ECONOMIC ISSUES

A Brief Primer on the

Economics of Targeted Advertising

by 

Yan Lau 

Bureau of Economics 

Federal Trade Commission 

January 2020 
Federal Trade Commission

Joseph J. Simons Chairman 

Noah Joshua Phillips Commissioner 

Rohit Chopra Commissioner 

Rebecca Kelly Slaughter Commissioner 

Christine S. Wilson Commissioner 

Bureau of Economics

Andrew Sweeting Director 

Andrew E. Stivers Deputy Director for Consumer Protection 

Alison Oldale Deputy Director for Antitrust 

Michael G. Vita Deputy Director for Research and Management 

Janis K. Pappalardo Assistant Director for Consumer Protection 

David R. Schmidt Assistant Director, Oÿce of Applied Research and Outreach 

Louis Silva, Jr. Assistant Director for Antitrust 

Aileen J. Thompson Assistant Director for Antitrust 

Yan Lau is an economist in the Division of Consumer Protection of the Bureau of 

Economics at the Federal Trade Commission. The views expressed are those of the 

author and do not necessarily refect those of the Federal Trade Commission or any 

individual Commissioner. 

ii 
Acknowledgments

I would like to thank Andrew Stivers and Jan Pappalardo for invaluable feedback on 

numerous revisions of the text, and the BE economists who contributed their thoughts 

and citations to this paper. 

iii 
Table of Contents

1 Introduction 1 

2 Search Costs and Match Quality 5 

3 Marketing Costs and Ad Volume 6 

4 Price Discrimination in Uncompetitive Settings 7 

5 Market Segmentation in Competitive Setting 9 

6 Consumer Concerns about Data Use 9 

7 Conclusion 11 

References 13 

Appendix 16 

iv 
1 Introduction

The internet has grown to touch a large part of our economic and social lives. This 

growth has transformed it into an important medium for marketers to serve advertising. 

Internet ad expenditure in the United States totaled $69.2 billion in 2017, representing 

35% of total ad expenditures across all media, compared to just 19% in 2012 (Zenith, 

2018). 

A crucial economic di ̇erence between internet advertising and more conventional 

forms of advertising is online technology’s ability to more precisely target ads at consumers exhibiting specifc characteristics based on browsing history and (derived) demographics (Goldfarb, 2014).1 To harness its value, targeted online advertising necessitates the mass collection of consumers’ data as they traverse cyberspace. These tracking 

activities have become more common over the years, but have also come under increasing public scrutiny since being introduced (Evans, 2009; Goldfarb and Tucker, 2011b). 

Most recently, policy makers in the European Union responded to broad consumer privacy concerns with the adoption of the General Data Protection Regulation (GDPR). 

One aim of the GDPR and similar policies is to limit the collection of consumer data 

by websites for the purposes of serving targeted ads online. 

This paper provides an economic approach to thinking about targeted online advertising, specifcally with regard to the collection and use of personal data to enable 

targeting. Restricting the collection of personal data necessarily limits the use of targeted ads by frms to reach potential customers. I consider the e ̇ects of moving away 

from data collection and targeted ads, with particular focus towards the e ̇ects on consumers, and discuss the theoretical and empirical research conducted to address this 

issue. 

1While conventional forms of advertising do target to some extent (e.g. direct mailers), targeting 

of online ads is much more pervasive and accelerated. 

1 
Figure 1: Ad-supported Business Model with Targeted Ads 

Consumers Websites

Firms

Digital Goods/Services

Targeted Ads

Personal Data

Analytics

$ for Ads 

& Analytics

$

Advertised

Goods/Services

To properly consider these policy implications, it is important to frst understand 

the current ad-supported business model that sustains much of the online economy.2

The diagram in Figure 1 depicts interactions between the economic actors within (a 

simplifed version of) this business model: consumers, websites, and frms producing 

the advertised product. Websites provide consumers with digital goods and services 

(e.g. news articles, videos, email, search), alongside which targeted ads appear. Websites receive money (via intermediaries such as ad networks) from frms that make the 

advertised products, and that specify which consumers they want targeted. In order to 

serve the targeted ads, websites use consumers’ personal data (e.g. IP addresses, browsing habits tracked using cookies, purchase history, behavioral patterns) as a basis for 

targeting. These data are then aggregated, packaged and provided by websites to frms 

(again, via intermediaries) as analytics for marketing purposes (e.g. to better target 

future ads or understand customers). Lastly, consumers—having viewed the targeted 

ads—purchase advertised goods and services from frms. An important aspect of this 

business model is that consumers are, in part, “paying for” the websites’ “free” digital 

2For an in-depth economic perspective on the structure of the online advertising industry, see 

Evans (2009). 

