advertiser Pricing model

SonyStyle.com CPMGateway.com CPMeBay.com CPMComputers-coupon-gateway.com CPCShopper.cnet.com CPC

However, as illustrated in Table 1, not all advertisersprefer the CPC model to the CPM model.

The choice of pricing models made by differentadvertisers is quite puzzling. One observes that forthe same search phrase, “computer,” on the samepublisher, Google.com, some advertisers prefer theCPM model to the CPC model and others preferthe CPC model to the CPM model. This behaviorcan be explained if there is a corporate policy inplace that forces the advertiser to adopt one pric-ing model regardless of the market forces at work.However, there are instances in which an advertiseruses the CPM model for some search phrases andthe CPC model for other search phrases. For example,eBay.com uses the CPM model for the search phrases“Dell” and “Hewlett-Packard” but the CPC modelfor the search phrases “Vaio” and “Pentium.”1 Thispractice suggests that at least some of the variationin advertisers’ choice of one pricing model over theother may be influenced by market factors. Sheddinglight on some of the strategic forces that may influ-ence advertisers’ and publishers’ choice of the pricingmodel is the main goal of this study.

We develop an economic model of targeting andvolume decisions in an online advertising campaign.We use a principal–agent model to capture the insti-tutional context in which CPM and CPC decisions aremade. In our model the principal (advertiser) hiresan agent (publisher) to deliver advertising.2 Agencytheory models focus on situations where the agent’seffort is unobservable and so the principal is uncer-tain about the agent’s effort level. The challenge thenis to devise compensation contracts that offer the rightincentives and insurance to the agent to engage inthe desired level of effort recognizing the stochasticrelationship between the agent’s effort and the out-put. This methodology has been extensively used invarious domains to analyze and explain behavior of

1 This observation was made at Google.com on November 13, 2002by recording the sponsored listings in the Google Premium Spon-sorship program (CPM) and Google AdWords program (CPC) forseveral search phrases.2 Throughout the paper we use “she” for the advertiser and “he”for the publisher.

Asdemir, Kumar, and Jacob: Pricing Models for Online Advertising: CPM vs. CPC806 Information Systems Research 23(3, Part 1 of 2), pp. 804–822, © 2012 INFORMS

economic agents. For example, Austin (2001) consid-ers a system development problem in which a projectmanager (principal) tries to balance incentives givento an individual software developer (agent) for stay-ing on schedule versus producing high-quality soft-ware. More recently, Iyer et al. (2005) model a supplychain management context in which a buyer (prin-cipal) faces the problem of delegating product spec-ification and/or production decisions to a supplier(agent).

In our setting, the advertiser seeking to advertiseto her target segment is uncertain about the pagesthat these consumers visit at the publisher’s website.The advertiser can induce the publisher to acquireinformation regarding pages visited by her targetmarket by engaging in a performance-based (CPC)contract. For the advertiser, a performance-based con-tract entails delegating campaign decisions to the pub-lisher and, as in the traditional agency model, tyingcompensation to the value generated from advertising.Consequently, the advertiser faces the following trade-off: she can control campaign decisions by choosingon which pages and how much to advertise with theCPM model, or she can opt for the CPC model wherethe publisher has control over decisions but also hasbetter information. We are interested in understand-ing the role market conditions play in influencing thepreference for the CPC or the CPM model.

The model provides insight on several issuesrelated to the choice of pricing models. Specifically,we address the effects of the advertiser’s target-market characteristics, competition for advertisingspace, and control over the advertising campaigndecisions on the choice of the appropriate pricingmodel for online advertising. We identify four impor-tant factors that affect the preference of the CPMmodel to the CPC model, and vice versa. Specifically,we highlight the interplay between uncertainty in thedecision environment, value of advertising, cost ofmistargeting ads, and alignment of incentives on thechoice of the pricing model. We discuss the implica-tions of these effects from the perspectives of adver-tisers and publishers.

The rest of the paper is organized as follows. In thenext section, we review the relevant literature. In §3,we introduce our models and their assumptions. Wecompare the CPC model with the CPM model in §4.Section 5 presents alternative specifications of themodels and the insights obtained. Section 6 discussesthe managerial implications for advertisers and pub-lishers. Finally, we conclude the paper with a discus-sion and future directions in §7.

2. Literature ReviewAcademic research on online advertising has beenconducted on three fronts: developing measurement

standards, understanding advertising response on theInternet, and developing models of operational adver-tising decisions such as targeting and scheduling ban-ner ads or determining the level of advertising.

Because there were no established standards inInternet advertising, some of the early work dealtwith exploring possible standards for the industry.Hoffman and Novak (2000) and Novak and Hoffman(1997) propose standard measurement constructs andpoint out potential problems in Internet advertisingpricing models. However, they do not identify themarket conditions that dictate the use of a particularpricing model. We extend this literature by investigat-ing the economic incentives influencing the choice ofa pricing model.

Marketing researchers have been interested in iden-tifying factors influencing a visitor’s response to Inter-net advertising. Using “click” as a response measure,Chatterjee et al. (2003) consider the effects of expo-sure to repeated banner advertising and competingads. Manchanda et al. (2006) find evidence that thereis temporal separation between exposure to advertis-ing and action. This problem has been recognized bythe industry, and a new metric “viewthrough,” whichmeasures the number of people who visited an adver-tiser’s website after exposure to advertising withoutclicking on the ad, has been introduced. We incorpo-rate this feature in our model by associating valuewith exposure to advertising rather than the numberof clicks. This implies that the value can be generatedeither through clicks or viewthroughs.

Substantial work has been done to improve oper-ational Internet advertising decisions. Dewan et al.(2002) build an optimal control theory model to bal-ance advertising and content on a website. Kumaret al. (2006) consider scheduling banner ads on a Webpage. Karuga et al. (2001) develop the AdPalette algo-rithm, which dynamically changes ad copies based onclick response. Langheinrich et al. (1999) develop alinear programming model for nonintrusive targetingto increase click rates. Tomlin (2000) highlights poten-tial problems with the linear programming approachand proposes a solution using traffic theory. Kohdaand Endo (1996) propose an advertising agent, whichselects ads based on consumers’ indicated prefer-ences. Baudisch and Leopold (1997) also propose user-configurable ads where the user indicates her interests.Gallagher and Parsons (1997) propose a frameworkwhere demographics of each individual are matchedagainst a defined demographic target. Even thoughour focus is on strategic issues, our study has impli-cations on ad copy (text, banner, video, etc.), design,and targeting choice under uncertainty.

The delegation issue in a general context hasattracted considerable interest in the literature. Thedelegation literatures in accounting and economics

Asdemir, Kumar, and Jacob: Pricing Models for Online Advertising: CPM vs. CPCInformation Systems Research 23(3, Part 1 of 2), pp. 804–822, © 2012 INFORMS 807

have mainly focused on decision-making rightswithin organizations (e.g., Aghion and Tirole 1997,Melumad and Reichelstein 1987). In information sys-tems, outsourcing can be considered as a delegationmechanism (Wang et al. 1997). Delegation issue alsoarises in supply chains and distribution channels (e.g.,Iyer et al. 2005). In marketing literature, delegationof pricing decisions to the sales force has been stud-ied (e.g., Lal 1986). Lal (1986) shows that a neces-sary condition for delegation to be preferred by theprincipal is that the agent is better informed thanthe principal. In this case where the agent cannotcommunicate his private information, the principal isalways weakly better off by delegating decisions tothe agent. We contribute to this literature by show-ing that the benefits of delegation may be outweighedby costs incurred because of the loss of control in theonline advertising context. Therefore, delegation maynot always be preferred by a principal even thoughthe agent is better informed. We see our work con-tributing to the literature on Internet advertising, del-egation, and agency theory. Next, we present the nota-tion and the description of our model and motivateits assumptions.

3. Model DescriptionA risk-neutral advertiser faces the problem of choos-ing where to advertise (targeting) and how much toadvertise (volume). It is well known that advertisersoften classify the consumer population into a targetmarket segment and a nontarget market segment depend-ing on how likely they are to purchase their product(Assael and Cannon 1979). Consequently, advertisingto consumers in the target segment yields a higherreturn relative to advertising to consumers in the non-target segment. We assume that the value of reachingthe target segment (exposure value) is a per impres-sion,3 and the exposure value in the nontarget seg-ment is b 4b < a5 per impression. This ensures thatthe advertiser would prefer targeted ads. However,in our model the advertiser does not know with cer-tainty which pages attract target market consumers.For instance, in an actual media plan for Samsoniteluggage, the target market segment is composed of (1)adults 25–54, household income $25,000+, businesstravelers; (2) men 25–54, household income $40,000+,business travelers; (3) adults 25–54, household income$25,000+, long trips 4+ (Barban et al. 1993). If Sam-sonite plans to advertise on Yahoo!, Samsonite may

3 Throughout the paper, impression implies one impression perunique visitor. In general, the optimal number of impressions to aunique visitor will vary depending on the advertiser and the pub-lisher. Without loss of generality, we normalize this value to one.Refer to the notation table in the appendix for a summary of thenotation used in the paper.

