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Online advertising
Online advertising
from Wikipedia

Online advertising, also known as online marketing, Internet advertising, digital advertising or web advertising, is a form of marketing and advertising that uses the Internet to promote products and services to audiences and platform users.[1] Online advertising includes email marketing, search engine marketing (SEM), social media marketing, many types of display advertising (including web banner advertising), and mobile advertising. Advertisements are increasingly being delivered via automated software systems operating across multiple websites, media services and platforms, known as programmatic advertising.[2]

Like other advertising media, online advertising frequently involves a publisher, who integrates advertisements into its online content, and an advertiser, who provides the advertisements to be displayed on the publisher's content. Other potential participants include advertising agencies that help generate and place the ad copy, an ad server which technologically delivers the ad and tracks statistics, and advertising affiliates who do independent promotional work for the advertiser.

In 2016, Internet advertising revenues in the United States surpassed those of cable television and broadcast television.[3]: 14  In 2017, Internet advertising revenues in the United States totaled $83.0 billion, a 14% increase over the $72.50 billion in revenues in 2016.[4] And research estimates for 2019's online advertising spend put it at $125.2 billion in the United States, some $54.8 billion higher than the spend on television ($70.4 billion).[5]

Many common online advertising practices are controversial and, as a result, have become increasingly subject to regulation. Many internet users also find online advertising disruptive[6] and have increasingly turned to ad blocking for a variety of reasons. Online ad revenues also may not adequately replace other publishers' revenue streams. Declining ad revenue has led some publishers to place their content behind paywalls.[7]

History

[edit]
Advertising revenue as a percent of US GDP shows a rise in digital advertising since 1995 at the expense of print media.[8]

In the early days of the Internet, online advertising was mostly prohibited. For example, two of the predecessor networks to the Internet, ARPANET and NSFNet, had "acceptable use policies" that banned network "use for commercial activities by for-profit institutions".[9][10] The NSFNet began phasing out its commercial use ban in 1991.[11][12][13][14]

Email

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The first widely publicized example of online advertising was conducted via electronic mail. On 3 May 1978, a marketer from DEC (Digital Equipment Corporation), Gary Thuerk, sent an email to most of the ARPANET's American West Coast users, advertising an open house for a new model of a DEC computer.[10][15] Despite the prevailing acceptable use policies, electronic mail marketing rapidly expanded[16] and eventually became known as "spam."

The first known large-scale non-commercial spam message was sent on 18 January 1994 by an Andrews University system administrator, by cross-posting a religious message to all USENET newsgroups.[17] In January 1994 Mark Eberra started the first email marketing company for opt-in email lists under the domain Insideconnect.com. He also started the Direct Email Marketing Association to help stop unwanted email and prevent spam. [18] [19]

Four months later, Laurence Canter and Martha Siegel, partners in a law firm, broadly promoted their legal services in a USENET posting titled "Green Card Lottery – Final One?"[20] Canter and Siegel's Green Card USENET spam raised the profile of online advertising, stimulating widespread interest in advertising via both Usenet and traditional email.[17] More recently, spam has evolved into a more industrial operation, where spammers use armies of virus-infected computers (botnets) to send spam remotely.[15]

Display ads

[edit]

Online banner advertising began in the early 1990s as page owners sought additional revenue streams to support their content. Commercial online service Prodigy displayed banners at the bottom of the screen to promote Sears products. The first clickable web ad was sold by Global Network Navigator in 1993 to a Silicon Valley law firm.[21] In 1994, web banner advertising became mainstream when HotWired, the online component of Wired Magazine, and Time Warner's Pathfinder[22] sold banner ads to AT&T and other companies. The first AT&T ad on HotWired had a 44% click-through rate, and instead of directing clickers to AT&T's website, the ad linked to an online tour of seven of the world's most acclaimed art museums.[23][24]

Search ads

[edit]

GoTo.com (renamed Overture in 2001, and acquired by Yahoo! in 2003) created the first search advertising keyword auction in 1998.[25]: 119  Google launched its "AdWords" (now renamed Google Ads) search advertising program in 2000[26] and introduced quality-based ranking allocation in 2002,[27] which sorts search advertisements by a combination of bid price and searchers' likeliness to click on the ads.[25]: 123 

Since 2010

[edit]

More recently, companies have sought to merge their advertising messages into editorial content or valuable services. Examples include Red Bull's Red Bull Media House streaming Felix Baumgartner's jump from space online, Coca-Cola's online magazines, and Nike's free applications for performance tracking.[24] Advertisers are also embracing social media[28][29] and mobile advertising; mobile ad spending has grown 90% each year from 2010 to 2013.[30]: 13 

According to Ad Age Datacenter analysis, in 2017 over half of agency revenue came from digital work.[31]

The March 2021 eBay advertisement for the first Asian Giant Hornet (Vespa mandarinia) nest in the US was controversial.[32] The owner of the first nest discovered in the United States – in Blaine, Washington – demanded its return instead of allowing scientific investigation, and proceeded to sell it.[32] A nearby beekeeper bought it to gift it back to the state entomology team which had exterminated it, for study.[32]

Types of online advertising

[edit]

Display advertising

[edit]
An example of display advertising featuring geotargeting

Display advertising conveys its advertising message visually using text, logos, animations, videos, photographs, or other graphics. Display advertising is ubiquitous across online systems including websites, search engines, social media platforms, mobile applications and email. Google and Facebook dominate online display advertising, which has become a highly concentrated market, with estimates that they were responsible for 70% of overall US digital advertising revenue in 2016.[2] The goal of display advertising is to obtain more traffic, clicks, or popularity for the advertising brand or organization. Display advertisers frequently target users with particular traits to increase the ads' effect.[33]

Web banner advertising

[edit]

Web banners or banner ads typically are graphical ads displayed within a web page. Many banner ads are delivered by a central ad server.

Banner ads can use rich media to incorporate video, audio, animations, buttons, forms, or other interactive elements using Java applets, HTML5, Adobe Flash, and other programs. Frame ads were the first form of web banners.[23] The colloquial usage of "banner ads" often refers to traditional frame ads. Website publishers incorporate frame ads by setting aside a particular space on the web page. The Interactive Advertising Bureau's Ad Unit Guidelines propose standardized pixel dimensions for ad units.[34]

Pop-ups/pop-unders: A pop-up ad is displayed in a new web browser window that opens above a website visitor's initial browser window.[35] A pop-under ad opens a new browser window under a website visitor's initial browser window.[30]: 22  Pop-under ads and similar technologies are now advised against by online authorities such as Google, who state that they "do not condone this practice".[36]

Floating ad: A floating ad, or overlay ad, is a type of rich media advertisement that appears superimposed over the requested website's content. Floating ads may disappear or become less obtrusive after a pre-set time period.

Expanding ad: An expanding ad is a rich media frame ad that changes dimensions upon a predefined condition, such as a preset amount of time a visitor spends on a webpage, the user's click on the ad, or the user's mouse movement over the ad.[37] Expanding ads enable the inclusion of more content within a limited initial ad space.

Trick banners: A trick banner is a banner ad where the ad copy imitates some screen elements users commonly encounter, such as an operating system message or popular application message, to induce ad clicks.[38] Trick banners typically do not mention the advertiser in the initial ad, and thus they are a form of bait-and-switch.[39][40] Trick banners commonly attract a higher-than-average click-through rate, but tricked users may resent the advertiser for deceiving them.[41]

News Feed Ads

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"News Feed Ads", also called "Sponsored Stories", "Boosted Posts", typically exist on social media platforms that offer a steady stream of information updates ("news feed"[42]) in regulated formats (i.e. in similar sized small boxes with a uniform style). Those advertisements are intertwined with non-promoted news that the users are reading through. Those advertisements can be of any content, such as promoting a website, a fan page, an app, or a product.

Some examples are: Facebook's "Sponsored Stories",[43] LinkedIn's "Sponsored Updates",[44] and Twitter's "Promoted Tweets".[45]

This display ads format falls into its own category because unlike banner ads which are quite distinguishable, News Feed Ads' format blends well into non-paid news updates. This format of online advertisement yields much higher click-through rates than traditional display ads.[46][47]

Advertising sales and delivery models

[edit]
A visualization of the real-time bidding market in programmatic advertising online
A visualization of the real-time bidding system in online display advertising.[48]

The process by which online advertising is displayed can involve many parties. In the simplest case, the website publisher selects and serves the ads. Publishers which operate their own advertising departments may use this method. Alternatively ads may be outsourced to an advertising agency under contract with the publisher, and served from the advertising agency's servers or ad space may be offered for sale in a bidding market using an ad exchange and real-time bidding, known as programmatic advertising.

Programmatic advertising

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Programmatic advertising involves automating the sale and delivery of digital advertising on websites and platforms via software rather than direct human decision-making.[2] Advertisements are selected and targeted to audiences via ad servers which often use cookies, which are unique identifiers of specific computers, to decide which ads to serve to a particular consumer. Cookies can track whether a user left a page without buying anything, so the advertiser can later retarget the user with ads from the site the user visited.[49]

As advertisers collect data across multiple external websites about a user's online activity, they can create a detailed profile of the user's interests to deliver even more targeted advertising. This aggregation of data is called behavioral targeting.[50] Advertisers can also target their audience by using contextual to deliver display ads related to the content of the web page where the ads appear.[25]: 118  Retargeting, behavioral targeting, and contextual advertising all are designed to increase an advertiser's return on investment, or ROI, over untargeted ads.[51]

Online advertising serving process using online bidding

Advertisers may also deliver ads based on a user's suspected geography through geotargeting. A user's IP address communicates some geographic information (at minimum, the user's country or general region). The geographic information from an IP can be supplemented and refined with other proxies or information to narrow the range of possible locations.[33] For example, with mobile devices, advertisers can sometimes use a phone's GPS receiver or the location of nearby mobile towers.[52] Cookies and other persistent data on a user's machine may help narrow down a user's location even further.

This involves many parties interacting automatically in real time. In response to a request from the user's browser, the publisher content server sends the web page content to the user's browser over the Internet. The page does not yet contain ads, but contains links which cause the user's browser to connect to the publisher ad server to request that the spaces left for ads be filled in with ads. Information identifying the user, such as cookies and the page being viewed, is transmitted to the publisher ad server.

The publisher ad server then communicates with a supply-side platform server. The publisher is offering ad space for sale, so they are considered the supplier. The supply side platform also receives the user's identifying information, which it sends to a data management platform. At the data management platform, the user's identifying information is used to look up demographic information, previous purchases, and other information of interest to advertisers. The process is sometimes described as a 'waterfall'.[53]

Broadly speaking, there are three types of data obtained through such a data management platform:

First party data refers to the data retrieved from customer relationship management (CRM) platforms, in addition to website and paid media content or cross-platform data. This can include data from customer behaviors, actions or interests.[54]
Second party data refers to an amalgamation of statistics related to cookie pools on external publications and platforms. The data is provided directly from the source (adservers, hosted solutions for social or an analytics platform). It is also possible to negotiate a deal with a particular publisher to secure specific data points or audiences.
Third party data is sourced from external providers and often aggregated from numerous websites. Businesses sell third-party data and are able to share this via an array of distribution avenues.[55]

This customer information is combined and returned to the supply side platform, which can now package up the offer of ad space along with information about the user who will view it. The supply side platform sends that offer to an ad exchange.

The ad exchange puts the offer out for bid to demand-side platforms. Demand side platforms act on behalf of ad agencies, who sell ads which advertise brands. Demand side platforms thus have ads ready to display, and are searching for users to view them. Bidders get the information about the user ready to view the ad, and decide, based on that information, how much to offer to buy the ad space. According to the Internet Advertising Bureau, a demand side platform has 10 milliseconds to respond to an offer. The ad exchange picks the winning bid and informs both parties.