2 
goods and services with their personal data. The presence of advertising implies they 

also pay with their time and attention (Bagwell, 2007). 

In a world where the transfer of personal data from consumers to websites is disallowed—an extreme case no doubt, but one which serves the purposes of this exercise—it 

becomes infeasible for websites to serve targeted ads to consumers (they no longer have 

information on which to base targeting) or to provide analytics to frms. This policy 

would likely give rise to two alternatives against which I compare the e ̇ects of targeted 

advertising. 

Figure 2: Ad-supported Business Model with Untargeted Ads 

Consumers Websites

Firms

Untargeted Ads

$ for Ads

$

Advertised

Goods/Services

Digital Goods/Services

The frst alternative is an ad-supported business model, except one with untargeted

ads, depicted in Figure 2. Websites still receive money from frms to serve ads to 

consumers, albeit untargeted ones.3 However, websites do not provide analytics services 

to frms anymore because they no longer receive personal data from consumers.4 Even 

without targeting, ad revenues still fnance the website’s digital goods and services, 

3Of course, there may still be some macro-level targeting, in that frms can choose which websites to 

advertise on (e.g. car dealers on auto review websites), but defnitely not the micro-level individualized 

targeting that requires the collection of consumers’ personal data.

4While I highlight the situation as one where no consumer data are collected for targeted advertising 

purposes, in reality, websites may still collect data internally to improve its own o ̇erings or diagnose 

technical problems. This exception also applies to the business model discussed later in Figure 3. 

3 
where consumers (like before) “pay for” content with their time and attention. But 

since consumers are no longer also “paying for” the digital goods and services with 

their personal data, the volume of untargeted ads and the amount of ad revenue from 

frms will most likely di ̇er from the volume and amount under the business model with 

targeted ads.5

Figure 3: Payment-supported Business Model without Ads 

Consumers Websites

Firms

$

$

(Formerly) 

Advertised

Goods/Services

Digital Goods/Services

A second alternative is a payment-supported business model without any ads, depicted in Figure 3. Here, websites eschew ads and the associated revenue from frms, and 

instead charge consumers directly for providing them with digital goods and services. 

They no longer receive personal data from consumers for the purposes of targeting ads 

and providing analytics to frms in exchange for revenue.6

When considering a policy limiting the transfer of consumers’ personal data (in 

e ̇ect shutting down targeted ads), it is important to stipulate which alternative is 

being considered as the basis for comparison. Moreover, the business models presented 

are not mutually exclusive; indeed, “freemium” websites such as Pandora and Hulu 

5In a sense, this alternative represents an early version of the internet, when expensive bandwidth 

limited the collection of big data for tracking purposes, and where random banner ads were pervasive. 

This is also the business model of most print media.

6Nonetheless, the exception mentioned in footnote 3 applies. 

4 
o ̇er examples of cases that combine ad-supported free tiers (Figure 2) with costly but 

ad-less premium tiers (Figure 3). 

In the sections to follow, I summarize various frameworks developed by economists 

to think about targeted advertising,7 and explain the benefts and costs to consumers 

that would arise when moving from an ad-supported business model with targeted ads, 

to one of the two alternatives presented above. 

2 Search Costs and Match Quality

Advertising creates value through facilitating welfare-enhancing matches between consumers and frms. Consumers with imperfect information search for frms making products that meet their needs. Firms in turn search for consumers interested in their products. (“Search” here is a term used by economists to describe a costly process through 

which a match is sought, and does not refer to search in the sense of “search engines.”) 

Consumers viewing ads placed by a frm will gain information about the product (e.g. 

its price or quality), and the ones interested in the product will “match” with the frm 

and buy the product (Ackerberg, 2003; Ippolito and Mathios, 1990). 

Targeted ads are directed at a specifc subset of consumers who are believed to be 

particularly interested in the frm’s product. Consumers receiving targeted ads will on 

average fnd them more “relevant” compared to untargeted ones, and spend less time 

searching for frms and their products to match their interests. Thus, targeting benefts 

the consumer because it e ̇ectively reduces their search costs. 