Figure 1 The Targeting Line and the Targeting Uncertainty Model for � ,a Particular Realization of T

dc0 1

Target segmentpages

Nontarget segmentpages�

choose to advertise on Yahoo! Travel (travelers) orYahoo! Finance (high-income segment) but may notexactly know whether these consumers share thecharacteristics of its target segment. Yahoo!, on theother hand, may acquire this information with rela-tive ease using consumers’ transaction history, Webserver log files, data collected using cookies, and mar-ket research.

To model targeting and volume decisions togetherin this setting, we develop the following model.Namely, target and nontarget segments on a web-site lie on a targeting line between zero and one(Figure 1). The idea we seek to capture here is thatthe advertiser’s target consumers may visit certainpages on the publisher’s site more often than the non-target consumers do. For simplicity we assume thatpages visited by the target consumers are not visitedby the nontarget consumers, and vice versa. The tar-geting line (between zero and one) in Figure 1 maybe interpreted as the content continuum on the pub-lisher’s website. It is possible to arrange the pagesso that pages in the interval [01T ] on the targetingline are visited by the target segment whereas thosein the interval [T 11] on the targeting line are vis-ited by the nontarget consumers. The boundary page,located at T , between target and nontarget segmentsis assumed to be unknown to the advertiser but maybe acquired by the publisher at a negligible cost. Weassume that the boundary is a random variable T thatcan be anywhere between c 40 = c = 15 and d 4c =

d = 15. We assume T has a uniform distribution F 4T 5(see Figure 1).4 The advertiser’s targeting decision isto choose a spread level s 40 = s = 15, which indicatesthat the ads will be shown in the interval [01 s].

For example, Dell Computers advertises onGoogle.com. Choosing s = 1 means that Dell Com-puters advertises on all result pages generated byGoogle.com searches. Clearly, there will be resultpages in the (d11] region such as “Edmontonweather,” “Sears Canada,” and others that may notbe visited by the target segment of Dell. In contrast,there will be pages in the [01 c5 region such as “Dellcomputer,” “Dell Inspiron,” and “Dell Inspiron 6400,”that are very likely to be visited by a potential Dell

4 We make this assumption for expositional simplicity. Our qualita-tive results will hold for any well-behaved distribution.

Asdemir, Kumar, and Jacob: Pricing Models for Online Advertising: CPM vs. CPC808 Information Systems Research 23(3, Part 1 of 2), pp. 804–822, © 2012 INFORMS

customer. Choosing a small s would correspond toa very targeted advertising campaign using resultpages like “buy Dell Inspiron 6400,” yet there are alsoresult pages that would be in the [c1d] region such as“Computer” that may or may not be visited by poten-tial customers. Volume decisions are made by settingthe intensity of the campaign x, which is the numberof impressions at each point on the targeting line. Theadvertiser thus faces the following revenue functionwhen the realization of T is � :

çA4x1 s � �5=

{

asx if s ≤ �1

4a� + b4s − �55x otherwise0(1)

It is costly to provide content, create infrastructure,and attract unique visitors for a website publisher(MarketWatch 2002). We assume that the risk-neutralpublisher’s cost of generating impressions (i.e., reach-ing unique visitors) depends on x and the cost ofdelivering the campaign depends on the total numberof impressions xs. Two natural characteristics of thepublisher’s cost function are (a) for the same numberof total impressions, cost should increase with bettertargeting; and (b) for a fixed targeting level, additionalcost of attracting another visitor should increase asthe number of impressions increase.5 Appendix A inthe online supplement6 provides an example deriva-tion of these qualitative characteristics that followsfrom Butters (1977) and Grossman and Shapiro (1984)in the economic analysis of advertising literature. Thequadratic form has been used by Tirole (1987, §7.3.2.1)and Bagwell (2007). A cost function for generatingimpressions and delivering an advertising campaignthat captures these features is C4x1 s5= x2 +kxs, wherea > k > b.7 We call k the ad serving cost; k captures thecost of serving ads and tracking and reporting impres-sions and clicks. Because k > b, the advertiser losesmoney in the nontarget segment. This, in turn, makestargeting important. We assume that k is a realizationof a random variable k ∼ U6k1 k7. Furthermore, onlythe publisher knows this value. The advertiser does

5 A more general cost function that satisfies Cx > 0, Cxx > 0, andfixing xs to a constant Cs < 0 can be used without changing thequalitative findings of our model. These characteristics are naturalbecause when an ad copy is displayed there is a certain probabil-ity of reaching a particular person. Moreover, for each impressionthe probability of reaching a person who satisfies a stricter target-ing criterion is lower because of the small number of such peoplein the population. This implies that reaching a targeted audienceis more expensive than reaching the same number of people in abroad audience because a higher number of ad copies needs to bedisplayed for reaching the targeted audience.6 An electronic companion to this paper is available as part of theonline version at http://dx.doi.org/10.1287/isre.1110.0391.7 Denote the number of total impressions or volume, M = xs, thenC = 4M/s52 + kM . It is then easy to see that for any given totalvolume M , the cost is decreasing in the spread level s.

not know the realization of the ad serving cost k; how-ever, she knows the distribution of ad serving costs.To focus on regions in which targeting is important,we assume b < k.

In the next three sections, we discuss our modelsof the two most popular compensation contractsin the online advertising industry. These modelsare principal–agent type models where a princi-pal (advertiser) hires an agent (publisher) to deliveradvertising. Of particular interest is whether a com-pensation contract can induce the publisher to acquirethe target segment boundary information. We assumethat for the advertiser it is too costly to acquire thisinformation; however, the publisher can attain thisinformation with minimal costs.

3.1. The CPM ModelWe begin with the CPM model where targeting andvolume decisions are made by the advertiser. Hence,CPM is a pricing model where decisions are central-ized. In this model, both of the decisions are observableand the publisher delivers as promised. In Figure 2,the game unfolds according to the following timelineof events.

In stage 1, the CPM contract, which specifies thecampaign decisions 4x1 s5 and the price per impres-sion 4p5, is agreed upon, and the advertiser pays forthe total number of impressions (pxs). The assumptionthat price is set by the advertiser approximates mar-kets in which prices are negotiable. For instance, thereis evidence in the trade press that although manypublishers have rate cards, often the price paid by theadvertiser is significantly lower than the quoted price(Meskauskas 2006). More recently, Google, amongothers, allows advertisers to bid a CPM/CPC pricethat it may reject if it has better offers. Furthermore,it appears to be standard industry practice whereinin the CPM model the advertiser specifies where andhow much to advertise, whereas in the CPC modelthe publisher determines where to place the ad copyand with what intensity. The assumptions regardingthe decisions made in the first stage attempt to cap-ture this institutional reality.8 In the second stage,the uncertainty in the target-segment boundary isresolved (� is realized). Regardless of this realiza-tion, the publisher delivers the promised intensity andspread in stage 3. In stage 4, the advertiser obtainsthe value generated from the campaign. Then, the

8 A recent example is Google’s introduction of CPM in its AdSenseprogram. With this model, Google lets advertisers choose specificwebsites where their ads will appear. However, with CPC Googlepicks the websites using a targeting algorithm that maximizes itsown return (Lee 2005a). Another example is that Yahoo! has movedto a search engine advertising model where the position of the adswill be determined by Yahoo!.

Asdemir, Kumar, and Jacob: Pricing Models for Online Advertising: CPM vs. CPCInformation Systems Research 23(3, Part 1 of 2), pp. 804–822, © 2012 INFORMS 809

Figure 2 Timeline of Events in the CPM Model

The advertiser chooses x, s, pThe advertiser payspxs to the publisher

The publisher delivers x, sBoundary τis realized

Value is created

Stage 2 Stage 3 Stage 4Stage 1

advertiser’s decision problem can mathematically beexpressed as

PCPM2 maxp1x1 s

{

E[

çA4x1 s1T5]

− pxs}

subject to p−C4x1 s5

xs≥ u1

(IR-1)

where C4x1 s5= x2 + kxs.In PCPM, the advertiser’s decision variables are price

per impression 4p5, the campaign intensity 4x5, andthe campaign spread level 4s5; the publisher decideswhether to accept or reject the offer. But the indi-vidual rationality constraint (IR-1) in PCPM guaranteesthat for every impression, the publisher makes at leasthis reservation utility per impression, u. As noted ear-lier, the advertiser’s bid may be rejected if it is notat least as good as a competitive bid. In practice, theadvertiser may not be fully informed about the out-side options of the publisher and/or the ad servingcosts. For simplicity, we assume that the reservationutility, u, is known to the advertiser but the ad servingcosts are not. Because the advertiser does not knowthe realization of the ad serving cost k, in (IR-1) theadvertiser guarantees that the publisher accepts thecontract by using the upper limit of the ad servingcost distribution, k. If the advertiser offers a contractbased on the average cost, the publisher may rejectsuch an offer when k is higher than the average adserving cost.