The ad exchange then passes the link to the ad back through the supply side platform and the publisher's ad server to the user's browser, which then requests the ad content from the agency's ad server. The ad agency can thus confirm that the ad was delivered to the browser.[56]

This is simplified, according to the IAB. Exchanges may try to unload unsold ("remnant") space at low prices through other exchanges. Some agencies maintain semi-permanent pre-cached bids with ad exchanges, and those may be examined before going out to additional demand side platforms for bids. The process for mobile advertising is different and may involve mobile carriers and handset software manufacturers.[56]

Interstitial ads: An interstitial ad displays before a user can access requested content, sometimes while the user is waiting for the content to load.[57] Interstitial ads are a form of interruption marketing.[58][59]

Text ads: A text ad displays text-based hyperlinks. Text-based ads may display separately from a web page's primary content, or they can be embedded by hyperlinking individual words or phrases to the advertiser's websites. Text ads may also be delivered through email marketing or text message marketing. Text-based ads often render faster than graphical ads and can be harder for ad-blocking software to block.[60]

Search engine marketing (SEM)

[edit]

Search engine marketing, or SEM, is designed to increase a website's visibility in search engine results pages (SERPs). Search engines provide sponsored results and organic (non-sponsored) results based on a web searcher's query.[25]: 117  Search engines often employ visual cues to differentiate sponsored results from organic results. Search engine marketing includes all of an advertiser's actions to make a website's listing more prominent for topical keywords. The primary reason behind the rising popularity of Search Engine Marketing has been Google. There were a few companies that had its own PPC and Analytics tools. However, this concept was popularized by Google. Google Ad words was convenient for advertisers to use and create campaigns. And, they realized that the tool did a fair job, by charging only for someone's click on the ad, which reported as the cost-per-click for which a penny was charged. This resulted in the advertisers monitoring the campaign by the number of clicks and were satisfied that the ads could be tracked.[61]

Search engine optimization, or SEO, attempts to improve a website's organic search rankings in SERPs by increasing the website content's relevance to search terms. Search engines regularly update their algorithms to penalize poor quality sites that try to game their rankings, making optimization a moving target for advertisers.[62][63] Many vendors offer SEO services.[30]: 22 

Sponsored search (also called sponsored links, search ads, or paid search) allows advertisers to be included in the sponsored results of a search for selected keywords. Search ads are often sold via real-time auctions, where advertisers bid on keywords.[25]: 118 [64] In addition to setting a maximum price per keyword, bids may include time, language, geographical, and other constraints.[25]: 118  Search engines originally sold listings in order of highest bids.[25]: 119  Modern search engines rank sponsored listings based on a combination of bid price, expected click-through rate, keyword relevancy and site quality.[27]

Social media marketing

[edit]

Social media marketing is commercial promotion conducted through social media websites. Many companies promote their products by posting frequent updates and providing special offers through their social media profiles. Videos, interactive quizzes, and sponsored posts are all a part of this operation. Usually these ads are found on Facebook, Instagram, Twitter, and Snapchat.[65]

Mobile advertising

[edit]

Mobile advertising is ad copy delivered through wireless mobile devices such as smartphones, feature phones, or tablet computers. Mobile advertising may take the form of static or rich media display ads, SMS (Short Message Service) or MMS (Multimedia Messaging Service) ads, mobile search ads, advertising within mobile websites, or ads within mobile applications or games (such as interstitial ads, "advergaming", or application sponsorship).[30]: 23  Industry groups such as the Mobile Marketing Association have attempted to standardize mobile ad unit specifications, similar to the IAB's efforts for general online advertising.[59]

Mobile advertising is growing rapidly for several reasons. There are more mobile devices in the field, connectivity speeds have improved (which, among other things, allows for richer media ads to be served quickly), screen resolutions have advanced, mobile publishers are becoming more sophisticated about incorporating ads, and consumers are using mobile devices more extensively.[30]: 14  The Interactive Advertising Bureau predicts continued growth in mobile advertising with the adoption of location-based targeting and other technological features not available or relevant on personal computers.[30]: 14  In July 2014 Facebook reported advertising revenue for the June 2014 quarter of $2.68 billion, an increase of 67 percent over the second quarter of 2013. Of that, mobile advertising revenue accounted for around 62 percent, an increase of 41 percent on the previous year.

Email advertising

[edit]

Email advertising is ad copy comprising an entire email or a portion of an email message.[30]: 22  Email marketing may be unsolicited, in which case the sender may give the recipient an option to opt out of future emails, or it may be sent with the recipient's prior consent (opt-in). Businesses may ask for your email and send updates on new products or sales.

Chat advertising

[edit]

As opposed to static messaging, chat advertising refers to real-time messages dropped to users on certain sites. This is done using live chat software or tracking applications installed within certain websites with the operating personnel behind the site often dropping adverts on the traffic surfing around the sites. In reality, this is a subset of the email advertising but different because of its time window.

Online classified advertising

[edit]

Online classified advertising is advertising posted online in a categorical listing of specific products or services. Examples include online job boards, online real estate listings, automotive listings, online yellow pages, and online auction-based listings.[30]: 22  Craigslist and eBay are two prominent providers of online classified listings.

Adware

[edit]

Adware is software that, once installed, automatically displays advertisements on a user's computer. The ads may appear in the software itself, integrated into web pages visited by the user, or in pop-ups/pop-unders.[66] Adware installed without the user's permission is a type of malware.[67]

Affiliate marketing

[edit]

Affiliate marketing occurs when advertisers organize third parties to generate potential customers for them. Third-party affiliates receive payment based on sales generated through their promotion.[30]: 22  Affiliate marketers generate traffic to offers from affiliate networks, and when the desired action is taken by the visitor, the affiliate earns a commission. These desired actions can be an email submission, a phone call, filling out an online form, or an online order being completed.

Content marketing

[edit]

Content marketing is any marketing that involves the creation and sharing of media and publishing content in order to acquire and retain customers. This information can be presented in a variety of formats, including blogs, news, video, white papers, e-books, infographics, case studies, how-to guides and more.

Considering that most marketing involves some form of published media, it is almost (though not entirely) redundant to call 'content marketing' anything other than simply 'marketing'. There are, of course, other forms of marketing (in-person marketing, telephone-based marketing, word of mouth marketing, etc.) where the label is more useful for identifying the type of marketing. However, even these are usually merely presenting content that they are marketing as information in a way that is different from traditional print, radio, TV, film, email, or web media.

Online marketing platform

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An online marketing platform (OMP) is an integrated web-based platform that combines the benefits of a business directory, local search engine, search engine optimisation (SEO) tool, customer relationship management (CRM) package and content management system (CMS). eBay and Amazon are used as online marketing and logistics management platforms. On social media, retail online marketing is often used. Online business marketing platforms such as Marketo, MarketBright and Pardot have been bought by major IT companies (Eloqua-Oracle, Neolane-Adobe and Unica-IBM).

Unlike television marketing in which Nielsen TV Ratings can be relied upon for viewing metrics, online advertisers do not have an independent party to verify viewing claims made by the big online platforms.[68]

The European Union defines online platforms as "information society services that allow business users to offer goods or services to consumers, with a view to facilitating the initiating of direct transactions between those business users and consumers; they are provided to business users on the basis of contractual relationships between the provider of those services and business users offering goods or services to consumers."[69] Almost half of the small and medium-sized businesses who responded to an EU survey in 2018 said that they use online marketplaces to sell their goods and services.[70]

Compensation methods

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Advertisers and publishers use a wide range of payment calculation methods. In 2012, advertisers calculated 32% of online advertising transactions on a cost-per-impression basis, 66% on customer performance (e.g. cost per click or cost per acquisition), and 2% on hybrids of impression and performance methods.[30]: 17 

CPM (cost per mille)

[edit]

Cost per mille, often abbreviated to CPM, means that advertisers pay for every thousand displays of their message to potential customers (mille is the Latin word for thousand). In the online context, ad displays are usually called "impressions." Definitions of an "impression" vary among publishers,[71] and some impressions may not be charged because they don't represent a new exposure to an actual customer. Advertisers can use technologies such as web bugs to verify if an impression is actually delivered.[72][73]: 59  Similarly, revenue generated can be measured in Revenue per mille (RPM).[74]

Publishers use a variety of techniques to increase page views, such as dividing content across multiple pages, repurposing someone else's content, using sensational titles, or publishing tabloid or sexual content.[75]

CPM advertising is susceptible to "impression fraud," and advertisers who want visitors to their sites may not find per-impression payments a good proxy for the results they desire.[76]: 1–4 

CPC (cost per click)

[edit]

CPC (Cost Per Click) or PPC (Pay per click) means advertisers pay each time a user clicks on the ad. CPC advertising works well when advertisers want visitors to their sites, but it's a less accurate measurement for advertisers looking to build brand awareness.[77] CPC's market share has grown each year since its introduction, eclipsing CPM to dominate two-thirds of all online advertising compensation methods.[30]: 18 [76]: 1 

Like impressions, not all recorded clicks are valuable to advertisers. GoldSpot Media reported that up to 50% of clicks on static mobile banner ads are accidental and resulted in redirected visitors leaving the new site immediately.[78]

CPE (cost per engagement)

[edit]

Cost per engagement aims to track not just that an ad unit loaded on the page (i.e., an impression was served), but also that the viewer actually saw and/or interacted with the ad.[79][80]

CPV (cost per view)

[edit]

Cost per view video advertising. Both Google and TubeMogul endorsed this standardized CPV metric to the IAB's (Interactive Advertising Bureau) Digital Video Committee, and it's garnering a notable amount of industry support.[81] CPV is the primary benchmark used in YouTube Advertising Campaigns, as part of Google's AdWords platform.

CPI (cost per install)

[edit]

The CPI compensation method is specific to mobile applications and mobile advertising. In CPI ad campaigns brands are charged a fixed of bid rate only when the application was installed.

CPL (cost per lead)

[edit]

Cost per lead compensation method implies that the advertiser pays for an explicit sign-up from a consumer interested in the advertiser's offer.

Attribution of ad value

[edit]

In marketing, "attribution" is the measurement of effectiveness of particular ads in a consumer's ultimate decision to purchase. Multiple ad impressions may lead to a consumer "click" or other action. A single action may lead to revenue being paid to multiple ad space sellers.[82]

Other performance-based compensation

[edit]

CPA (Cost Per Action or Cost Per Acquisition) or PPP (Pay Per Performance) advertising means the advertiser pays for the number of users who perform a desired activity, such as completing a purchase or filling out a registration form. Performance-based compensation can also incorporate revenue sharing, where publishers earn a percentage of the advertiser's profits made as a result of the ad. Performance-based compensation shifts the risk of failed advertising onto publishers.[76]: 4, 16 

Fixed cost

[edit]

Fixed cost compensation means advertisers pay a fixed cost for delivery of ads online, usually over a specified time period, irrespective of the ad's visibility or users' response to it. One examples is CPD (cost per day) where advertisers pay a fixed cost for publishing an ad for a day irrespective of impressions served or clicks.

Benefits of online advertising

[edit]

The low costs of electronic communication reduce the cost of displaying online advertisements compared to offline ads. Online advertising, and in particular social media, provides a low-cost means for advertisers to engage with large established communities.[65] Advertising online offers better returns than in other media.[76]: 1 

Online advertisers can collect data on their ads' effectiveness, such as the size of the potential audience or actual audience response,[25]: 119  how a visitor reached their advertisement, whether the advertisement resulted in a sale, and whether an ad actually loaded within a visitor's view.[72][73]: 59  This helps online advertisers improve their ad campaigns over time.