These models also take product heterogeneity into consideration, in that frms 

produce products with di ̇erent characteristics. In such models, average match quality—the “distance” between product characteristics desired by the consumer and the 

actual characteristics of the matched frm’s product—improves with targeted ads (de 

7See Bagwell (2007) for a more general survey on the economics of advertising. 

5 
Corniere, 2016; Burguet and Petrikaite, 2017). Higher match quality means a greater 

increase in the consumer’s utility when the better-matched product is consumed. Empirical evidence confrms that match quality (in terms of ad e ̇ectiveness) declines when 

new privacy regulations limit the use of targeted ads (Goldfarb and Tucker, 2011b). 

Overall, search eÿciency increases when advertising is targeted compared to the alternative with untargeted ads (Figure 2) because consumers search for less time to obtain 

better matches (de Corniere, 2016). 

A secondary consequence of search cost reduction and improved match quality often 

identifed in theoretical models is greater price competition between frms in equilibrium (de Corniere, 2016; Burguet and Petrikaite, 2017).8 “This result stems from the 

fact that targeting endogenously reduces the perceived cost of additional search because consumers know that with targeting they draw frms from a better pool. The 

intensifcation of price competition thus lowers frms’ mark-up. . . ” (de Corniere, 2016 

p.157). 

3 Marketing Costs and Ad Volume

Precise targeting can lead to fewer ads being served in equilibrium; this could lower 

marketing costs for frms by reducing wastage of ads served to disinterested consumers 

vis-à-vis the untargeted alternative (Esteban, Gil and Hernandez, 2001; Iyer, Soberman 

and Villas-Boas, 2005).9 Some of these cost savings could translate into lower prices for 

consumers, especially under competitive market conditions (Evans, 2009). Moreover, if 

consumers experience disutility from viewing ads, especially irrelevant untargeted ones, 

then having to view fewer of them is a beneft. 

8Brahim, Lahmandi-Ayed and Laussel (2011) derive similar e ̇ects using a horizontal di ̇erentiation 

model. 

9In contrast, Johnson (2013) presents a model without frm-side competition in which the volume 

of ads increases in equilibrium with targeting. 

6 
On the other hand, websites (or rather their intermediaries, the ad networks) may 

charge frms more for serving targeted ads and providing analytics, relative to untargeted ads. This would lead to higher advertising costs for frms, which may then be 

passed through to consumers in the form of higher prices (de Corniere and de Nijs, 

2016; de Corniere, 2016). 

4 Price Discrimination in Uncompetitive Settings

Targeted advertising may also lead to targeted pricing.10 Such price discrimination 

occurs in models where a single frm with market power (e.g. barriers to entry) separates 

a market into segments and charges consumers in each segment di ̇erent prices.11 Price 

discrimination in the context of targeted online advertising is relevant only when the 

price o ̇ered to consumers seeing targeted ads is di ̇erent from the price o ̇ered to 

other consumers. Examples of this include providing coupon codes or tracking users’ 

click-through from a “special o ̇er” ad to the merchant’s web store. 

In price discrimination models with more and less price-sensitive market segments, 

the frm maximizes profts by o ̇ering the segment containing more (less) price-elastic 

(i.e. price-sensitive) consumers a low (high) price. In the context of an online advertising campaign, the frm would ask websites to use personal data to identify these 

price-elastic consumers and serve them targeted ads o ̇ering a discount. Alternatively, 

they could identify price-inelastic consumers and serve them targeted ads o ̇ering an 

infated price.12

10All the models cited so far assume an undi ̇erentiated price among consumers, except Iyer, Soberman and Villas-Boas (2005), who incorporate targeted pricing in an extension to their model.

11More specifcally, this practice of segmenting the market is referred to as third degree price 

discrimination. Shiller (2014) considers frst degree price discrimination with targeted ads. Acquisti 

and Varian (2005) consider intertemporal price discrimination in online settings where frms track 

consumers’ purchase history. In economics, price discrimination occurs when frms charge di ̇erent 

consumers di ̇erent prices despite there being no di ̇erence in the cost of producing the (identical) 

good or service. 