By endowing the publisher with an outside optionper impression, we capture the effect of competitionfor advertising space on the pricing model choice.When competition for advertising space is high (low)the publisher’s outside options, and hence u, will behigh (low). Therefore, u tends to increase as the com-petition for advertising space and/or the leverage ofthe publisher increases. Because the advertiser preferspaying the minimum amount possible that will makethe publisher content with the offer, she can adjust theper impression price (p5 so that in equilibrium (IR-1)binds in PCPM. The emerging ad exchange model thatbecame widely known after Yahoo!’s acquisition ofRight Media Inc., which is worth more than $680 mil-lion, is a good example of how our CPM model canbe implemented in the real world:

With ad exchanges, member advertisers specify theprice they’re willing to pay for a certain type of ad spot,

such as a banner ad that will be viewed by a femalein Boston. When a woman in Boston pulls up a Webpage of an exchange member with a banner slot avail-able, software assesses the exchange’s offer. If the priceoffered is better than the site’s minimum rate for thatpage and higher than what it can get from other sources,such as ads sold by its sales staff, the site will usu-ally accept the exchange-brokered offer. The exchange’scomputers can then deliver the winning ad to be dis-played as the Web page loads on the consumer’s PC.The exchange immediately notifies the site if it doesn’thave a buyer for the ad space, and the site can then putin a nonpaying house ad or try other means to unload iton the fly.

(Guth and Delaney 2007, B1)

Although the ad exchange model implements themodel at a per impression basis, CPM campaigns tra-ditionally span many months or years and have mul-timillion dollar budgets. Fortune Magazine reports thatcompanies such as Chrysler and Pepsi-Cola run suchadvertising campaigns on Yahoo! (Vogelstein 2005).Typically an ad agency’s creative department designsthe ad copies (banners or rich media), and the mediabuying/planning department, which has long-termrelationships with publishers, makes the campaignbuys. In this sense, our model is a reasonable abstrac-tion of the online media buying process in whichmedia buyers can have knowledge of a publisher’sminimum acceptable rate.

3.2. The CPC ModelIn the CPC model, the publisher’s compensation isbased on the number of clicks; the campaign deci-sions are thus delegated to the publisher who in turndecides on targeting 4s5 and intensity 4x5. To keep theanalysis simple, we assume that the publisher incursa fixed cost to acquire the target boundary informa-tion; we normalize this fixed cost to zero withoutany loss of generality. In the CPC model, the adver-tiser delegates the targeting and volume decisions tothe publisher and ties the compensation to an outputmeasure: the number of clicks generated, N(x1 s � �5.As in Equation (1), we define N(x1 s � �5 for a particu-lar realization of T , � as

N4x1 s � �5=

�sx if s ≤ �1

4�� +�4s − �55x otherwise0 (2)

Asdemir, Kumar, and Jacob: Pricing Models for Online Advertising: CPM vs. CPC810 Information Systems Research 23(3, Part 1 of 2), pp. 804–822, © 2012 INFORMS

In (2), the propensity of consumers in the target seg-ment and nontarget segment to click on the ad copyis assumed to be � and �, respectively. We note thatboth the number of clicks and the advertiser’s rev-enue depend on the number of impressions. In Fig-ure 3, the game unfolds in the following timeline ofevents.

In the first stage, the CPC contract, which specifiesthe price per click (�), is agreed upon. In stage 2, thetarget market uncertainty is realized. The publisherlearns this realization and then decides on the inten-sity x(�) and the spread s(�) and delivers the cam-paign in stage 3. In stage 4, the advertiser pays priceper click times the total number of clicks generatedafter the campaign. Finally in stage 5, the advertiserobtains the value generated from the campaign. Wecan write the advertiser’s optimization problem in theCPC model as

PCPC2 max�

E[

çA4x4T51 s4T5 � T5−�N4x4T51 s4T5 � T5]

subject to

E6�N4x4T51 s4T5 � T5− C4x4T51 s4T557E6x4T5s4T57

≥ u1 (IR-2)

x4T51 s4T5 ∈ arg maxx4T51 s4T5

{

�N(

x4T51 s4T5 � T)

− C4x4T51 s4T55}

1

(IC-1)

��≤ k1 k ≤ ��0 (IC-2)

In PCPC, the advertiser’s decision variable is priceper click (�); the publisher’s decision variables arethe campaign intensity 4x5 and the campaign spreadlevel 4s5. The advertiser’s objective function is theexpectation of the revenue function defined in (1)minus the price per click times the expected numberof clicks defined in (2). (IR-2) is the individual ratio-nality constraint that ensures that even the publisherwith the highest cost (k = k5 is guaranteed his reser-vation utility per impression u.

Note that the reservation utility per impression in(IR-2) is the same as that in the CPM model becauseu represents the reservation utility of displaying anadvertiser’s ad copy on the website. The issue of howthe publisher is compensated is different from whatis displayed. In both the CPM and the CPC mod-els, the same ad copy is displayed. As noted above,the parameter u represents the opportunity cost tothe publisher of doing so. In the CPM model thepublisher gets compensated for merely exposing thead copy to a visitor, and in the CPC model com-pensation results only if the visitor clicks on the adcopy. However, it is possible that the competition forad copy/advertising space may depend on the pric-ing model that is adopted. In this case, the differ-ence in the reservation utilities across the two models

will also affect the choice of the appropriate pricingmodel. We demonstrate this formally in an extensionwhere the reservation utility per impression is differ-ent across the two pricing models.9

The second constraint (IC-1) requires that the pub-lisher with cost parameter, k, chooses the intensity xand s to maximize his payoff with the knowledge ofthe true realization of T = � . Recall that because kis not known to the advertiser the price per click isset given the recognition that a publisher with costparameter k will set the campaign decisions to maxi-mize his profits.

The last set of constraints (IC-2) corresponds to theinformation selection constraints, which induce thepublisher to use the available boundary information(e.g., s = �). There are two cases in which the pub-lisher may not make informed decisions: if the adver-tiser pays on average more than k per impression(�� > k5 in the nontarget segment, the publisher willnot use the boundary information and will insteaddisplay ads on the whole website (s = 1). There-fore, (IC-2) guarantees that for any realization of k,the publisher loses money in the nontarget segment4��≤ k5. Likewise, if the advertiser pays on averageless than k per impression (�� < k5 in the target seg-ment, then the publisher will not use the informa-tion by declining the contract (s = 0). As expected,this implies that the propensity to click in the nontar-get segment has to be less than the one in the targetmarket (� < �) for the publisher to engage in target-ing. This condition reflects the belief that consumers’propensity to click tends to be higher in the targetsegment than the nontarget segment. This constrainthas implications on ad copy design. The advertiserhas to design ads that appeal to the target segment.This is well known in the industry: “If I have a mes-sage that says ‘Free $1 Million’ if you click here, I mayget lots of clicks but so what? What matters is whothese people are 0 0 0 0 Not all clicks are equal” (Sheiner2001). With these assumptions in place, we are nowready to characterize the equilibrium decisions in theCPM and CPC models.

4. Results and Comparison ofthe Models

4.1. Analysis of the CPM ModelWe first turn our attention to the solution of PCPM.We characterize this solution for a general distribu-tion F of the target segment boundary T , and thenwe present the solution when F is a uniform distri-bution. As noted earlier, the participation constraint

9 Details of this extension are available from the authors uponrequest.

Asdemir, Kumar, and Jacob: Pricing Models for Online Advertising: CPM vs. CPCInformation Systems Research 23(3, Part 1 of 2), pp. 804–822, © 2012 INFORMS 811

Figure 3 Timeline of Events in the CPC Model

The advertiserchooses �

The publisherlearns �,

chooses x(�),and sets s(�)

Boundary �is realized

Valueis created

The advertiser pays�N(x(�), s(�) | �) to the

publisher

Stage 1 Stage 2 Stage 3 Stage 4 Stage 5

(IR-1) is binding. We can solve for the total paymentpxs using (IR-1) and calculate the advertiser’s payofffunction by subtracting the total payment from therevenue function (1). Let ì4x1 s � �5 be the advertiser’spayoff function; then

ì4x1 s � �5

=

ì+4x1 s � �5= 44a−u− k5�

+ 4b−u− k54s − �55x− x2 if s > �1

ì−4x1 s � �5= 4a−u− k5sx− x2 if s ≤ �0

(3)

Using (3), we can write the advertiser’s objective func-tion in PCPM as

maxs1 x

ì4x1 s5 = E6ì4x1 s � T57

=

∫ s

cì+ dF +

∫ d

sì− dF 0 (4)

Proposition 1 presents the solution to PCPM with theassumption that F ∼ Uniform4c1d5. Following Propo-sition 1, the spread of the campaign is increasing inthe exposure value in either segment (a or b) butdecreasing in the outside options of the publisher (u)and delivery costs (k5.