Advertisers have a wide variety of ways of presenting their promotional messages, including the ability to convey images, video, audio, and links. Unlike many offline ads, online ads also can be interactive.[24] For example, some ads let users input queries[83] or let users follow the advertiser on social media.[84] Online ads can even incorporate games.[85]

Publishers can offer advertisers the ability to reach customizable and narrow market segments for targeted advertising. Online advertising may use geo-targeting to display relevant advertisements to the user's geography. Advertisers can customize each individual ad to a particular user based on the user's previous preferences.[51] Advertisers can also track whether a visitor has already seen a particular ad in order to reduce unwanted repetitious exposures and provide adequate time gaps between exposures.[86]

Online advertising can reach nearly every global market, and online advertising influences offline sales.[87][88][89]

Once ad design is complete, online ads can be deployed very quickly. The delivery of online ads does not need to be linked to the publisher's publication schedule. Furthermore, online advertisers can modify or replace ad copy more rapidly than their offline counterparts.[90]

Concerns with online advertising

[edit]

Insufficient security

[edit]

According to a US Senate investigation in 2014, there are security and privacy concerns for users due to the infrastructure of online advertising.[91] This is because of the potential for malware to be disseminated through online advertisements and for such malvertising to be inserted and triggered without sufficient protection or screening. Ransomware gangs were spotted using carefully targeted Google search advertising to redirect victims to pages dropping malware.[92]

Disinformation and dark money

[edit]

Research published on New Media & Society shows that several actors abuse the obscurity and complexity of programmatic advertising to spread disinformation online,[93] for example by directing advertising money to fund fake news websites.[94][95] Additionally, the lack of regulation and accountability in the digital advertising ecosystem has led to the influx of dark money campaigns that fund political campaigns without disclosing the source of the funds.[96]

Viewability limitations

[edit]

Eye-tracking studies have shown that Internet users often ignore web page zones likely to contain display ads (sometimes called "banner blindness"), and this problem is worse online than in offline media.[97] On the other hand, studies suggest that even those ads "ignored" by the users may influence the user subconsciously.[98]

Ad fraud

[edit]
An illustration of a "made-for-advertising" website from the book "Market-Oriented Disinformation Research" (p.139)
Digital advertising is used to fund fake news websites[99]

There are numerous ways that advertisers can be overcharged for their advertising. For example, click fraud occurs when a publisher or third parties click (manually or through automated means) on a CPC ad with no legitimate buying intent.[100] For example, click fraud can occur when a competitor clicks on ads to deplete its rival's advertising budget, or when publishers attempt to manufacture revenue.[100]

Click fraud is especially associated with pornography sites. In 2011, certain scamming porn websites launched dozens of hidden pages on each visitor's computer, forcing the visitor's computer to click on hundreds of paid links without the visitor's knowledge.[101]

Online impression fraud can occur when publishers overstate the number of ad impressions they have delivered to their advertisers. To combat impression fraud, several publishing and advertising industry associations are developing ways to count online impressions credibly.[102][103]

Heterogeneous clients

[edit]

Because users have different operating systems, web browsers[104] and computer hardware (including mobile devices and different screen sizes), online ads may appear to users differently from how the advertiser intended, or the ads may not display properly at all. A 2012 comScore study revealed that, on average, 31% of ads were not "in-view" when rendered, meaning they never had an opportunity to be seen.[105] Rich media ads create even greater compatibility problems, as some developers may use competing (and exclusive) software to render the ads (see e.g. Comparison of HTML 5 and Flash).

Furthermore, advertisers may encounter legal problems if legally required information does not actually display to users, even if that failure is due to technological heterogeneity.[106]: i  In the United States, the FTC has released a set of guidelines indicating that it's the advertisers' responsibility to ensure the ads display any required disclosures or disclaimers, irrespective of the users' technology.[106]: 4–8 

Ad blocking

[edit]

Ad blocking, or ad filtering, means the ads do not appear to the user because the user uses technology to screen out ads. Many browsers block unsolicited pop-up ads by default.[107] Other software programs or browser add-ons may also block the loading of ads, or block elements on a page with behaviors characteristic of ads (e.g. HTML autoplay of both audio and video). Approximately 9% of all online page views come from browsers with ad-blocking software installed,[108] and some publishers have 40%+ of their visitors using ad-blockers.[7]

Use of mobile and desktop ad blocking software designed to remove traditional advertising grew by 41% worldwide and by 48% in the U.S. between Q2 2014 and Q2 2015.[109][110] As of Q2 2015, 45 million Americans were using ad blockers.[109][111] In a survey research study released Q2 2016, Met Facts reported 72 million Americans, 12.8 million adults in the UK, and 13.2 million adults in France were using ad blockers on their PCs, smartphones, or tablet computers. In March 2016, the Internet Advertising Bureau reported that UK ad blocking was already at 22% among people over 18 years old.[112][113] As of 2021, 27% of US Internet users used ad blocking software, a trend that has been increasing since 2014.[114] Among technical audiences the rate of blocking reaches 58% as of 2021.[115][116][117]

Anti-targeting technologies

[edit]

Some web browsers offer privacy modes where users can hide information about themselves from publishers and advertisers. Among other consequences, advertisers can't use cookies to serve targeted ads to private browsers. Most major browsers have incorporated Do Not Track options into their browser headers, but the regulations currently are only enforced by the honor system.[118][119][120]

Privacy and user surveillance

[edit]

The collection of user information by publishers and advertisers has raised consumer concerns about their privacy.[33][73] Sixty percent of internet users surveyed said they would use Do Not Track technology to block all collection of information if given an opportunity.[121][122] Over half of all Google and Facebook users are concerned about their privacy when using Google and Facebook, according to Gallup.[123]

Many consumers have reservations about online behavioral targeting. By tracking users' online activities, advertisers are able to understand consumers quite well. Advertisers often use technology, such as web bugs and respawning cookies, to maximize their abilities to track consumers.[73]: 60 [124] According to a 2011 survey conducted by Harris Interactive, over half of Internet users had a negative impression of online behavioral advertising, and forty percent feared that their personally-identifiable information had been shared with advertisers without their consent.[125][126] Consumers can be especially troubled by advertisers targeting them based on sensitive information, such as financial or health status.[124] Furthermore, some advertisers attach the MAC address of users' devices to their "demographic profiles" so they can be retargeted (regardless of the accuracy of the profile) even if the user clears their cookies and browsing history.[citation needed]

Trustworthiness of advertisers

[edit]

Scammers can take advantage of consumers' difficulties verifying an online persona's identity,[127]: 1  leading to artifices like phishing (where scam emails look identical to those from a well-known brand owner)[128] and confidence schemes like the Nigerian "419" scam.[129][130][131] The Internet Crime Complaint Center received 289,874 complaints in 2012, totaling over half a billion dollars in losses, most of which originated with scam ads.[132][133]

Consumers also face malware risks, i.e. malvertising, when interacting with online advertising. Cisco's 2013 Annual Security Report revealed that clicking on ads was 182 times more likely to install a virus on a user's computer than surfing the Internet for porn.[134][135] For example, in August 2014 Yahoo's advertising network reportedly saw cases of infection of a variant of Cryptolocker ransomware.[136]

Spam

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The Internet's low cost of disseminating advertising contributes to spam, especially by large-scale spammers. Numerous efforts have been undertaken to combat spam, ranging from blacklists to regulatorily-required labeling to content filters, but most of those efforts have adverse collateral effects, such as mistaken filtering.[10]

Regulation

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In general, consumer protection laws apply equally to online and offline activities.[106]: i  However, there are questions over which jurisdiction's laws apply and which regulatory agencies have enforcement authority over trans-border activity.[137] Many laws specifically regulate the ways online ads are delivered. For example, online advertising delivered via email is more regulated than the same ad content delivered via banner ads. Among other restrictions, the U.S. CAN-SPAM Act of 2003 requires that any commercial email provide an opt-out mechanism.[137] Similarly, mobile advertising is governed by the Telephone Consumer Protection Act of 1991 (TCPA), which (among other restrictions) requires user opt-in before sending advertising via text messaging.

As with offline advertising, industry participants have undertaken numerous efforts to self-regulate and develop industry standards or codes of conduct. Several United States advertising industry organizations jointly published Self-Regulatory Principles for Online Behavioral Advertising based on standards proposed by the FTC in 2009.[138] European ad associations published a similar document in 2011.[139] Primary tenets of both documents include consumer control of data transfer to third parties, data security, and consent for collection of certain health and financial data.[138]: 2–4  Neither framework, however, penalizes violators of the codes of conduct.[140]

The Online Intermediation Services Regulation (2019/1150/EU) or P2B Regulation came into force in all EU Member States and the UK on 12 July 2020. The Regulation aims to promote fairness and transparency for business users of online intermediation services or online platforms. The main aim of the Regulation is to establish a legal framework which will guarantee transparent terms and conditions for business users of online platforms, as well as effective opportunities for redress when these terms and conditions are not respected. Such transparency and fairness underpin improvements in the function of the Digital Single Market especially for the benefit of SMEs.[141] The regulations also set up an EU Observatory to monitor the impact of the new rules,[70] called the Observatory on the Online Platform Economy.[142]

The UK's Online Intermediation Services for Business Users (Enforcement) Regulations 2020 replicate the effects of the EU Regulation.

Privacy and data collection

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Privacy regulation can require users' consent before an advertiser can track the user or communicate with the user. However, affirmative consent ("opt in") can be difficult and expensive to obtain.[73]: 60  Industry participants often prefer other regulatory schemes.

Different jurisdictions have taken different approaches to privacy issues with advertising. The United States has specific restrictions on online tracking of children in the Children's Online Privacy Protection Act (COPPA),[138]: 16–17  and the FTC has recently expanded its interpretation of COPPA to include requiring ad networks to obtain parental consent before knowingly tracking kids.[143] Otherwise, the U.S. Federal Trade Commission frequently supports industry self-regulation, although increasingly it has been undertaking enforcement actions related to online privacy and security.[144] The FTC has also been pushing for industry consensus about possible Do Not Track legislation.

In contrast, the European Union's "Privacy and Electronic Communications Directive" restricts websites' ability to use consumer data much more comprehensively. The EU limitations restrict targeting by online advertisers; researchers have estimated online advertising effectiveness decreases on average by around 65% in Europe relative to the rest of the world.[73]: 58 

See also

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References

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Revisions and contributorsEdit on WikipediaRead on Wikipedia
from Grokipedia

Online advertising consists of paid promotions delivered through internet-connected channels, including websites, search engines, mobile apps, and , utilizing formats such as display banners, search listings, video interstitials, and sponsored content to drive consumer actions like purchases or engagement. These campaigns rely on data-driven targeting, aggregating user behavioral signals from browsing history, , and demographics to optimize reach and , enabling advertisers to measure via metrics like click-through rates and conversions.
The practice originated in 1994 with the debut of the first clickable banner ad by on the HotWired website, transitioning advertising from static print and broadcast media to interactive digital formats amid the web's commercialization. Subsequent innovations, including in the early 2000s and programmatic via auctions, scaled the industry by automating ad placements and enhancing efficiency through algorithmic decision-making. In 2024, global digital ad spending reached approximately $694 billion, representing over half of total advertising expenditures and funding vast portions of content provision, with dominant platforms like and Meta capturing the majority of revenues through integrated ecosystems. This growth reflects empirical demonstrations of effectiveness, where targeted ads have been shown to positively impact consumer mindsets and purchase intentions, outperforming untargeted alternatives in controlled studies. However, the sector grapples with systemic issues, including ad fraud—estimated to affect 18% of digital ad impressions worldwide in 2024, resulting in over $100 billion in annual losses through bots and fake traffic—and erosions from pervasive tracking that compile detailed user profiles without uniform mechanisms. These challenges, compounded by regulatory responses like data protection laws, underscore tensions between advertising's economic utility and its causal contributions to economies and deceptive practices.