12Whether the price di ̇erence is framed as “discounted” or “infated” to which segment of consumers 

7 
Targeting technology enables websites to segment the market and charge consumers 

di ̇erent prices; losing access to targeting (e.g. because of arbitrage or limits on its 

ability to collect information about consumers) would force the website to revert to a 

one-size-fts-all untargeted ad campaign (Figure 2), the basis for comparison. In this 

case, the price charged will be between the high and low prices under price discrimination. Thus, with price discrimination and targeted ads, price-inelastic consumers 

are worse o ̇ because they pay a relatively high price, while price-elastic consumers are 

better o ̇ because they pay a relatively low price (Aguirre, Cowan and Vickers, 2010). 

In an audit study, Hannak, Soeller, Lazer, Mislove and Wilson (2014) experimentally 

vary user features in simulated user profles and empirically observe price di ̇erences 

based on data gathered by websites. 

In the aggregate though, welfare analysis under price discrimination becomes more 

complex because total consumer surplus depends not only on the price individual consumers pay, but also on the quantity of consumers in each segment paying those prices. 

For instance, if the low price attracts a deluge of price-elastic consumers, then the aggregate gain in consumer surplus from that segment may surpass the aggregate loss in 

consumer surplus from the price-inelastic segment being charged the higher price. (The 

frm is necessarily better o ̇ because if it did not proft from segmenting the market, 

it would not have done so to begin with.) While total welfare depends on whether 

quantity demanded increases / stays constant / decreases, a necessary condition for an 

increase in total welfare is that aggregate quantity demanded increases (Schmalensee, 

1981; Varian, 1985). More generally, economic models show that when moving from 

a situation without price discrimination (untargeted ads) to one with (targeted ads), 

changes in total welfare overall, and consumer surplus in particular, depend on the 

shape (in particular, the relative concavity) of demand functions in the two market 

is irrelevant. In equilibrium, the pair of high and low prices would be the same. 

8 
segments (Aguirre, Cowan and Vickers, 2010; Cowan, 2012). 

5 Market Segmentation in Competitive Setting

Price discrimination models assume a single frm with market power splitting the market 

into segments. However, another form of market segmentation may occur in competitive settings with multiple frms selling di ̇erentiated products. Under the untargeted 

ads regime (Figure 2), frms serve ads randomly. When frms move to a regime with targeted ads (Figure 1), they may now fnd it more e ̇ective to concentrate advertising onto 

a small segment of the product space, generating an equilibrium which endogenously 

di ̇erentiates and divides the market. As frms become “mini-monopolists” in their respective targeted market segments, price competition declines, and consumers may end 

up paying higher prices relative to an unsegmented market (Roy, 2000; Iyer, Soberman 

and Villas-Boas, 2005; Galeotti and Moraga-Gonzalez, 2008; Brahim, Lahmandi-Ayed 

and Laussel, 2011). 

6 Consumer Concerns about Data Use

Limiting the collection of personal data and its use for targeted advertising may be 

benefcial to consumers for privacy reasons. Concerns relating to privacy13 can enter a 

consumer’s utility function in several ways. First, consumers often underestimate the 

degree and consequence of the personal data collection websites carry out in exchange 

for providing free digital goods and services (McDonald and Cranor, 2010; Acquisti, 

Taylor and Wagman, 2016). Consumers may experience disutility from the uncertainty 

regarding how the data will be used—possibly in a manner that could result in higher 

13Acquisti, Taylor and Wagman (2016) defne privacy, in the informational economics sense, as 

concerns relating to “trade-o ̇s arising from protecting [as opposed to] sharing of personal data” (p. 

433). 

9 
prices or fewer choices when ads are targeted.14 Moreover, there is the potential adverse 

outcome regarding data security; should a website be breached, “consumers could incur the costs of having private information disclosed and potentially misused” (Evans, 

2009). Limiting the collection of personal data mitigates these risks because there would 

no longer be stored personal data for websites to exploit or hackers to steal. 

Second, consumer harm can “come directly from disutility that a customer might 

feel from the perceived intrusiveness of the advertising” (Tucker, 2012 p. 327). Indeed, 

a feld experiment by Goldfarb and Tucker (2011a) fnds that consumers are less responsive to obtrusive ads when they are targeted based on the content of the website 

on which they appear, possibly because they deem targeted ads manipulative. Others 

postulate that consumers respond to overly intrusive ads by resisting their appeal in a 

process known in social psychology as “reactance” (Tucker, 2012). 