Proposition 1. If u ≤ a − k and the target segmentboundary has a uniform distribution F ∼ Uniform(c1d5,the solution to PCPM is given by

s∗= c+

4a−u− k54d− c5

a− b1

x∗=

4a−u− k54s∗ + c5

41 and p∗

= u+ k+x∗

s∗0 (5)

The expected payoff of the advertiser is

ìA4CPM5= 4x∗520 (6)

The expected payoff of the publisher is

ìP 4CPM5=

(

k− k

2+u

)

x∗s∗0 (7)

Proof. See Appendix A in the online supple-ment. �

Analysis of the equilibrium strategies and their sen-sitivity to market parameters offer insights on the

interplay between the choice of campaign decisionsand uncertainty in the target segment boundary. InCorollary 1, we identify the effect of increasing thetarget segment uncertainty on the payoffs.

Corollary 1. As the target segment uncertainty4∼d− c5 increases while preserving the mean, in the CPMmodel both the advertiser and the publisher are worse off ifa+ b < 24u+ k5.

Proof. See Appendix A in the online supple-ment. �

The negative impact of higher uncertainty on theadvertiser and the publisher in the CPM model is notsurprising. This result is driven by the advertiser’sdecision problem. If the advertiser displays impres-sions on the nontarget segment pages by choosinga high spread level, s, she displays ads on pageswhere the returns are not as high as that from dis-playing impressions on the target segment pages. Incontrast, if the advertiser chooses a small s, then shemay miss a portion of the target segment. The adver-tiser’s choice of the spread level and the advertis-ing intensity depends on the relative magnitude ofthese costs. As target segment uncertainty increasesand the mean remains the same (c → c − � and d →

d+ �5, these costs increase. When the exposure valueof advertisements to the target segment (a) is not toolarge, the benefit of advertising to the target segmentis not large enough to offset the costs of advertisingto the nontarget segment. Consequently, the adver-tiser reduces both the spread and advertising inten-sity. Because the publisher’s payoff is also dependenton the advertiser’s volume and targeting decisions,this reduction makes both the advertiser and the pub-lisher worse off.

Therefore, we show that when the advertiser’sinformation quality deteriorates, this will make hercautious by not advertising to a broad audience withhigh volume. This, in turn, makes both parties worseoff. Next, we analyze the solution of the CPC model.

4.2. Analysis of the CPC ModelRecall that in the CPC model the advertiser sets theprice per click and delegates the decisions of the cam-paign decisions to the publisher. We analyze PCPC byfirst solving the publisher’s problem for any givenprice per click � and then solving the advertiser’s

Asdemir, Kumar, and Jacob: Pricing Models for Online Advertising: CPM vs. CPC812 Information Systems Research 23(3, Part 1 of 2), pp. 804–822, © 2012 INFORMS

problem taking into account the publisher’s optimalresponse. The publisher’s decision problem after thecontract is signed and � is realized is

maxx1 s

{

�N4x1 s � �5− C4x1 s5}

=

{

44��− k5� + 4��− k54s − �55x− x2 if s > �1

4��− k5sx− x2 if s ≤ �0(8)

The information selection constraints (IC-2) togetherwith (8) imply that the publisher’s optimal choice ofspread s∗

P = � . Then, the solution to (8) is

s∗

P = � and x∗

P =4��− k5�

20 (9)

Substituting s∗P 4=T 5 and x∗

P , and then multiplying(IR-2) with E6ux∗

PT7 and rearranging, the advertiser’sproblem PCPC becomes

max�

E[

çA4x∗

P 1T � T5−�N4x∗

P 1T � T5]

(10)

subject to

E[

�N4x∗

P 1T � T5− C4x∗

P 1T5−ux∗

PT]

≥ 00 (IR-2)

We present the solution to PCPC in Proposition 2.

Proposition 2. We provide the solution to PCPC intwo cases depending on whether the publisher’s individualrationality constraint (IR-2) binds in equilibrium.

(a) If a > 24u + k +

√

u2 + 4k− k543u+ k− k5/35 −

4k+ k5/2 so that (IR-2) does not bind in equilibrium, thenthe advertiser sets �∗ = 42a + k + k5/4�. And, the pub-lisher’s choice of campaign decisions is s∗

P = � or x∗P = 4a+

4k+ k5/2 − 2k5�/4. The resulting expected payoffs are

ìA4CPC5 =1

244d2

+ dc+ c254a− 4k+ k5/252 and

ìP 4CPC5 =1

484d2

+ dc+ c25

·

(

a2 − a4k+ k5+10754k+ k52 − 4kk

3

)

0

(11)

(b) If u+ k+

√

u2 +4k−k543u+ k−k5/3≤a≤24u+ k

+

√

u2 + 4k− k543u+ k− k5/35 − 4k + k5/2 so that theadvertiser participates in the contract and (IR-2) bindsin equilibrium, then the advertiser sets �u∗ = 4u + k +√

u2 + 4k− k543u+ k− k5/35/�. The publisher’s choice ofcampaign decisions is su

∗

P = �1xu∗

P = 44�u∗

�− k5�5/2. Theresulting expected payoffs are

ìA4CPCu5= 16 4d

2+dc+c254a−�u∗

�54�u∗

�−4k+k5/25

and

ìP 4CPCu5= 112 4d

2+dc+c25

·[

4�u∗

�52 −�u∗

�4k+k5+4k2 + kk+k25/3]

0

(12)

Proof. See Appendix A in the online supple-ment. �

A careful examination of Proposition 2, part (a),reveals interesting insights into the performance-based model. In the CPC model, the advertiserdelegates campaign decisions to the publisher. Thepublisher, in turn, makes decisions with perfect infor-mation. However, the publisher chooses the advertis-ing intensity x to maximize his payoff (8). Therefore,there is an incentive conflict between the publisherand the advertiser on the choice of x. The adver-tiser’s choice of the price per click �∗ in Proposition 2,part (a), tries to alleviate this conflict.

On the other hand, in Proposition 2, part (b),the price is determined by the market conditions(the publisher’s outside option), (IR-2), and so theincentive alignment role of the price per click �u∗

is different. Whereas the publisher’s choice of cam-paign intensity increases with u, the advertiser wouldreduce intensity as u gets larger, if she has control.The rationale for this conflict is that the price per click�u∗ is increasing in u. Because the publisher alreadyadvertises to all consumers in the target segment,the only way to increase his payoff is by increasingadvertising intensity. The intuition for why the adver-tiser would prefer to reduce the advertising inten-sity is simply a cost argument; as the clicks get moreexpensive, the advertiser would want to lower inten-sity. Therefore the degree of misalignment betweenthe advertiser and the publisher in the CPC modelincreases with u. We will highlight this phenomenonwhen we compare the pricing models.

In Proposition 2, the advertiser factors in the con-sumers’ propensity to click in setting the price perclick. Note that whereas both �∗ and �u∗ depend onthe propensity to click �, �∗� and �u∗

� do not. Saiddifferently, the price per click is set so that the effec-tive CPM (propensity to click times price per click)is independent of �. To see this, suppose a = 105 andk+ k = 1. Consider two cases: if �= 0025, then �∗ = 4;and if �= 005, then �∗ = 2. But in each case, the effec-tive CPM remains the same: �∗�= 1. This implies thatthe advertiser should determine price offers basedon effective CPM just as the publisher should decidewhether to accept or reject campaigns based on reser-vation utility per impression. A prominent industryexpert, Kevin Lee, has been a strong advocate of thisfinding: “To succeed with Google, marketers mustrun their campaigns using CPM-like strategies 0 0 0 ”(Lee 2002).

Because the publisher acquires target segmentinformation, uncertainty plays a different role in PCPC.We study the impact of increasing uncertainty on theadvertiser and the publisher in Corollary 2.

Corollary 2. As the target segment uncertainty4∼d− c5 increases while preserving the mean, both the

Asdemir, Kumar, and Jacob: Pricing Models for Online Advertising: CPM vs. CPCInformation Systems Research 23(3, Part 1 of 2), pp. 804–822, © 2012 INFORMS 813

advertiser and the publisher are better off using the CPCmodel.