History

Early pioneers (1990s)

The origins of online advertising trace to proprietary dial-up services in the late 1980s and early , where platforms like Prodigy introduced fixed-position graphical ads to support content delivery amid subscriber fees. Prodigy, a of , , and launched in 1988, pioneered consumer-facing online ads by displaying them consistently across screens, generating revenue through sponsorships from brands like as early as 1990. These early efforts, however, were confined to closed networks without hyperlinked , limiting their compared to the open web. The pivotal shift occurred with the World Wide Web's commercialization, as banner advertising debuted on October 27, 1994, when HotWired—the digital arm of Wired magazine—launched with rotating banner ads from 14 sponsors, including AT&T, MCI, Volvo, Club Med, and Zima. AT&T's inaugural banner, a 468x60 pixel rectangle proclaiming "Have you ever clicked your mouse right here? You will," targeted tech-savvy users and achieved an initial click-through rate (CTR) of 44%, far exceeding later industry averages of under 1%. This success stemmed from novelty and minimal ad fatigue, with HotWired charging $14,000 for the first million impressions across four ad slots, establishing impression-based pricing as a standard. Ad technology advanced rapidly to manage growing . In 1995, FocaLink Media Services developed the first dedicated ad server, enabling automated delivery and basic tracking of and clicks on sites like Pathfinder and SportsZone. DoubleClick, founded in 1996 by Kevin O'Connor and , introduced the DART (Dynamic Advertising Reporting and Targeting) system, which automated ad serving, rotation, and rudimentary behavioral targeting based on IP addresses and cookies, handling millions of daily for clients like . By 1998, DoubleClick's IPO valued it at $1.3 billion, reflecting explosive demand as U.S. online ad spend reached $1.9 billion, dominated by display formats amid the dot-com boom. These pioneers faced challenges, including emerging by 1997—where users ignored static creatives—and privacy concerns over early tracking, yet they laid the infrastructure for scalable, data-informed advertising. Pioneering agencies like 24/7 Media and Adsmart also emerged, aggregating inventory from thousands of sites, but DoubleClick's innovations in proved most influential, processing over 2 billion impressions monthly by decade's end.

Search and display dominance (2000s)

The 2000s witnessed the entrenchment of search and display advertising as the preeminent formats in online advertising, buoyed by technological innovations and a rebound from the dot-com bust's fallout. The burst of the in March 2000 precipitated a sharp downturn in digital ad expenditures, as speculative firms curtailed aggressive outlays amid widespread bankruptcies and investor retrenchment, with U.S. online ad revenues contracting significantly after peaking at $8.2 billion in 2000. Recovery commenced around 2003, driven by more sustainable models emphasizing measurable returns, culminating in revenues climbing to $26 billion by 2010. Search advertising ascended rapidly, propelled by Google's AdWords platform, launched on October 23, 2000, which offered advertisers a system for keyword-targeted text ads aligned with user queries. Initially featuring flat-rate pricing for a limited set of advertisers, AdWords transitioned to cost-per-click auctions in , enhancing efficiency by tying payments to actual engagement rather than . This intent-driven approach fueled explosive growth; by 2004, paid search constituted 35% of total ad revenue, totaling $2.5 billion, outpacing other formats in efficacy and advertiser adoption. Search's expanded from 1% in 2000 to 46% by 2010, reflecting its superiority in capturing high-intent traffic compared to less precise alternatives. Display advertising, centered on and graphical formats inherited from the , retained substantial volume but grappled with due to user habituation and "," where average click-through rates plummeted below 1% by the mid-decade as audiences grew ad-averse. Despite these challenges, display held a 38% share by 2010, supported by 's AdSense program, introduced in 2003, which automated contextual ad placement on third-party websites, thereby extending reach beyond search results and fostering a nascent content-driven . The interplay between search's precision and display's scale underscored their joint , with leveraging both to amass over 60% of search ad by decade's end, laying groundwork for consolidated platform control.

Mobile, social, and programmatic expansion (2010s)

The proliferation of and app ecosystems in the early catalyzed a surge in , as advertisers shifted budgets from desktop to portable devices. In , U.S. mobile ad spending totaled $550–$650 million, representing the inaugural year of formal revenue tracking by the (IAB), amid rising adoption that drove mobile data traffic to grow 2.6-fold globally. By 2012, worldwide mobile ad revenue reached $9.8 billion, fueled by platforms like and Android enabling in-app and location-based targeting, with U.S. projections indicating an 80% year-over-year increase in mobile spend that year. This expansion reflected causal shifts in consumer behavior, where mobile devices captured 78% of data traffic despite comprising only 13% of handsets by , prioritizing empirical usage patterns over legacy desktop dominance. Social media platforms amplified this growth by integrating advertising into user feeds and networks, leveraging vast for precision targeting. U.S. ad spending rose 7.1% in 2010 following a modest 3.9% recovery from 2009, as platforms like refined sponsored content post its 2007 ad launch but accelerated mobile formats around 2012. Global ad revenue quadrupled from $7.3 billion in 2010 to nearly $31 billion by 2016, driven by milestones such as Instagram's ad introduction in 2013 and Twitter's promoted tweets in 2010, which capitalized on real-time metrics rather than unverified virality claims. These developments intertwined with mobile, as derived 93% of its ad revenue from mobile by mid-decade, underscoring data-backed attribution over anecdotal platform hype. Programmatic advertising emerged as a core mechanism, automating ad purchases through (RTB) and demand-side platforms, reducing manual negotiations. Adoption accelerated in the mid-2010s, with nearly half of digital ads traded programmatically by 2015, up from nascent RTB trials around 2009–2010, as supply-path optimization addressed early fragmentation in inventory quality. This shift, extending to mobile and social channels, prioritized —evident in U.S. ad revenues hitting $26 billion overall in 2010, a 15% rise—over traditional direct deals, though it introduced challenges like ad that required subsequent verification standards. By decade's end, these vectors collectively propelled online 's scale, with programmatic enabling cross-channel scalability grounded in auction-based economics rather than opaque networks.

AI-driven innovations and privacy shifts (2020s)

In the 2020s, advanced online advertising through enhanced automation in programmatic platforms, where algorithms optimized and audience segmentation with greater precision. Generative AI emerged as a key innovation, enabling automated creation of ad copy, images, and dynamic video content tailored to individual users, reducing production times from weeks to hours, with nearly 90% of advertisers planning to use it for video ads by 2025. These tools leveraged large language models to generate personalized creatives, improving engagement rates while minimizing human intervention in campaign scaling. Emerging agentic AI systems, capable of autonomous task execution, introduced challenges to traditional ad engagement by potentially bypassing clickable ads in favor of direct intent fulfillment in AI-powered environments. Privacy regulations and technological changes concurrently disrupted traditional tracking methods, prompting a pivot toward privacy-preserving AI techniques. Apple's App Tracking Transparency framework, implemented in 14.5 on April 26, 2021, required explicit user consent for cross-app tracking via the (IDFA), resulting in widespread opt-outs that diminished third-party data availability. This shift caused measurable declines in ad attribution accuracy, with mobile marketers reporting reduced performance metrics as signal loss affected up to 30% of traffic. Google's response included the initiative, announced in 2019 and iteratively developed through the decade to replace third-party cookies with and cohort-based targeting. However, on October 21, 2025, discontinued , citing the ad industry's rapid adoption of AI-driven strategies that infer from aggregated, anonymized signals rather than personalized identifiers. Platform adaptations included integrating ads into AI Overviews, where sponsored content appears within AI-generated search responses for queries with commercial intent, expanding to desktop and additional countries by late 2025. This abandonment reflected a broader causal dynamic: privacy constraints accelerated AI reliance on first-party and contextual , fostering innovations like on-device for ad relevance without persistent tracking. The interplay of these trends yielded hybrid models, where AI compensated for data scarcity by predicting behaviors from behavioral patterns and environmental contexts, sustaining revenue growth amid regulatory pressures. Systematic reviews of peer-reviewed studies from 2018–2024 highlight AI's role in transitioning from intrusive to ethical, outcome-focused ecosystems. By 2025, programmatic platforms integrated generative AI for real-time ad variations, enabling advertisers to navigate cookieless environments while adhering to frameworks like GDPR and emerging U.S. state laws.

Core Technologies and Mechanisms

Targeting algorithms and data collection

Targeting algorithms in online advertising employ machine learning models to analyze user data and predict ad relevance, segmenting audiences by demographics, behaviors, and inferred interests to optimize delivery. These systems process signals such as browsing history, search queries, and purchase patterns to construct probabilistic profiles, enabling platforms like Google and Meta to match ads with high-propensity users. For instance, contextual targeting infers intent from page content, while behavioral targeting tracks cross-site actions to refine predictions. A common misconception holds that advertisements appearing relevant after offline discussions result from device microphones eavesdropping on conversations; empirical evidence attributes this instead to extensive data sources including users' online activity, search histories, location data, friends' behaviors via social graphs, and on-platform interactions, which generate correlations without audio surveillance. Data collection underpins these algorithms through persistent identifiers and passive tracking. Third-party , set by ad networks across sites, store user IDs for cross-domain profiling, though their phase-out accelerates post-2024 via browser policies from Chrome and . Tracking pixels—tiny, invisible images embedded in webpages, emails, and ads—trigger server calls upon loading, events like views or clicks without user interaction. Device fingerprinting aggregates browser attributes (e.g., screen resolution, installed fonts, timezone) and hardware signals to generate unique hashes, evading cookie restrictions and enabling 99% identification accuracy in some studies. Integration of collected into algorithms yields measurable efficacy gains. Empirical analyses indicate AI-driven targeting boosts by 20-50% over non-algorithmic methods, via precise audience matching that elevates click-through rates and conversions. A 2025 study of ad campaigns found algorithmic increased by correlating user signals with outcome probabilities, though effectiveness diminishes with sparse post-deprecation of . Privacy regulations constrain these practices, mandating consent and data minimization. The EU's GDPR, effective May 25, 2018, requires explicit opt-in for non-essential processing, reducing available signals and prompting reliance on consented first-party data. California's CCPA, enforced from January 1, 2020, grants rights for sales of personal information, with 88% of advertisers reporting impacts on personalized targeting per a 2024 survey. Compliance has spurred cookieless alternatives like fingerprinting, yet signal loss from aggregated anonymization erodes precision, with industry estimates projecting 15-30% revenue dips without adaptive modeling. Mainstream analyses often understate persistence of tracking via probabilistic methods, as enforcement varies and workarounds proliferate despite regulatory intent.

Programmatic buying and real-time bidding

Programmatic buying refers to the automated of purchasing digital inventory through software platforms, replacing manual negotiations with algorithmic decision-making based on parameters such as targeting, ad placement, and pricing. This method emerged in the early as ad exchanges facilitated automated trades, evolving from earlier ad networks that aggregated remnant inventory. By 2024, programmatic accounted for approximately 595 billion USD in global ad spend, representing nearly 90% of transactions worldwide. Real-time bidding (RTB) constitutes a primary mechanism within programmatic buying, wherein individual ad impressions are auctioned in milliseconds via open or closed exchanges before a webpage loads. In this process, a publisher's supply-side platform (SSP) sends a bid request containing user data and impression details to an ad exchange, which notifies demand-side platforms (DSPs) representing advertisers; DSPs then evaluate and submit bids algorithmically, with the highest bidder winning the impression for display. RTB auctions typically occur on a cost-per-impression (CPM) basis, enabling precise targeting using first-party data, cookies, or device IDs, though privacy regulations like GDPR have prompted shifts toward contextual and consented data alternatives. The adoption of RTB has driven efficiency gains, allowing advertisers to optimize campaigns in real time and access vast inventory pools without fixed commitments, while publishers maximize yield through competitive bidding. For instance, RTB reduces manual labor and enables , potentially lowering costs by 20-30% compared to traditional direct buys, according to industry analyses. However, RTB's opacity in supply chains has facilitated ad , including invalid from bots estimated at 20-40% of impressions in some reports, eroding trust and inflating effective costs. Brand safety risks persist, as automated placements may juxtapose ads with unsuitable content, prompting tools like pre-bid verification to mitigate mismatches. Programmatic buying extends beyond RTB to include private marketplaces (PMPs) and programmatic deals, which offer guaranteed at fixed prices without auctions, providing transparency advantages for premium placements. Despite challenges, RTB's has propelled programmatic dominance, with projections indicating growth to 800 billion USD by 2028, fueled by advancements in AI-driven bidding and header bidding techniques that enhance publisher revenue by competing multiple SSPs simultaneously.