That consumers care about privacy is refected in numerous studies that attempt to 

empirically quantify their value of privacy.15 Importantly, valuations depend on the way 

choices are framed, revealing a gap between a willingness to pay for privacy protection 

(starting from a no privacy standpoint) versus a willingness to accept a loss of privacy 

(starting from a fully protected standpoint) (Acquisti, John and Loewenstein, 2013). 

While most studies report privacy valuations to be low, they generally confrm that 

consumers do place a positive—though highly variable and diÿcult to establish—value 

on protecting their privacy (Acquisti, Taylor and Wagman, 2016). 

14One approach to curing the information asymmetry is to require websites to make disclosures that 

fully inform consumers. Yet some research suggest that privacy disclosures may have a limited impact 

on consumers’ willingness to allow access to their data (Berendt, Gunther and Spiekermann, 2005).

15While this literature relies heavily on surveying stated preferences, there is research highlighting 

a disconnect between consumers’ stated and revealed preferences when it comes to privacy (Berendt, 

Gunther and Spiekermann, 2005). 

10 
7 Conclusion

In considering policies limiting personal data collection practices, it is important to 

remember that consumers highly value the free online goods and services provided 

by websites. In one study using large-scale discrete choice experiments, Brynjolfsson, 

Eggers and Gannamaneni (2018) estimate that the median consumer in 2017 values 

search engine services at $17,530 annually and email at $8,414 annually. While a shift 

to the payments-supported alternative (Figure 3) would not represent a total loss of 

this value, it would nonetheless reduce the consumer surplus somewhat, as websites 

start charging for their digital goods and services. (See the Appendix for an economic 

explanation of what these valuations represent.) 

The advent of the targeted ad-supported business model essentially allows individual consumers to monetize their personal data in exchange for valuable digital goods 

and services, fnanced by revenues from targeted ads (Figure 1). If policies restrict 

websites from collecting consumer personal data, then consumers lose the opportunity 

to monetize their personal data, representing an income e ̇ect, which consumers pay 

for either in dollars (Figure 3) or in time and attention spent watching a (potentially) 

greater number of untargeted ads (Figure 2).16 Moreover, the loss of this monetization opportunity could disproportionately a ̇ect more wealth-constrained users, who 

may end up losing access to these free services. Policy makers must carefully consider 

the ramifcations of altering the current targeted ad-supported regime, which generates 

signifcant consumer and economic value. 

Personal data collection and targeted advertising can be benefcial or detrimental to 

consumers depending on many factors. Targeted ads reduce search costs and improve 

16This line of thinking is related to the allocation of property rights over personal data, frst postulated by Posner (1981). Hermalin and Katz (2006) provide a summary of these arguments, but also 

argue that complete assignment of property rights may be insuÿcient for protecting consumer privacy. 

11 
match quality, which in turn may increase price competition; this increases the total 

value consumers derive from acquiring the products they match with. Targeting could 

mean fewer ads overall; consumers beneft directly from not having to view ads, but 

also indirectly from cost-savings passed on by frms. On the other hand, price discrimination by a single frm or market segmentation by previously-competing frms may lead 

to higher prices for some or all consumers involved, though the overall welfare e ̇ect 

depends on the shape of demand functions. There are also consumer privacy concerns 

that need to be addressed. Policy decisions in this arena must account for all these 

various aspects of economic analysis. 

12 
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13 
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15 
Appendix

Brynjolfsson, Eggers and Gannamaneni (2018) estimate the median payments consumers would be willing to accept to forgo various online services. When the services are 

o ̇ered free of charge, the valuation amounts represent the full consumer surplus of the 

median consumer. However, consumers would not lose the entire amount of consumer 

surplus even in the extreme event where all websites shift to a payments-supported 

business model. Price competition among websites and entry of new competitors would 

keep payments well below these willingness-to-accept amounts. 

Figure 4 illustrates how these median valuations relate to overall consumer surplus 

in a simplifed market setting. The downward sloping line represents market demand 

for a specifc online service. The median valuation is a point on this demand curve 

representing the median consumer’s willingness to pay. When the website moves from 

charging a price of zero to a price of p, the shaded amount of consumer surplus is lost 

in aggregate. The higher the price p charged, the greater the loss in consumer surplus. 

Figure 4: The Market for an Online Service 

Quantity

Annual Price

p

0

Median Valuation

16