Proof. See Appendix A in the online supple-ment. �

Recall that the target segment boundary T is a ran-dom variable with support [c1d]. As (d− c5 increasesthe range of values that T can take (i.e., the varianceof T) increases. In Corollary 1, we noted that as 4d−c5increases the advertiser lowers both the spread andintensity in the CPM model resulting in reduced prof-its for both the advertiser and the publisher. Underthe CPC model the publisher acquires the target seg-ment information and makes both the spread andintensity decisions with perfect information.10 Nowconsider the value of making decisions with perfectinformation vis-à-vis the CPM model. If target seg-ment uncertainty is low, i.e., (d − c5 is small, thenthe possibility of over or under targeting in the CPMmodel is low. However, as target segment uncertaintyincreases, the possibility of over- or undertargetingincreases in the CPM model. In contrast, regardlessof the level of uncertainty, the publisher in the CPCmodel makes decisions with information of the real-ized value of the target segment boundary, T . Hence,information is more valuable when uncertainty (d−c5is higher, and the benefits to the advertiser and thepublisher for a given mean increase with uncertainty.Thus, when all else remains the same, both the adver-tiser and the publisher are more likely to prefer theCPC model to the CPM model under higher uncer-tainty because the value of suitably adjusting the cam-paign decisions is greater when the range of (d−c5 oruncertainty is higher.

4.3. Comparison of the ModelsRecall that in the CPM model the advertiser does notdelegate campaign decisions to the publisher. Insteadshe sets the CPM rate, the advertising intensity x,and the spread s while remaining uncertain about thetrue realization of the target segment boundary T .The advertiser’s profits would be higher if the real-ization of T were known with certainty. In contrast,in the CPC model the advertiser simply sets the CPCrate � and delegates the campaign decisions to thepublisher. The publisher in turn acquires the infor-mation on the target segment boundary before mak-ing the campaign decisions. In this case, even thoughthe publisher uses better information, the advertiser’spayoff need not be as high as her payoff if the pricing

10 In the CPC model the advertiser’s and the publisher’s payofffunctions turn out to be convex functions of the realization of tar-get segment boundary. Uncertainty increases payoffs because theexpectation of a convex function over a wider range is greater thanthe expectation of the function over a smaller range.

model was CPM because the publisher makes deci-sions to maximize his payoff not the advertiser’s pay-off. Consequently, the choice of the pricing model forthe advertiser boils down to the trade-off betweenbenefits of delegating and having the publisher makebetter informed decisions versus the cost of losingcontrol over the campaign decisions.

This trade-off is consistent with observations in thetrade press. As noted earlier, Yahoo! has moved toa search engine advertising model where the posi-tion of the ads will be determined by Yahoo! Thischange resulted in less control by advertisers andbetter targeting provided by the Yahoo! algorithm.From the advertisers’ perspective delegating the cam-paign decisions to the publisher, for instance the posi-tion of the banners on the publisher’s site (s in ourmodel) can result in better targeting. The followingquote attests to this finding: “Position control and theability to predict position for a given search is mov-ing from difficult to impossible. With loss in posi-tion [control], searchers are getting more relevance”(Lee 2005b). Because position of an ad copy is animportant decision variable that determines numberof impressions and clicks, this move by Yahoo! under-scores the institutional feature of CPC that the pub-lisher should make campaign decisions with betterinformation.

To understand the determinants of pricing modelpreferences, we identify market conditions underwhich the advertiser and the publisher prefer theCPM model to the CPC model, or vice versa. Weuse the difference between expected payoffs in differ-ent models for the corresponding ranges in Proposi-tions 2(a) and 2(b). Figure 4 illustrates the possiblecombinations of preferences for specific values of theparameters (u= 3, b = −5, k = 1, k = 0081, c = 0049−� ,d = 0051 + �). The y axis is a measure of variance ofthe target segment boundary T ; we vary the range ofT with � so that T ∼ U[0049−�1 0051+�]. The graphshows the value of � on the y axis and the expo-sure value in the target segment a per impression onthe x axis. For a > 1303, Propositions 1 and 2(a) areused for comparison, and for 7.1≤ a ≤ 1303, Proposi-tions 1 and 2(b) are used for comparison. In Figure 4,Regions I and II denote market conditions in whichthe publisher prefers PCPM to PCPC (III and IV are thepublisher’s PCPC preference regions); Regions II andIII denote market conditions in which the advertiserprefers PCPM to PCPC (I and IV are the advertiser’s PCPCpreference regions). The respective boundaries in thefigure represent the curve along which the publisher(or the advertiser) is indifferent between the two pric-ing models. Notice that in Region I, the advertiserprefers PCPC to PCPM and the publisher prefers PCPM toPCPC. This is consistent with the tension that is oftendiscussed in the trade press.

Asdemir, Kumar, and Jacob: Pricing Models for Online Advertising: CPM vs. CPC814 Information Systems Research 23(3, Part 1 of 2), pp. 804–822, © 2012 INFORMS

Figure 4 The Boundary Between the CPM and the CPC Models for the Advertiser and the Publisher

Advertiser’s boundary

Publisher’s boundary

u b k

3 –5 1

k c d

0.81 0.49–� 0.51 + �

Region I (expected tension) Advertiser prefers CPC and publisher prefers CPM

Region II (consensus with CPM) Advertiser prefers CPM and publisher prefers CPM

Region III (unexpected tension) Advertiser prefers CPM and publisher prefers CPC

Region IV (consensus with CPC) Advertiser prefers CPC and publisher prefers CPC

�

I

II III

a

III

0.5

0.4

0.3

0.2

0.1

10 15 20 25 30

IV

Interestingly, there is a (rather large) region in theparameter space (Region III) where the advertiserprefers PCPM and the publisher prefers PCPC. This find-ing is counter to the prevailing wisdom; i.e., the prin-cipal prefers an input-based contract and the agentprefers to be held accountable with a performance-based contract. Further, contrary to the academic lit-erature that shows delegation is preferred by a prin-cipal when the agent is better informed (Lal 1986,Melumad and Reichelstein 1987), we show that thismay not always be true; i.e., advertiser’s profits in PCPC

do not always dominate her profits from PCPM. In ourcontext, benefits of using better information in mak-ing decisions may be less than the value lost becauseof delegating decisions to the publisher. Moreover,prior literature has not focused on the agent’s (pub-lisher’s) preference over a pricing model. To helpexplain the intuition behind our findings, we iden-tify four factors that impact the pricing model pref-erence of the advertiser and the publisher: (a) uncer-tainty over the target segment boundaries (uncertaintyeffect), (b) the value of advertising to the target seg-ment (exposure-value effect), (c) the cost of mistargetingads to consumers in the nontarget segment (mistarget-ing effect), and (d) the difference in the alignment ofincentives of the advertiser and the publisher (align-ment effect). These factors are interrelated. The inter-play of the factors may make an effect inconsequentialin one region and dominant in another. For example, astrong exposure-value or alignment effect may dimin-ish the impact of the uncertainty effect on preferences.We discuss the impact of these effects in turn below.

4.3.1. Uncertainty Effect. Corollaries 1 and 2characterize the effect of uncertainty over the targetsegment boundaries in the CPM and CPC models.

Corollary 1 states that uncertainty over the realiza-tion of the target segment boundaries has a negativeeffect on both the advertiser’s and publisher’s pay-offs in PCPM. Because decisions are made with betterknowledge of the target segment boundary in PCPC,advertising volume can be decreased or increaseddepending on the level of uncertainty. As a result,losses will be limited and gains will be higher. Uncer-tainty, thus, has a positive effect on payoffs in Corol-lary 2. In other words, the model predicts that, ceterisparibus, both advertisers and publishers are morelikely to prefer the CPC model under high uncer-tainty. Therefore, uncertainty effect favors the CPCmodel for both the advertiser and the publisher. Fig-ure 4 clearly illustrates this finding. Note that, as thevariance (�5 increases, both parties prefer the CPCmodel: Region IV in Figure 4.

4.3.2. Exposure-Value Effect. This effect moder-ates the uncertainty effect for the advertiser. Althoughthe uncertainty effect unambiguously favors the useof the CPC model for both parties, its strengthdepends on the exposure value in the target seg-ment (a5. Inspection of the optimal spread choice inCPM s∗ in (5) reveals the direction of this effect. Asexposure value goes to infinity, s∗ goes to the upperlimit of the target segment boundary distribution d4s∗ → d as a → �5. This implies that the value ofdelegating decisions to the publisher—so that he canmake better informed decisions—diminishes as expo-sure value increases. In this case, control of cam-paign decisions is more critical than value of infor-mation for the advertiser. In contrast, the publisher’sdesire to control campaign decisions increases withthe exposure value because, as the exposure valueincreases, the advertiser would choose increasingly

Asdemir, Kumar, and Jacob: Pricing Models for Online Advertising: CPM vs. CPCInformation Systems Research 23(3, Part 1 of 2), pp. 804–822, © 2012 INFORMS 815

Figure 5 The Boundary Between the CPM and CPC Models for the Advertiser and the Publisher When b Is Increased from b = −5 to b = 008

b = –5 b = 0.8

III

IIIII

I

IVIV

10 15 20 25 30

0.1

0.2

0.3

0.4

0.5

a10 15 20 25 30

a

III

IIIII

I

IV�

0.1

0.2

0.3

0.4

0.5

�

higher campaign intensities (x) than the level the pub-lisher would prefer. Therefore, exposure-value effectfavors the CPM model for the advertiser and the CPCmodel for the publisher.