Attribution models and analytics

Attribution models in online advertising assign credit for conversions, such as purchases or sign-ups, to specific touchpoints in a user's multi-channel journey, enabling advertisers to evaluate campaign effectiveness beyond simplistic metrics like last-click attribution. These models address the complexity of paths involving search ads, display banners, social media interactions, and email, where users often engage multiple assets before converting. Rule-based models distribute credit heuristically, while data-driven variants leverage machine learning on historical data to weigh contributions empirically. Common rule-based models include first-touch, which credits the initial interaction fully, often overvaluing awareness-stage channels like display ads; last-touch (or last-click), which attributes 100% to the final click, favoring direct-response tactics but ignoring upstream influences; linear, apportioning equal shares across all touchpoints; time-decay, escalating toward recency; and position-based (U-shaped), allocating 40% to first, 40% to last, and 20% to intermediates. -driven attribution (DDA), implemented in platforms like and , analyzes conversion patterns to dynamically assign value, requiring sufficient volume for accuracy and outperforming rules in complex funnels per platform analyses. Analytics for attribution involve aggregating user-level data via pixels, SDKs, and server-side tracking to reconstruct journeys, often using tools like 4 or Adobe Analytics for visualization and modeling. These systems compute metrics such as assisted conversions—touchpoints aiding but not closing sales—and incremental lift, quantifying ad exposure's causal impact through experiments. However, and view-through conversions (crediting impressions without clicks) complicate accuracy, with studies showing last-click underestimates early-funnel contributions by up to 50% in scenarios. Privacy regulations and the deprecation of third-party since 2023 have intensified attribution challenges, fragmenting cross-site tracking and inflating signal loss to 20-30% in cookieless environments, prompting shifts to first-party data and contextual signals. Google's delayed but ongoing cookie phase-out by 2025 necessitates probabilistic modeling and consented data pools, while platforms like emphasize privacy-safe DDA to mitigate biases from incomplete paths. indicates that hybrid approaches, combining DDA with incrementality tests, yield more causal insights than legacy models amid these constraints.

Major Formats and Types

Display and banner advertising

Display advertising encompasses visual promotions delivered across websites, mobile apps, and other digital platforms, typically featuring banners, images, text, or multimedia elements to drive brand awareness or direct responses. Banner advertising, a core subset, refers to rectangular graphical ads positioned at the top, bottom, sides, or within content of web pages, often standardized in sizes like 728x90 pixels (leaderboard) or 300x250 pixels (medium rectangle). The first banner ad appeared on October 27, 1994, when AT&T sponsored a campaign on HotWired, the digital edition of Wired magazine, featuring the slogan "Have you ever clicked your mouse right here? YOU WILL." This 468x60 pixel ad achieved a 44% click-through rate (CTR), far exceeding modern benchmarks, as early internet users encountered few distractions. Display ads vary in format, including static images for simple messaging, animated variants using GIFs or for motion without plugins, and rich media incorporating such as expandable panels, video playback, or user-triggered elements like carousels. Rich media formats enhance over static banners but require larger file sizes and compatible ad servers. Ads are served via networks that match advertiser creatives to publisher inventory, often through programmatic systems involving for impressions. Empirical data indicate limited direct-response effectiveness for display and ads, with average CTRs ranging from 0.05% to 0.1% across industries, prioritizing branding and reach over conversions. In 2024, U.S. ad revenue reached $259 billion, with display formats comprising a significant share amid programmatic dominance, which handled nearly 90% of digital display transactions globally. Key challenges include , where users psychologically ignore ad-resembling content—eye-tracking studies show 86% of consumers skip banner areas—and ad blockers, which evaded billions in impressions by altering page loads or filtering requests. Ad fatigue from repetitive exposure further diminishes returns, prompting shifts toward contextual or native integrations, though core display persists for cost-efficient awareness at scale.

Search engine marketing

Search engine marketing () encompasses paid advertising strategies designed to promote websites by increasing their visibility in search engine results pages (SERPs), primarily through auction-based systems where advertisers bid on keywords relevant to user queries. Unlike organic search optimization, SEM relies on sponsored placements, often in the form of (PPC) models, where charges accrue only upon user interaction such as clicks. This approach leverages signals inherent in search behavior, enabling advertisers to target high-conversion opportunities when consumers actively seek products or services. The foundational platform for modern SEM, Google AdWords (rebranded as Google Ads in 2018), launched on October 23, 2000, introducing a self-serve PPC system that revolutionized digital advertising by aligning ad relevance with search queries via automated auctions. Early iterations featured simple text ads displayed alongside organic results, with innovations like quality scoring—introduced in 2005—to factor ad relevance and landing page experience into bid rankings, thereby rewarding effective campaigns over mere spending. Subsequent developments included expanded text ads in 2016 and responsive search ads in 2018, which use machine learning to dynamically assemble ad variations for optimal performance. Google dominates the SEM landscape, holding 92.61% of the global market share as of 2025, which translates to substantial control over paid search inventory. Other platforms include (formerly Bing Ads) and smaller engines like Yahoo, but they collectively represent a minority of . The SEM services market was valued at $120.3 billion in 2024, projected to reach $278.5 billion by 2034 at a (CAGR) of approximately 8.8%, driven by rising digital commerce and mobile search adoption. Google's alone exceeded $264 billion in 2024, underscoring SEM's scale within online advertising ecosystems. Core mechanisms involve to identify high-intent terms, followed by real-time auctions where ad rank is determined by bid amount multiplied by quality score, ensuring cost efficiency for relevant . Advertisers set budgets, targeting options (e.g., demographics, , device), and negative keywords to refine , with extensions like sitelinks enhancing utility. Empirical studies indicate SEM's effectiveness, particularly in generating steady and positive returns; for instance, retargeted PPC campaigns based on prior site visits have demonstrated positive ROI in controlled analyses. Comparative research shows SEM outperforming in ROI for intent-driven purchases, though success depends on precise targeting and optimization to convert clicks into actions. Challenges include rising cost-per-click rates due to and fatigue, mitigated by ongoing algorithmic refinements.

Social media and influencer advertising

Social media advertising encompasses paid promotional content delivered through platforms like Meta's and , , and X (formerly Twitter), leveraging user-generated data for precise targeting based on demographics, interests, behaviors, and past interactions. Common formats include in-feed posts, stories, , and carousel ads, which integrate seamlessly with organic content to drive engagement, traffic, or conversions. In 2024, U.S. social media ad revenues reached $88.8 billion, reflecting a $23.8 billion increase from 2023, driven by video and short-form content formats. Globally, the market is projected to hit $275.98 billion in 2025, accounting for a significant portion of digital ad spend due to platforms' vast user bases exceeding 5 billion monthly across major networks. Targeting algorithms on these platforms analyze first-party from user profiles, likes, shares, and device signals to segment audiences, enabling advertisers to reach niche groups with high relevance—such as age-specific cohorts on , where over 60% of users are under 30. Empirical studies indicate that such precision boosts click-through rates by 2-3 times compared to non-targeted display ads, though attribution remains complicated by multi-touch user journeys across apps and web. Effectiveness varies by platform: excels in visual via shoppable posts, while 's favors viral, algorithm-driven discovery, yielding higher organic reach amplification for ads. However, ad fatigue and privacy regulations like Apple's App Tracking Transparency have reduced targeting efficacy since 2021, prompting shifts toward contextual and probabilistic methods. Influencer advertising, a subset often integrated with social media, involves brands compensating individuals with sizable followings for authentic endorsements, typically through sponsored posts, stories, or live streams that disclose partnerships per FTC guidelines. The global influencer marketing industry was valued at $24 billion in 2024 and is forecasted to reach $32.55 billion in 2025, fueled by micro- and nano-influencers (under 100,000 followers) who command lower fees but deliver superior engagement rates averaging 3-5%. Peer-reviewed meta-analyses confirm influencers enhance brand attitudes and purchase intent more than traditional ads, particularly for experiential products, due to perceived authenticity and social proof—effects amplified when endorsements align with the influencer's niche expertise. ROI measurement for influencer campaigns relies on metrics like earned media value (EMV), conversion tracking via unique promo codes, and lift studies, with direct-to-consumer brands reporting 5-11x returns from smaller influencers versus celebrities, whose broad appeal often dilutes impact amid high costs. Challenges include fake followers and engagement pods inflating metrics—up to 20% of influencer audiences may be inauthentic per industry audits—necessitating tools for fraud detection and post-campaign verification. Despite these, longitudinal data shows sustained consumer trust in disclosed sponsorships, with 49% of buyers influenced by influencer recommendations in 2024 surveys, underscoring the format's causal role in driving sales over mere awareness.

Video, native, and emerging formats

in online contexts primarily includes in-stream formats such as pre-roll, mid-roll, and post-roll ads embedded within content on platforms like and streaming services, as well as outstream formats that autoplay outside traditional players on websites and apps. By 2025, advertising—encompassing connected TV (CTV), social video, and online —drives nearly 60% of total U.S. TV and ad spending, reflecting its shift from supplementary to core revenue driver amid declining linear TV viewership. Short-form videos under one minute achieve the highest engagement rates, with brands prioritizing them over longer formats due to consumer preferences for quick consumption on mobile and social platforms. Native advertising consists of paid promotions designed to blend seamlessly with surrounding editorial or , adopting the platform's visual style, tone, and functionality to minimize disruption and ad avoidance. This format, often labeled as "sponsored" or "promoted," appears in feeds on , news sites, or recommendation engines, where it matches the look of organic posts or articles. Empirical studies demonstrate native ads outperform traditional display banners in attention metrics and brand lift, with one analysis of large-scale placement data showing higher click-through rates and reduced bounce rates when content congruence aligns with user expectations, though effectiveness diminishes if perceived as overly promotional or annoying. Native formats foster greater trust and credibility over time compared to intrusive banners, as their contextual encourages voluntary rather than forced exposure. Emerging online ad formats in 2025 leverage technological advances and shifting behaviors, including interactive shoppable videos that enable direct purchases within playback, (AR) overlays for immersive trials, and AI-generated personalized video sequences adapting in real-time to viewer data. Short-form vertical video ads on hyperscale platforms like and Reels dominate trends, capitalizing on users' average 100 minutes daily video consumption and comprising the preferred content type for brands seeking viral reach. Connected TV (CTV) extensions, such as addressable ads on streaming services, represent rapid growth, blending video with programmatic targeting for household-level precision, while soundless "silent" ads address muted autoplay habits. These formats prioritize performance metrics like conversions over impressions, with carousel and story-based interactives boosting engagement on social channels by integrating elements directly into ad experiences. Despite promise, their efficacy hinges on platform algorithms favoring authentic-feeling content, as over-reliance on novelty risks user fatigue without sustained ROI validation from controlled trials.