4.3.3. Mistargeting Effect. This effect also mod-erates the uncertainty effect for both the advertiserand the publisher. Note that the price per impressionand effective CPM in CPC are bounded and cannotbe less than the maximum ad-serving cost (k5 plusthe publisher’s outside option u. When the value ofadvertising to consumers in the nontarget segmentb is significantly lower than k or u, then advertis-ing in this segment is simply dissipative. Althoughwe use negative values for b in some of our simu-lations, as illustrated in the right panel in Figure 5,our findings hold for positive values as well. The signof b is not critical. The mistargeting effect dependson how low b is relative to k. Said differently, whenmistargeting ads to consumers in the nontarget seg-ment is sufficiently costly, then the advertiser wouldlike to ensure that the campaign decisions are set suchthat mistargeting is minimized. Under these condi-tions the value of information on the target segmentboundaries is high so that the advertiser would pre-fer to delegate the campaign decisions to the pub-lisher. Note that in the CPM model s∗ → c as k− b →

�, which essentially means that when the value ofadvertising to consumers in the nontarget segment isvery low compared to the ad-serving cost (k5. Theadvertiser, therefore, chooses a spread so that onlythe consumers in the target segment see the banners(s∗ → c5. This very conservative decision affects boththe advertiser and the publisher’s profits adverselyin the CPM model. Under the CPC model the pub-lisher makes decisions with complete information onthe target segment boundaries, which result in higherpayoffs to both vis-à-vis the CPM model. A similarreasoning applies to k and u because s∗ → c as k +

u → a, making CPC the preferred pricing model ask increases. Figure 5 shows the expansion of CPMregions as the mistargeting effect is decreased (b is

increased from −5 to 0.8). The mistargeting effect essen-tially increases the value of information and favors the CPCmodel for both the advertiser and the publisher.

4.3.4. Alignment Effect. Note that in the CPCmodel the equilibrium solution depends upon mar-ket conditions. Proposition 2(a) characterizes the solu-tion when the exposure value a is sufficiently high. Inthis case, the participation constraint of the publisher(IR-2) is slack. In contrast, when the exposure value isnot too large, the solution is characterized as in Propo-sition 2(b), and in this case the participation constraint(IR-2) binds in equilibrium. Recall that in the CPCmodel the advertiser delegates decisions to the pub-lisher who sets the intensity and spread to maximizehis payoff, not the advertiser’s payoff. Consequently,the campaign decisions will never be perfectly alignedwith what the advertiser may desire. This misalign-ment of incentives helps explain the unexpected ten-sion in Region III in Figures 4 and 5.

We use a numerical illustration to highlight therole of this effect. For the parameter values used toplot Figure 4 (u = 31 b = −51 k = 11 k = 0081, and � =

00051 c = 00441d = 0056), the condition in Proposition2(a) is satisfied for exposure value a > 1303, and thecondition in Proposition 2(b) is satisfied for 701 < a<1303. In Table 2, we vary the exposure value a. Whenthe exposure value is sufficiently small, a= 8, the pub-lisher’s participation constraint determines the priceper click. The publisher’s CPC choice of intensity isdistorted upward vis-à-vis that which would be setby the advertiser in the CPM model (compare x =0.92 in CPM with E[x7 = 1055 in CPC). The reason is,

Table 2 Numerical Illustration of the Alignment Effect

Exposure x for CPM p for CPM Advertiser Publishervalue Model E�x� for CPC �� for CPC profit �A profit �P

a= 8 CPM 0�92 5�92 0�84 1�35CPC 1�55 7�10 0�70 2�41

a= 40 CPM 8�78 20�39 77�2 14�57CPC 4�89 20�45 47�99 24�0

Asdemir, Kumar, and Jacob: Pricing Models for Online Advertising: CPM vs. CPC816 Information Systems Research 23(3, Part 1 of 2), pp. 804–822, © 2012 INFORMS

Figure 6 The Boundary Between the CPM and the CPC Models for the Advertiser and the Publisher When U Is Increased from u= 3 to u= 5

15 20 25 30 35 40

0.1

0.2

0.3

0.4

0.5u = 3 u = 5

a

IIIII

I

IV

10 15 20 25 30

0.1

0.2

0.3

0.4

0.5

a

IIIII

I

IV

� �

III

III

although the exposure value is low, the advertiserhas to pay a high enough price per click to guar-antee publisher participation (compare 5.92 in CPMwith effective CPM (��) 7.10 in CPC), resulting inan expected advertiser CPM payoff of 0.84 and pub-lisher CPM payoff of 1.35. The respective payoffs inthe CPC model are 0.70 (advertiser is worse off) and2.41 (publisher is better off). This explains Region IIIwhen exposure value is sufficiently small.

When exposure value is large, say, a = 40, the par-ticipation constraint of the publisher is slack. The dis-tortion in the publisher’s CPC choice of intensity isnow in the opposite direction. Given the high expo-sure value, the advertiser would prefer high intensity.Thus, the effective CPM in CPC (20.45) exceeds theCPM price (20.39) because the advertiser ties com-pensation to exposure value in CPC to induce higheradvertising intensity. As a result, the publisher earns50% of what the advertiser earns (24 versus 47.99)and 65% more than his would-be CPM payoff (14.57).Yet the publisher’s expected CPC choice of intensity(E[x7 = 4089) still falls short of what the advertiserwould like (x = 8078). Consequently, the advertiserearns 38% less than her would-be CPM payoff (77.16).Therefore, we obtain the following result. The align-ment effect becomes a dominant factor when the pub-lisher’s outside option (u) is sufficiently higher orsmaller than the exposure value (a). If the differencebetween u and a is sufficiently large (negative or pos-itive), the alignment effect favors the CPM model forthe advertiser and the CPC model for the publisher.

4.3.5. Regions I (Expected Tension) and II (Con-sensus with CPM). In Figures 4 and 5, when uncer-tainty over the target segment boundaries is low,both the advertiser and the publisher prefer the CPMmodel to the CPC model (CPM provides consensusin Region II). The intuition for this follows directlyfrom Corollaries 1 and 2. For exposure values inthe neighborhood of 10, the uncertainty effect is thedominant effect for the advertiser, and as a increases,the uncertainty effect is moderated by the exposure-value effect. In other words, for higher values of a,

uncertainty has to be higher for the advertiser to pre-fer the CPC model to the CPM model. The regionwhere the advertiser prefers the CPC model and thepublisher prefers the CPM model (Region I) dependsupon the net result of the uncertainty, exposure-value,and the alignment effects. Because the alignment andexposure-value effects are dominated by the uncer-tainty effect for the advertiser, she prefers the CPCmodel in Region I.

The alignment effect explains why the publisherprefers the CPM model to the CPC model in Regions Iand II. Because CPM and effective CPM (in CPC)prices are similar in these regions, the campaigndecisions become critical for the publisher’s pref-erence. The advertiser’s campaign decisions in theCPM model are more beneficial to the publisherthan the decisions the publisher would make in theCPC model. The rationale is, although the advertiserwould choose x and s based on exposure value a, thepublisher in the CPC model would base them only ona fraction of the exposure value because of the opti-mal effective CPM (�∗� = 4a + 4k + k5/25/25. That is,the publisher would not fully internalize the exposurevalue in making her decisions. Figure 6 shows that ahigher outside-option u enlarges the publisher’s CPMregions, because CPM price always depends on u buteffective CPM does not.

5. Alternative Specifications of theCPM and CPC Models

5.1. Pricing Model Choice with Price MenusIn our base model, we analyze the setting where theprices in both the CPM and the CPC models are deter-mined with a contract offered by the advertiser whorecognizes that the publisher has outside options. It isworth noting that the base setup approximates one-on-one negotiations in which each party is informedabout the value to the other of entering into the agree-ment. Specifically, the advertiser recognizes the pub-lisher’s outside options, and the publisher recognizesthe value of advertising. Consequently, the equilib-rium characterized in the base model depends upon

Asdemir, Kumar, and Jacob: Pricing Models for Online Advertising: CPM vs. CPCInformation Systems Research 23(3, Part 1 of 2), pp. 804–822, © 2012 INFORMS 817

the characteristics of the participating agents makingthe CPM and CPC prices specific to the target mar-ket characteristics of the advertiser and the publisher.In many cases, however, the publisher may have astandard rate card, with CPM and CPC rates that areindependent of the specific advertiser’s characteris-tics. Instead, the rates may only depend on the inten-sity (x) and spread of the advertising campaign (s).

In this extension, we develop a model in whichthe publisher charges a price that depends only onthe spread and intensity of the campaign. Specifically,the price menu assumes the following functionalform:

CPM price2 p4x1 s5= �x

s+�3

CPC price2 �4x1 s5=�

�p4x1 s50

(13)

We maintain the rest of the assumptions of the basemodel in §§3.1 and 3.2. The parameters �, �, and� are exogenous. This pricing function has an intu-itive form: when the spread (s5 is high (targeting isless precise), the price is lower. In addition, when thenumber of unique impressions (x5 is high, the priceis higher. The parameter �/� captures the relativestrength of CPC over CPM prices. Recall that � is therate at which consumers in the target segment clickon the ad copy. The functional form of the price perclick has the desirable feature that when propensityto click is lower, the effective CPM does not change.Given the exogenous prices, the advertiser decides onspread (s) and intensity (x) in the CPM model, and thepublisher makes these decisions in the CPC model.This setup allows us to examine the robustness of ourfindings by allowing the publisher to determine theprice.11 The details of this setup are in Appendix B inthe online supplement. As in the base model we com-pare the profits of the advertiser and the publisher toidentify market conditions in which they would pre-fer the CPM model to the CPC model, and vice versa.These findings are depicted in Figure 7.

Figure 7 shows the preference regions and theparameter values used in addition to the parametervalues in Figure 4. Notably, comparison of Figures7(a) and 7(b) with Figures 4 and 5 reveal that thetwo main results qualitatively hold: the four prefer-ence regions exist and CPC is more likely when thereis high target market uncertainty (uncertainty effect).Moreover, low mistargeting effect (higher b) makestargeting less critical and thus expands the CPM con-sensus Region II (Figure 7). A slight difference isthat CPM provides consensus when the target marketexposure value a is high. In contrast, in Figure 4 the

11 We thank an anonymous reviewer and the area editor for encour-aging us to reflect on this issue.

Figure 7 The Boundary Between the CPM and the CPC Models withPrice Menus for b = −5 and b = 008

(a) b = –5 and � = 0.7, � = 6.5, � = 1

(b) b = 0.8 and � = 0.7, � = 6.5, � = 1

7.5 10 12.5 15 17.5 20 22.5 25

0.1

0.2

0.3

0.4

0.5

a

III II

IIV

��

7.5 10 12.5 15 17.5 20 22.5 25

0.1

0.2

0.3

0.4

0.5

a

III II

IIV

publisher prefers CPC for high levels of a (Regions IIIand IV). This difference arises because unlike in thebase model, the prices in this extension do not explic-itly depend on the individual advertiser’s parameters.In particular, price per click is not tied to exposurevalue.

5.2. Partial Delegation in the CPC ModelIn our base model the advertiser delegates both thespread and intensity decisions to the publisher. Asnoted earlier, although our assumptions in the basemodel regarding the decision variables of the adver-tiser and the publisher are consistent with institu-tional practice, in this extension we consider the casein which the advertiser delegates the targeting deci-sion to the publisher but retains control of the inten-sity decision in the CPC model. In this setting, theadvertiser can induce the publisher to acquire thetarget segment boundary information and also con-trol the intensity decision. Appendix C in the onlinesupplement presents the analysis of this alternativemodel, PCPC-A. Corollary 3 summarizes the fundamen-tal insights.12

Corollary 3. The advertiser always prefers PCPC-A toPCPM. Moreover, the advertiser may or may not prefer

12 We thank an anonymous reviewer for suggesting this extension.

Asdemir, Kumar, and Jacob: Pricing Models for Online Advertising: CPM vs. CPC818 Information Systems Research 23(3, Part 1 of 2), pp. 804–822, © 2012 INFORMS

Figure 8 The Boundary Between the CPM with a Strategic Publisher and the CPC Models for the Advertiser and the Publisher

b = –2.5 b = 0.8

10 15 20 25 30 35 40 45

a

0.1

0.2

0.3

0.4III

II

IVIV

I

10 15 20 25 30 35 40 45

a

0.1

0.2

0.3

0.4s I

II

III

IV IV

PCPC-A to PCPC. In addition, the publisher may or may notprefer PCPC-A to either PCPM or PCPC.

Proof. See Appendix C in the online supple-ment. �

In this setup, not surprisingly the advertiser prefersthe CPC model with partial delegation to the CPMmodel because she retains the control of the intensitydecision and also obtains the benefits of better infor-mation. However, compared to our base CPC model,the CPC model with partial delegation does not uti-lize the target segment information in choosing cam-paign intensity. Therefore, there is still a region wherethe advertiser may delegate the intensity decision tothe publisher. Similarly, the publisher may opt for theCPM model over the CPC model with partial dele-gation because the advertiser may choose a higherspread and intensity in the CPM model to reduce thepossibility of overtargeting. In general, we expect thatthe publisher would prefer making all the campaigndecisions. Surprisingly, there is a region where thepublisher would prefer the CPC model with partialdelegation to the CPC model. In the base CPC model,the publisher is hurt as a result of the advertiser’sstrategic reaction to the publisher’s choice of inten-sity. However, partial delegation reduces this strate-gic burden by having the advertiser set the intensity.Thus, in some regions, the publisher is better off withpartial delegation.

5.3. The CPM Model with the Publisher ChoosingIntensity and Spread

In this alternative specification, we investigate theCPM model where certain decisions are made bythe publisher. This specification will shed light onwhether the results of our base model are applica-ble to situations where the publisher has more controlover the campaign decisions in the CPM model. Inthis model, the advertiser sets the price per impres-sion p and the total number of impressions M = xs,leaving the publisher to decide on the campaign deci-sions x and s.

We provide the analysis of this model in Appen-dix D in the online supplement. In this setup, the pub-lisher will choose x and s to maximize his payoff;in equilibrium s∗ = 1. In other words, the publisherwill not target the ads. Figure 8 depicts the preferenceregions for this model. As is evident in Figure 8, themain results of the base models hold under this alter-native specification. In Figure 8, we have b = −205 incontrast to b = −5 in Figure 4. The reason is, the pub-lisher never prefers the current CPM model to the CPCmodel for b = −5. Mistargeting effect becomes a dom-inant factor (low b), because the publisher does notmake the optimal trade-offs in choosing the spread inthe current model (s∗ = 1). The CPM model of this sec-tion, thus, performs quite poorly for both the adver-tiser and the publisher under high mistargeting effect.On the other hand, for low mistargeting effect (b = 008)in Figure 8, the publisher’s CPM and the advertiser’sCPC preference regions are larger compared to thesame case in Figure 5. Therefore, under low mistarget-ing effect, the publisher is better off controlling somecampaign decisions and the advertiser is worse off bynot controlling all the decisions in the CPM model.

5.4. The CPC Model with Costly EndogenousInformation Acquisition

In this alternative CPC model, we relax the assump-tion of perfect information acquisition at no cost. The

Figure 9 The Boundary Between the CPM Model and the CPC Modelwith Costly Information Acquisition

7.5 10 12.5 15 17.5 20 22.5 25

0.1

0.2

0.3

0.4A = 0.1A = 3.0

I

II IIIIII

IV

Asdemir, Kumar, and Jacob: Pricing Models for Online Advertising: CPM vs. CPCInformation Systems Research 23(3, Part 1 of 2), pp. 804–822, © 2012 INFORMS 819

publisher can now reduce the level of initial uncer-tainty (d− c) by an amount � at a fixed cost of infor-mation acquisition (A/2)(d − c − �52, where A ≥ 0.This cost function is highest when perfect informa-tion is acquired (�= 0) and lowest when no informa-tion is acquired (� = d − c). Furthermore, the choiceof � is endogenous and made by the publisher. Weprovide the details of the statement and analysis ofthis model in Appendix E in the online supplement.Although all the qualitative results of our base mod-els hold with this extension, not surprisingly, intro-ducing costly information acquisition makes the CPCmodel less desirable compared to the CPM model(see Figure 9).

6. Managerial Implications6.1. Implications for AdvertisersAn advertising campaign usually employs differentpricing models and runs on a number of websites.The campaign may include text, display, and videoads. The advertiser’s problem is to choose a portfolioof websites and ad networks that employ differentpricing models. As the following quote emphasizes,one of the fundamental issues is to manage downsiderisk due to the uncertainty effect exacerbated by themistargeting effect:

CPA [or CPC] offers guaranteed results, while CPMcarries the risk that nobody will click through and yourinvestment vanishes into the ether. (Limbach 2002)

The source of uncertainty may stem from theadvertiser’s business model. Advertisers with nicheproducts and small target markets in the consumerpopulation face higher uncertainty. For a small adver-tiser, it is difficult to predict whether a website’s audi-ence belongs to its narrowly defined target market.For example, for a pure online retailer without a phys-ical presence, target market is constrained, amongother things to be consumers who are willing to shoponline. On the other hand, for a national brand, tar-get market can be defined in terms of age categoryand gender. Table 3, which shows the pricing modelchoice on the same website, supports this prediction.CPM advertisers are national brands, and they attractsignificantly more traffic than CPC advertisers. Forexample, whereas Classmates.com has a traffic rank-ing of 804, TCAfutures.com has a ranking of 428,877.