Email, affiliate, and performance-based variants

Email advertising entails the delivery of promotional content directly to recipients' inboxes, typically to lists built through opt-in mechanisms such as sign-ups or purchases, with messages including calls-to-action for conversions like or sign-ups. In the United States, it is governed by the , which requires s to be accurately labeled as advertisements, provide a functional mechanism honored within 10 business days, and include the sender's valid physical postal address to curb deceptive practices. Violations can result in fines up to $43,792 per as of 2024 adjustments. Globally, nearly 4.5 billion people used in 2025, underpinning its scale in online advertising. Effectiveness metrics highlight email's direct-response strengths: average open rates reached 32.55% across industries in August 2024, with click-through rates varying by segmentation—personalized campaigns yielding 100.95% higher CTRs than non-segmented ones. averages $36 per $1 spent, outperforming many digital channels due to low marginal costs post-list acquisition. However, deliverability challenges persist, with 47% of marketers citing it as their top concern amid spam filters and inbox prioritization algorithms from providers like . Affiliate marketing operates as a commission-based variant where third-party publishers, or affiliates, promote advertisers' products via unique tracking links on websites, , or emails, earning payments only upon attributable actions such as referrals or sales. Online origins trace to Amazon Associates, launched July 16, 1996, allowing website owners to embed product links and receive commissions up to 10% on qualifying purchases. Early adopters included music retailer in 1994, which partnered with for referral fees, formalizing the model before Amazon's scale. The global market reached $17 billion in 2023, driven by growth and networks like Commission Junction (founded 1998) and . Projections estimate $15.7 billion in spending for 2024, with affiliates generating 16% of U.S. online orders. Performance-based advertising encompasses models tying compensation to measurable outcomes, minimizing advertiser risk compared to impression or view-based payments. Key variants include cost-per-click (CPC), charging per user click on an ad—common in search and display, with average costs varying by industry (e.g., $1-2 for in 2024)—and cost-per-action (CPA), reimbursing for conversions like form submissions or purchases, often $20-100 per acquisition depending on product value. Affiliate programs exemplify CPA, as do lead-generation campaigns where publishers are paid for qualified prospects. These models leverage tracking pixels and for attribution, though multi-device behavior complicates accuracy. Empirical data shows CPA campaigns achieving ROAS (return on ad spend) of 4:1 or higher when optimized, as advertisers only pay for verified results. and affiliate often integrate performance elements, with hybrid tracking ensuring payments align with causal contributions to sales.

Business Models and Compensation

Cost-per-action metrics (CPC, CPM, CPA)

Cost per mille (CPM), or cost per thousand impressions, is a pricing model in online advertising where advertisers pay a fixed amount for every 1,000 times their ad is displayed, irrespective of user engagement such as clicks or conversions. This metric, derived from the Latin "mille" meaning thousand, originated in traditional media like print and broadcast but adapted to digital formats to measure exposure efficiency. The formula for CPM is calculated as (total ad spend divided by total impressions) multiplied by 1,000, enabling publishers to monetize inventory based on viewership volume rather than outcomes. CPM suits campaigns prioritizing broad reach and , as it incentivizes high-volume impressions over targeted interactions, though it risks inefficiency if impressions fail to drive measurable results. In display and , CPM rates vary by platform and audience; for instance, premium sites command higher rates due to verified traffic quality, while programmatic exchanges often feature lower, auction-driven CPMs. Critics note that CPM overlooks ad viewability—actual to users—which can inflate perceived value, with industry standards requiring at least 50% of pixels in view for one continuous second to count as viewable. Cost per click (CPC) charges advertisers only when a user interacts by clicking the ad, shifting focus from mere exposure to intent-driven traffic. Introduced prominently with search engines like in the early 2000s via models, CPC bidding allows advertisers to set maximum bids per click, with auctions determining ad placement based on bid, quality score, and . CPC is computed as total spend divided by total clicks, making it prevalent in and performance-oriented display campaigns where traffic volume correlates with potential conversions. CPC promotes accountability by tying costs to engagement, yet average rates fluctuate widely—e.g., $1–$2 per click in competitive sectors like as of 2025—prompting advertisers to optimize for to lower effective costs through higher click-through rates. Compared to CPM, CPC reduces waste from non-engaging impressions but may undervalue awareness-building efforts, as non-clicking exposures still contribute to recall and consideration in the marketing funnel. Cost per action (CPA), also termed cost per acquisition, remunerates publishers or networks solely upon completion of a predefined user action, such as a purchase, lead form submission, or app install, rendering it a results-oriented model. CPA evolved from in the late 1990s, emphasizing downstream outcomes over intermediate metrics, with the formula being total campaign cost divided by number of actions achieved. Platforms like report average search CPAs around $49 across industries in 2025, though can exceed $45 on social channels, reflecting variability by conversion complexity.
MetricFocusCalculationBest ForDrawbacks
CPMImpressions(Cost / Impressions) × 1,000, reachIgnores engagement quality; vulnerable to low viewability
CPCClicksCost / Clicks generation, intent captureMay overlook passive exposure value; click fraud risks
CPAConversionsCost / ActionsDirect response, ROI optimizationHigher upfront risk for publishers; attribution challenges in multi-touch paths
CPA maximizes alignment with business goals by deferring payment until value is realized, outperforming CPC and CPM in conversion-heavy funnels, but demands robust tracking to attribute actions accurately amid cookie deprecation and cross-device behaviors. Empirical analyses indicate CPA yields superior long-term ROI for , as it filters out non-performing , though it requires larger budgets to scale due to dependency on conversion rates. Hybrid models blending these—e.g., CPC with CPA targets—emerge to balance risk, particularly in programmatic ecosystems where real-time optimization adjusts bids dynamically.

Hybrid and fixed-cost approaches

Fixed-cost approaches in online advertising involve advertisers paying a predetermined flat fee for ad placements, independent of metrics like clicks or impressions. This model, also known as flat-rate pricing, provides publishers with revenue predictability and simplifies forecasting, as earnings are guaranteed regardless of traffic fluctuations. It contrasts with performance-based models by shifting risk to the advertiser, who commits to the fee for specified such as slots or newsletter mentions. Sponsorships exemplify fixed-cost deals, where brands pay a for branded content integration or association with digital properties, like newsletters or podcasts, ensuring prominent visibility without competition. For instance, sponsorships inbox delivery for a fixed amount, offering advertisers direct audience access amid rising ad fatigue from algorithmic feeds. Direct-sold , often used by premium publishers, employs fixed fees for guaranteed placements in high-value positions, prioritizing brand safety and control over programmatic variability. Hybrid models blend fixed-cost elements with performance incentives, such as a base retainer plus bonuses for exceeding click thresholds, balancing stability with . This approach mitigates the limitations of pure fixed fees—where underperformance yields no recourse—by incorporating variable components like cost-per-click surcharges. In practice, hybrids appear in agency contracts or campaigns mixing guaranteed sponsorships with CPM adjustments, allowing advertisers to cap upfront costs while tying portions to outcomes. Empirical adoption of hybrids has grown for their flexibility in volatile digital markets, enabling publishers to secure baseline revenue amid declining CPMs from ad blockers and .

Attribution challenges in multi-channel campaigns

In multi-channel online advertising campaigns, attribution refers to the process of assigning credit for conversions—such as or leads—to specific touchpoints across channels like search, display, , , and programmatic ads. This task is complicated by the non-linear of journeys, where users often interact with multiple channels before converting, leading to fragmented that obscures causal contributions. Traditional single-touch models, such as last-click attribution, which credits 100% of the value to the final interaction, systematically undervalue upper-funnel channels like display ads while inflating the role of bottom-funnel tactics like paid search. Evidence from indicates that last-click models create bias by ignoring externalities from multi-homing s exposed to ads across publishers, resulting in suboptimal allocation. Multi-touch attribution (MTA) models attempt to address this by distributing credit across interactions using rule-based approaches (e.g., linear or time-decay) or data-driven methods leveraging to weigh touchpoints based on historical conversion . However, faces significant hurdles, including silos between platforms, which hinder holistic tracking; for instance, 41% of marketers report difficulty in unifying customer touchpoints across channels as a primary barrier. Cross-device behavior further exacerbates issues, as users switch between mobile, desktop, and apps without persistent identifiers, leading to undercounting of assisted conversions. regulations, such as the of third-party by browsers like Chrome (phased out starting 2024), compound these problems by limiting probabilistic matching and user-level tracking, forcing reliance on aggregated or contextual signals that reduce accuracy. Empirical studies highlight the causal realism deficit in flawed models: data-driven MTA can improve ROI estimation by 20-30% over heuristics in controlled experiments, but requires vast datasets and computational resources, with 42% of professionals citing insufficient expertise as a blocker. Moreover, externalities like view-through conversions—where exposure without clicks influences later actions—are often unmeasured, biasing against non-click channels. Industry reports note that without robust MTA, campaigns misallocate up to 30% of budgets to overcredited channels, underscoring the need for incrementality testing via randomized experiments to validate attributions empirically rather than assuming model outputs. These challenges persist despite advances, as no model fully captures unobserved influences like offline interactions or competitive effects, necessitating hybrid approaches combining MTA with econometric methods for .

Economic Impact and Empirical Effectiveness

Global market scale and growth drivers

The global digital advertising market generated approximately $600 billion in revenue in 2024, representing over 70% of total worldwide advertising spend and surpassing traditional media channels. Projections indicate growth to around $777 billion in 2025, accounting for 75.2% of the $1 trillion total ad market, driven by accelerated digital adoption amid economic recovery. This expansion reflects a (CAGR) of roughly 9-10% through the decade, outpacing overall GDP growth in major economies due to measurable returns on digital targeting. Key drivers include surging internet penetration and smartphone proliferation, which expanded the addressable audience to over 5 billion users by 2024, enabling scalable reach unattainable in offline media. acceleration, fueled by post-pandemic shifts and platforms like Amazon and Alibaba, has correlated with higher ad allocations, as retailers leverage data-rich environments for direct-response campaigns yielding higher conversion rates than broadcast alternatives. Advancements in programmatic buying and AI-driven personalization further amplify efficiency, reducing acquisition costs by up to 30% through and behavioral segmentation, while and video formats capture shifting consumer attention spans. Emerging markets in and contribute disproportionately to growth, with CAGRs exceeding 12% due to and affordable data plans, contrasting slower maturation in and where saturation prompts innovation in formats like connected TV. Regulatory pressures on , such as cookie deprecation, paradoxically spur investment in first-party data and contextual targeting as proxies for sustained performance. Overall, these factors underscore a causal shift from impression-based to outcome-oriented spending, substantiated by advertiser ROI metrics favoring digital over legacy channels.

ROI evidence versus traditional media

Empirical assessments of return on investment (ROI) in online advertising compared to traditional media reveal mixed outcomes, influenced by differences in measurability, targeting precision, and attribution methodologies. Online platforms enable granular tracking of user interactions, such as clicks and conversions, facilitating calculation of return on ad spend (ROAS) for performance-oriented campaigns; for instance, paid search advertising has demonstrated a stronger positive effect on short-term sales than offline advertising, attributed to its proximity to purchase intent and algorithmic targeting. In contrast, traditional media like television, radio, and print rely on broader impression-based metrics, where direct attribution is challenging, yet studies indicate superior efficiency in delivering reach, attention, and engagement relative to costs, particularly amid rising digital ad fraud and inventory saturation. These disparities underscore that online advertising often yields higher tactical ROI for lower-funnel objectives, while traditional channels contribute more to upper-funnel brand equity, with long-term firm value enhancements from display ads sometimes exceeding offline impacts. Quantitative comparisons highlight contextual dependencies. Ebiquity's analysis of media efficiency found traditional channels outperforming digital in cost-adjusted and reach, prompting marketer reallocations; for example, predictions from the CMO Survey indicated a 2.9% increase in traditional spending by early 2022, reversing prior declines, as digital costs escalated due to invalid traffic. Academic examinations of firm-level data across 1,651 companies over seven years confirmed online display advertising's edge in elevating (a proxy for firm value) over offline equivalents, yet emphasized heterogeneous returns based on campaign goals—paid search for immediate revenue, display for sustained valuation. However, systemic issues like ad viewability (often below 50% for digital) and multi-touch attribution errors can inflate perceived online ROI, whereas traditional media's halo effects on offline sales are underrepresented in siloed analyses. Integrated media mixes mitigate these limitations, with evidence suggesting optimal ROI emerges from complementary use rather than zero-sum shifts. Nielsen's reports note that over-reliance on digital channels risks suboptimal reach, as traditional formats maintain advantages in audience scale and trust, driving incremental lifts when combined with tactics. Kantar studies reinforce television's role in unmatched effectiveness for awareness, despite marketer preferences tilting digital, highlighting a disconnect where empirical receptivity favors hybrid strategies over pure pivots. Ultimately, while advertising's data-driven precision supports claims of superior direct ROI in contexts, traditional media's proven persistence in brand-building metrics challenges narratives of outright digital dominance, urging causal evaluation beyond self-reported platform data.

Subsidy role in digital content ecosystems

Online advertising acts as a critical for ecosystems, funding the production and distribution of free or low-cost online media, including news articles, posts, videos, and search results, without requiring direct user payments. This model emerged prominently in the with the of the web, where platforms monetize user attention through targeted ads, generating revenue streams that cover operational costs and incentivize . In the United States, advertising revenue reached $258.6 billion in 2024, representing a 14.9% year-over-year increase and underpinning ecosystems reliant on ad-supported access. Globally, digital ad spending supports key industries like and video, funding , infrastructure, and labor for platforms serving billions of users daily. The enables widespread dissemination of , contrasting with subscription-only models that limit reach to paying audiences; for instance, ad allows search engines and social networks to offer core services gratis, drawing users who might otherwise forgo them due to cost barriers. Empirical analyses indicate this approach expands consumer welfare by lowering entry costs for content consumption, with digital ads comprising about 65% of total U.S. spend by recent estimates, fueling a valued at $4.9 trillion or 18% of GDP in 2025. However, industry reports like those from the , while highlighting economic contributions, may underemphasize incentive misalignments due to their promotional stake in ad growth. Causally, the ad-subsidy structure ties publisher revenues to metrics like impressions, clicks, and engagement, prioritizing volume over depth and fostering low-quality content optimized for algorithmic amplification rather than informational value. This has proliferated "made-for-advertising" (MFA) sites, which generate thin, sensational material solely to harvest ad views, diverting funds from substantive journalism; estimates place annual U.S. ad spend on such clickbait domains at $17 billion, siphoning resources that could support verified reporting. Programmatic systems exacerbate this by automating placements without rigorous quality checks, subsidizing disinformation and conspiracy-laden pages that exploit engagement loops. While peer-reviewed comparisons of ad-funded versus subscription models remain sparse, the mechanism incentivizes creators to maximize short-term attention—often via outrage or novelty—over sustained accuracy, as evidenced by the rise of content farms during ad revenue booms. In response, some platforms experiment with hybrid models blending ads and subscriptions to balance subsidies with quality signals, though ad dominance persists due to its scale advantages in user acquisition. This dynamic underscores a trade-off: broad accessibility subsidized by advertising democratizes content but risks eroding ecosystem integrity through perverse incentives, where low-effort output crowds out high-value alternatives unless advertisers or regulators intervene on placement standards.

Criticisms and Operational Challenges

Ad fraud, bots, and invalid traffic

Ad fraud refers to deceptive practices in online advertising where fraudsters generate artificial impressions, clicks, or conversions to extract revenue from advertisers, often through automated bots that simulate . Invalid traffic (IVT) encompasses this non-genuine activity, including bot-generated interactions, traffic, and incentivized or hijacked human actions, which distort campaign metrics and waste budgets. In programmatic advertising, exacerbates vulnerabilities, as bots can rapidly bid on inventory using techniques like domain spoofing—falsely representing traffic origins from high-value sites—or ad stacking, where multiple invisible ads load simultaneously to inflate . The economic scale of ad fraud is substantial, with 22% of global digital ad spend—$84 billion—lost to fraud in 2023, according to Juniper Research. Projections for 2024 estimate losses between $100 billion and $140 billion, driven by advanced botnets mimicking user patterns in mobile and video formats. Globally, IVT rates averaged 18.31% across 152 countries in 2024, with in search campaigns reaching 14-22% depending on industry and . Bots account for a significant portion, with one finding 7.82% of conversions invalid due to confirmed bot activity, amplifying costs as undetected bots repeat engagements. Common methods include , where bots automate repeated clicks on ads to exhaust budgets; impression fraud, generating fake views without engagement; and install or lead fraud, fabricating app downloads or form submissions via device farms or emulators. Emerging AI-powered bots enhance evasion by replicating human browsing variability, contributing to click fraud surges in 2025. These tactics not only drain direct spend but skew attribution data, leading to misguided optimizations and inflated ROI claims, as IVT masquerades as legitimate engagement. Mitigation relies on pre-bid detection tools, which reduce sophisticated exposure by up to 15 times compared to unprotected , though gaps persist—evidenced by brands inadvertently serving ads to known bots despite safeguards. Industry standards from bodies like the classify IVT into general (e.g., non-browser traffic) and sophisticated variants, but enforcement varies, with platforms like deploying AI to block millions of fraudulent accounts annually. Despite progress, the opacity of supply chains in programmatic ecosystems sustains the issue, as fraudsters exploit unverified publishers and scale attacks via botnets.

Viewability and measurement inaccuracies

Viewability in online advertising refers to the metric assessing whether an ad impression is actually visible to a user, defined by the Media Rating Council (MRC) and (IAB) standards as requiring at least 50% of the ad's pixels to be on-screen within the of an active browser tab or app for a minimum of one continuous second for display ads or two seconds for video ads. These criteria aim to distinguish served impressions from those likely perceived, but implementation varies across devices, formats, and vendors, contributing to inconsistencies. Empirical data reveals persistent gaps in viewability rates, with global averages reaching 76.1% in the second half of 2023, implying that approximately 24% of paid impressions failed to meet visibility thresholds and thus may not have been seen. , mobile app display ads achieved the highest rates among formats during the same period, yet overall figures underscore that billions in ad spend annually target non-viewable placements, such as ads loaded below or on inactive tabs. Low viewability correlates with wasted budgets, as advertisers often compensate based on total served impressions rather than verified views, inflating perceived reach without corresponding exposure. Measurement inaccuracies exacerbate these issues, stemming from technical limitations, vendor discrepancies, and integration with ad fraud. For instance, non-human traffic like bots generates invalid impressions that mimic but evade basic viewability checks, skewing metrics and leading to overreported performance. Studies indicate that up to 49% of data used for ad targeting and is inaccurate, undermining attribution models that rely on impression logs for causality in conversions. further compounds errors, as impressions served on one platform may not align with user sessions on another, resulting in fragmented or duplicated counts without standardized deduplication. Critics argue that even compliant viewable impressions overestimate true engagement, as the 50% visibility threshold for one second does not guarantee cognitive or , particularly amid page scrolling or multitasking. Vendor-specific methodologies introduce variability; for example, differences in pixel tracking or execution can yield divergent viewability scores for identical campaigns. Emerging attention metrics, standardized by IAB and in 2025, seek to incorporate dwell time and interaction signals to refine beyond binary viewability, but adoption remains uneven, and reliance on self-reported vendor data persists as a risk. These inaccuracies hinder causal assessment of ad efficacy, prompting calls for probabilistic modeling over deterministic tracking to better isolate genuine impact from noise.

User resistance via blockers and fatigue

Ad blockers, software tools that prevent the display of online advertisements, have proliferated as a primary form of user resistance to digital advertising. As of the second quarter of 2023, approximately 912 million worldwide utilized ad blockers, encompassing desktop plugins, browsers, and mobile applications, with projections indicating growth toward 1.1 billion users by subsequent years. Globally, 31.5% of internet users employed ad blockers at least occasionally in the first quarter of 2024, driven by factors such as intrusive formats like autoplay videos and pop-ups, which disrupt without user consent. In the United States, 32.2% of adults reported using ad blockers in 2024, with higher adoption on desktops (37%) compared to mobile devices (15%), reflecting preferences for uninterrupted content consumption. This adoption imposes substantial economic costs on the ecosystem, with publishers facing an estimated $54 billion in lost revenue during , representing about 8% of total global digital ad expenditure. Ad blockers not only suppress ad impressions but also correlate with reduced online, totaling $14.2 billion annually in diminished purchases, as users encounter fewer promotional prompts and gravitate toward familiar brands without external influence. Empirical analyses further reveal that ad-blocked environments decrease site visits and search queries, amplifying revenue shortfalls for content providers reliant on ad subsidies. Complementing technological circumvention, ad emerges as a psychological and behavioral response to overload, where repeated exposures erode user engagement and foster avoidance. defines creative fatigue as the decline in ad performance from users viewing identical creatives multiple times, leading to lower click-through rates and diminished recall; for instance, platforms observe optimal effectiveness within 3-5 exposures before saturation sets in. Branded content on exacerbates this, with intrusiveness and irrelevance driving fatigue that mediates reduced platform usage and ad interactions, as evidenced in studies linking ad density to heightened user irritation and disengagement. Systematic reviews of digital ad avoidance highlight causal factors including perceived invasions and content overload, prompting tactics like scrolling past ads or platform abandonment, independent of blocker usage. User motivations for resistance underscore causal links to advertising practices: surveys attribute blocking primarily to annoyance from non-consensual interruptions (e.g., 42% of users cite pop-ups), followed by bandwidth consumption and tracking concerns, rather than blanket opposition to commerce. Fatigue compounds these effects, with models showing that unchecked ad frequency prompts proportional expenditure reductions by advertisers to mitigate waning returns, as sustained exposure yields negative marginal impacts on attention and purchase intent. Collectively, these dynamics reveal user prioritization of experiential utility over subsidized content, challenging the sustainability of volume-based ad models without adaptations for relevance and restraint.

Privacy, Ethical, and Societal Concerns

Surveillance capitalism critiques and data misuse

The concept of surveillance capitalism, as articulated by emerita professor in her 2019 book , posits that dominant online advertising platforms such as and Meta extract vast quantities of personal behavioral data to create predictive models of user actions, commodifying this "behavioral surplus" for profit through targeted advertisements. Zuboff argues that this process, originating from 's early 2000s innovations in ad auctions and behavioral targeting, has evolved into a system where private human experiences are unilaterally claimed and rendered into data assets without user consent, enabling platforms to not only predict but also shape behavior via personalized nudges and incentives embedded in ads. Critics contend that these practices erode individual by fostering a of manipulation, where advertisers bid on real-time user profiles derived from , location data, and inferred preferences, often via third-party and pixels deployed on websites. Zuboff describes this as an "assault on human ," linking it to broader societal risks like democratic interference, as seen in how micro-targeted political ads exploit psychological vulnerabilities to influence and opinions. Empirical assessments of harms remain limited; a 2023 study surveying 420 online behavioral advertising (OBA) users identified self-reported issues including psychological distress from incessant , perceived loss of due to inescapable targeting, behavioral (e.g., avoiding certain sites to evade ads), and algorithmic marginalization for non-conforming profiles. Data misuse in online advertising has manifested in high-profile breaches and unauthorized applications, exemplified by the 2018 Cambridge Analytica scandal, where data from up to 87 million Facebook users—harvested via a personality quiz app—was repurposed to fuel psychographically targeted political ads during the 2016 U.S. presidential election and Brexit referendum, bypassing platform consent protocols. More recent incidents include malvertising campaigns, where malicious ads on legitimate sites deliver spyware to extract financial data, with over 1.7 billion malvertising impressions detected globally in 2022 alone, often exploiting ad networks' opaque supply chains. Regulatory filings reveal persistent issues, such as Meta's 2023 admission of over-sharing user data with ad partners in violation of consent rules, prompting multimillion-dollar fines under frameworks like the EU's GDPR. These cases underscore causal links between lax data governance in ad tech and tangible harms, including identity theft and electoral distortion, though proponents argue that aggregated, anonymized data yields net economic benefits outweighing isolated abuses.