When the uncertainty effect’s impact is slight, thenCPM pricing, which provides more control over tar-geting and volume (intensity) decisions, becomesmore desirable, as suggested by the following excerptfrom an industry publication:

Many marketers are wedded to CPA pricing, whenthey could generate stronger performance by purchas-ing on a CPM basis. Since the expected value of CPM

Table 3 Pricing Model Choice at NYTimes.com’s Business Section inJuly 2008

Advertiser Log10(Traffic rank) Pricing model

Classmates.com 209 CPMChevrolet.com 307 CPMCDW.com 401 CPMSpanishschool.uninter.edu.mx 506 CPCTCAfutures.com 606 CPCMandus-Forex.com 704 CPC

Note. See Appendix A in the online supplement regarding the data collectionprocedure for this table.

inventory is already known to a network, by agree-ing to CPM pricing on highly targeted inventory, mar-keters can better manage volume of their targetedcampaigns and potentially pocket a significant ROIimprovement. In CPA pricing, the network will apply“black box” targeting to maximize its own yield. InCPM pricing, the advertiser may be able to employits own targeting schemes to procure highly relevantimpressions at a lower effective cost. (Howe 2005)

Our model can also explain the popularity of CPMor branding campaigns on large portals among largeadvertisers (eMarketer 2008a). National brands havevery large target markets with broad interests. There-fore, uncertainty effect is small for national brands.Moreover, attracting people with broad interests ischeaper per impression than attracting a targetedaudience. This, in turn, implies that the price perimpression would be lower for large campaigns.Because the probability of covering the target seg-ment is high and the cost is low, CPM emerges asthe most suitable pricing model for large portals andlarge advertisers.

Exposure value and alignment effects also play crit-ical roles in the advertiser’s preference for a pric-ing model, especially when exposure value is high.Because CPC prices are value based, CPC is moreexpensive for these advertisers (see Table 2 witha= 40). Recent discussions in the trade press furtherbuttress the importance and validity of this finding:

It may be asked why an advertiser would purchaseinventory based on a CPM price structure when inven-tory might be available based on a pay-for-performanceprice structure. The reason is that sometimes, depend-ing on performance, CPM-based buys can actuallyyield lower costs per action than CPC buys.

With a CPC (cost per click) or CPA (cost per acquisi-tion) buy, an advertiser is locked to a cost per responseregardless of whether or not response rates rise or fall.By fixing this variable, one manages the downside riskof possible poor performance (e.g., weak creative orpoorly targeted placements). But if response is good orimproves, the advertiser is still beholden to that costper response. (Meskauskas 2005)

Asdemir, Kumar, and Jacob: Pricing Models for Online Advertising: CPM vs. CPC820 Information Systems Research 23(3, Part 1 of 2), pp. 804–822, © 2012 INFORMS

It is worth noting that the alignment effect is alsoimportant in Table 3, as it may not be feasible for tca-futures.com to pay NYTimes.com’s CPM rates.

6.2. Implications for PublishersPublishers ultimately decide what offers to acceptfrom advertisers. A publisher faces advertisingdemand from advertisers with different character-istics and preferences. For this reason, the onlineadvertising medium evolved such that several pub-lishers offer different pricing models that use sepa-rate parts of a Web page and different formats (e.g.,CPC for text ads, CPM for display ads). This strat-egy reflects a need to satisfy advertisers’ needs. Forexample, publishers with websites associated withhigh uncertainty effect—without past performancehistory or with volatile results—are more likely tooffer performance-based deals. These publishers can-not attract advertising demand, because CPM adver-tisers would reduce their business volume due to highuncertainty and mistargeting effects. The publisher,thus, may have to accept CPC deals to attract adver-tisers, as the following quotation recommends:

CPM based fee elements are more suitable for websiteswhich are successful in terms of track records of brandawareness, responses, or other advertiser’s target, 0 0 0 0[CPC]-based fee elements are more suitable where theonline property cannot demonstrate that track record,or where there’s an element of uncertainty 0 0 0 0 Allads are experiments, some more certain than others.(Baugh 2001)

On the flip side, exposure-value effect wouldencourage publishers to opt for CPC deals eventhough the advertisers are willing to offer CPM deals.For example, search engines successfully adoptedand popularized CPC pricing. Google switched fromoffering both models to only the CPC-based GoogleAdWords program (see New York Times 2009). Inter-estingly, in 2005, Google reintroduced the CPM modelin its AdWords auction system for placement-targetedads, where it displays ads on its network of part-ner websites. The stated reason for offering CPM is“[a]dvertisers have told us that CPM pricing is atool they like to have available for their AdWordscampaigns 0 0 0 0 As a result, AdWords has made CPMpricing available to those who prefer it” (Google 2006).

However, not all publishers can be successful withCPC pricing. As traditional agency theory predicts,performance-based compensation will attract agentswith higher abilities (Prendergast 1999). As a result,publishers with good targeting capabilities and abilityto produce good results would prefer CPC:

0 0 0 industry rates average about 0.25% from what I hearand read. On smartly targeted sites 0 0 0you can beat thatrate. We regularly clear CTR [click-through] rates of 3%

to 14%, mainly because we keep our advertisers withina narrowly defined brand-compatibility 0 0 0 0

(Frankel 2001)

What matters most is targeting the right market.

(Hopkins 2001, emphasis added)

In summary, online publishers face a trade-offbetween pleasing their advertisers and using theircapabilities to maximize their own profits. Natu-rally, this will segment the advertising market onthe demand side with respect to the different adver-tiser preferences. To meet the needs of these differentsegments, publishers should offer a portfolio of dif-ferent pricing models, as the NYTimes.com exampleillustrates in Table 3.

7. ConclusionOnline advertising will increasingly become an indis-pensable element of information technology compa-nies’ business models. We developed a model ofadvertising on a website and explained the role of thetwo most popular pricing models for online advertis-ing using a game-theoretic framework. We identifiedseveral factors that influence the choice of a pricingmodel: uncertainty effect, exposure-value effect, mis-targeting effect, and alignment effect. These factorsmay lead to conflicts between publishers and adver-tisers, and we highlighted the role of market charac-teristics on these factors.

One possible direction for future research is inves-tigating the impact of advertiser competition on thepricing model choices. We conjecture that competi-tion is a control dimension. A reasonable assumptioncan be made such that a publisher wants to increasethe level of advertiser competition, and advertiserswant to reduce direct competition. Because CPC ischaracterized by the publisher making decisions withbetter information, the publisher may act to intensifycompetition. Considering this incentive on the pub-lisher side, the advertisers may opt for CPM pricing,where they retain the control of targeting. This paperidentifies market conditions in which the advertiserand publisher’s preference for CPM or CPC coin-cide (diverge). It would be interesting, particularlywhen the two parties prefer different pricing policies,to develop a mechanism to resolve the conflict. Thiscould be another promising avenue for future work.13

Electronic CompanionAn electronic companion to this paper is available aspart of the online version at http://dx.doi.org/10.1287/isre.1110.0391.

13 We thank an anonymous reviewer for this suggestion.

Asdemir, Kumar, and Jacob: Pricing Models for Online Advertising: CPM vs. CPCInformation Systems Research 23(3, Part 1 of 2), pp. 804–822, © 2012 INFORMS 821

Appendix. Notation Table

Model Parametersa: exposure value in the target segment.b: exposure value in the nontarget segment.u: publisher’s outside option.�: the propensity to click in the target segment.�: the propensity to click in the nontarget segment.T ∼ U6c1 d7: unknown location of the target seg-

ment boundary with realization � for 0 ≤ c ≤ d ≤ 1.k ∼ U6k1 k7: random variable for the ad serving cost

with realization k.

Decision Variabless: campaign spread level corresponds to advertis-

ing in 601 s].x: campaign intensity, the number of impressions

at each point on the targeting line.p: price per impression in the CPM model.�: price per click in the CPC model.M = xs: total number of impressions.

FunctionsçA4x1 s � �5: advertiser’s revenue function given

T = � .ì4x1 s � �5: advertiser’s payoff function given T = � .N4x1 s � �5: the number of clicks given T = � .ìA4CPM51 ìA4CPC5: advertiser’s equilibrium CPM

and CPC payoffs.ìP 4CPM51 ìP 4CPC5: publisher’s equilibrium CPM

and CPC payoffs.

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