Disinformation amplification and dark patterns

![An illustration of a "made-for-advertising" website from the book "Market-Oriented Disinformation Research" p.139p.139](./assets/Made_for_Advertising_MFAMFA
Online advertising amplifies by creating economic incentives for to prioritize sensational, low-quality content that generates high click-through rates, often including misleading or false narratives to maximize ad . Programmatic advertising systems, which automate ad placements based on , frequently direct brand advertisements to such "made-for-advertising" (MFA) sites without advertisers' explicit intent, as these platforms exploit algorithmic preferences for engagement over content veracity. A 2024 study analyzing over 3,000 outlets found that advertising financed 70% of their operations, with major brands appearing on these sites despite internal policies against it, due to opaque supply chains in ad tech. In the United States alone, advertisers spent an estimated $1.62 billion on ads hosted by websites between 2019 and 2023, underscoring the scale of inadvertent funding.
This amplification occurs through causal mechanisms where ad revenue models reward virality: MFA sites produce headlines and recycled falsehoods to lure traffic, which in turn boosts visibility via recommendation algorithms on platforms like search engines and , embedding deeper into user feeds. Empirical data from network analysis of cascades reveals that ad-monetized content spreads 6-10 times faster than non-monetized equivalents, as creators iterate on proven tactics without regard for factual accuracy. Advertisers face backlash effects, with experiments showing that exposure to ads alongside erodes trust by up to 15%, as consumers associate legitimate products with deceptive contexts. While platforms have implemented safety tools, their efficacy remains limited, as evidenced by persistent ad placements on 67% of surveyed domains from 2019-2021. Dark patterns in online advertising refer to interface designs that subvert user autonomy to extract consents, data, or engagements benefiting advertisers, such as masquerading promotional content as neutral recommendations or burying options in convoluted flows. A 2022 U.S. analysis of 153 companies identified sophisticated dark patterns like "confirmshaming" (guilt-inducing unsubscribe prompts) and disguised ads mimicking editorial content, which tricked users into prolonged exposure to targeted promotions. In settings, patterns including timers and low-stock alerts have been shown to increase purchase intentions by 20-30% through psychological , independent of actual status. These tactics extend to ad , where consent banners employ misdirection—e.g., defaulting to data-sharing while obscuring rejection paths—to fuel surveillance-based targeting, raising consent validity concerns under frameworks like GDPR. Studies across web and mobile modalities confirm dark patterns reduce cancellation rates by 40%, sustaining ad revenue streams at the expense of informed choice. The interplay between amplification and s manifests in ad ecosystems where deceptive designs on MFA sites encourage shares or dwells, further propagating false content while harvesting user data for refined targeting of susceptible audiences. For instance, nagging prompts and offers on misinformation-laden pages have been linked to higher retention of erroneous beliefs, as users are nudged toward deeper interaction without transparent disclosure. Empirical interventions, such as simplified opt-outs, reduce efficacy by 25%, suggesting that regulatory scrutiny could mitigate both issues without broadly curtailing ad efficiency. However, economic incentives persist, as platforms derive 80-90% of revenue from , often prioritizing scale over rigorous content or design vetting. Targeted advertising enhances efficiency by leveraging user data to deliver relevant content, yielding higher click-through rates and compared to non-personalized approaches; for instance, behavioral targeting can increase ad relevance, thereby reducing wasted impressions and advertiser costs. However, regulations such as the EU's (GDPR), effective May 25, 2018, mandate explicit, for processing personal data used in targeting, introducing friction that limits data availability and precision. This requirement stems from privacy protections but compels advertisers to balance granular targeting—dependent on cross-site tracking and profiling—with user opt-in mechanisms, often resulting in incomplete datasets for non-consenting users. Empirical analyses of GDPR's implementation reveal modest declines in ad performance metrics, including bid prices and publisher revenues, attributable to curtailed targeting capabilities; one study estimated these effects at around 5-10% reductions in display ad efficiency shortly after enforcement. banner designs influence opt-in rates, with optimized interfaces achieving 72-82% acceptance in some European markets, allowing partial preservation of targeting value, while poorly implemented prompts yield rates as low as 39%, exacerbating efficiency losses. Restrictions on third-party data further shift reliance to first-party or contextual alternatives, which maintain basic without personal profiling but deliver lower ROI, as evidenced by reduced consumer engagement and satisfaction in privacy-enhanced environments. Prohibitions on , as tested in empirical settings like policies, demonstrate significant trade-offs, including up to 60% drops in user retention and feature updates due to diminished ad revenues funding development. From a causal perspective, mechanisms mitigate risks but can inadvertently increase overall ad volumes to compensate for imprecision, heightening user fatigue without proportional gains. Industry adaptations, such as transparent privacy notices paired with , foster trust and sustain moderate targeting benefits, though long-term evidence indicates that overly stringent erodes the subsidy model underpinning free digital services.

Regulation and Policy Responses

Major frameworks (GDPR, CCPA, DSA)

The General Data Protection Regulation (GDPR), enacted by the on May 25, 2018, imposes stringent requirements on data processing for online , mandating explicit, for tracking technologies like and prohibiting processing for profiling or without a lawful basis, such as legitimate interest balanced against user rights. It extraterritorially applies to any entity targeting EU residents, compelling ad tech firms to overhaul behavioral targeting practices, with non-compliance penalties reaching 4% of global annual turnover or €20 million, whichever is higher. Enforcement has targeted ad-related violations, including Meta's €1.2 billion fine in December 2023 for unlawful data transfers used in personalized across EU-U.S. borders, and Criteo's €40 million penalty in 2023 for insufficient in retargeting. These measures have empirically reduced the prevalence of online trackers by up to 20-30% on EU-facing sites post-2018, shifting industry reliance toward consent management platforms and to avoid fines exceeding €2.1 billion in 2023 alone. The , effective January 1, 2020, and expanded by the in 2023, grants residents rights to of the "sale" or "sharing" of —broadly interpreted to include ad targeting data transfers—requiring businesses with over $25 million in revenue or handling data of 100,000+ consumers to provide clear mechanisms like "Do Not Sell My " links. Unlike GDPR's consent model, CCPA emphasizes rights without mandating prior approval for data collection, but it prohibits discrimination against opting-out users, such as charging higher prices for ad-free experiences. Enforcement by the California Privacy Protection Agency has intensified, with a $1.35 million penalty approved in October 2025 against a firm for failing to honor in ad personalization, alongside scrutiny of website functionality and third-party data flows. This has prompted ad platforms to integrate Global Privacy Control (GPC) signals for automated , reducing targeted ad revenue for non-compliant publishers by an estimated 10-15% in affected markets. The 's (DSA), adopted in October 2022 and fully applicable from February 17, 2024, for very large online platforms (VLOPs) handling over 45 million EU users, complements GDPR by requiring transparency in practices, including disclosure of ad sponsors, targeting parameters, and algorithmic recommendations to prevent manipulative practices. Platforms must conduct annual systemic risk assessments for ad-driven harms like amplification and ban targeted ads based on sensitive or directed at minors under 18, with general intermediaries facing lighter obligations but all subject to fines up to 6% of global turnover. Early targeted VLOPs, with the initiating proceedings against X (formerly Twitter) in December 2024 for incomplete ad transparency reporting and risk mitigation, potentially leading to the DSA's first penalties in 2025. These rules have accelerated demands for ad libraries and verifiable impression reporting, though critics note lags behind GDPR's, with total DSA fines projected to escalate as national regulators align by mid-2025. Collectively, GDPR's consent barriers, CCPA's opt-out mandates, and DSA's platform accountability have curtailed cross-border data flows essential for behavioral advertising, with studies showing a 15-25% drop in ad personalization effectiveness across regulated jurisdictions, spurring investments in like while raising compliance costs estimated at 1-2% of ad revenue for large firms. However, extraterritorial reach has unevenly burdened smaller advertisers, prompting industry adaptations such as cookieless targeting via first-party data, amid debates over whether these frameworks prioritize user autonomy or inadvertently favor incumbents with resources to navigate fragmented enforcement. In the early , enforcement of regulations intensified against online practices, with European data protection authorities issuing substantial fines under the GDPR for ad tech firms' handling of user and . For instance, France's CNIL imposed a €40 million penalty on in June 2023 for processing without valid in , marking a key case in scrutinizing behavioral advertising chains. Broader GDPR fines in 2025 included €530 million against and €200 million against , often tied to violations in data transfers and profiling for ads, reflecting regulators' focus on cross-border data flows in ad ecosystems. In the US, the Privacy Protection Agency (CPPA) ramped up CCPA enforcement, settling cases emphasizing opt-out mechanisms and ad tech integrations; faced a $1.55 million fine in July 2025 for failing to honor opt-outs from and sharing sensitive with third parties without proper disclosures. Tractor Supply was fined $1.35 million in October 2025 for inadequate opt-outs from third-party trackers, underscoring scrutiny on pixel-based advertising tools. The EU's (DSA), fully enforceable from February 2024, introduced obligations for platforms to mitigate risks in online advertising, including bans on profiling minors and using sensitive data attributes like for ad targeting. Early DSA actions targeted very large online platforms (VLOPs), with the probing non-compliance in ad transparency reporting by January 2025, though fines remained pending as codes of conduct for advertising self-regulation were still developing. In the , expanding state privacy laws—reaching 17 comprehensive frameworks by 2025—amplified CCPA-like requirements, prompting multistate actions against weak tools in ad delivery. Overall, enforcement trended toward granular audits of banners, vendor contracts, and tracking technologies, with total GDPR fines exceeding €4 billion by mid-2025, disproportionately affecting ad-dependent tech firms. Industry responses emphasized transitioning to privacy-respecting alternatives amid third-party cookie deprecation, accelerated by regulatory pressures. Google advanced its initiative, rolling out tools like Protected Audience API by 2024 to enable cohort-based targeting without individual identifiers, though adoption lagged due to concerns. Advertisers shifted to first-party and zero-party strategies, collecting consented preferences directly from users via quizzes or loyalty programs to sustain . gained traction, leveraging page content and AI for relevance without cross-site tracking, as seen in platforms optimizing for signal loss estimated at 20-30% from cookie phase-outs. Server-side tagging and clean rooms emerged for secure collaboration, reducing reliance on client-side scripts vulnerable to blockers and audits. By 2025, trade groups like IAB promoted standards for cookieless measurement, while firms invested in AI-driven predictive modeling to offset efficiency losses, though critics noted persistent challenges in attribution accuracy. These adaptations prioritized compliance to avert fines, yet raised questions about reduced targeting precision impacting small publishers' revenues.

Economic costs of over-regulation

Over-regulation in online advertising, particularly through frameworks like the GDPR and DSA in , imposes substantial compliance burdens on platforms and advertisers, often exceeding initial estimates and diverting resources from to legal adherence. Compliance costs for GDPR alone have reached over $1 million for one in ten businesses surveyed, encompassing legal fees, upgrades for consent management, and operational disruptions from audits. In the , the DSA's transparency and requirements for ad platforms add layered obligations on top of GDPR, estimated to contribute to $14.8 billion in lost online advertisement revenues for U.S. companies in 2024, reflecting broader inefficiencies in cross-border flows and targeting restrictions. Empirical studies document direct revenue declines from curtailed personalized advertising, a core driver of digital ad efficiency. Following GDPR implementation in May 2018, display ad revenue per click fell by 5% on average, with publishers experiencing moderate but persistent drops in overall ad performance due to reduced availability for targeting. Similarly, privacy laws like CCPA, effective from January 2020, have forced advertisers to limit sales and opt-out mechanisms, diminishing targeting precision and contributing to unintended , as evidenced by a 17% increase in vendor consolidation among websites one week post-GDPR, where smaller ad tech firms were sidelined by compliance barriers. These effects cascade to higher effective costs per impression, as contextual alternatives prove less efficient than data-driven models. Broader economic repercussions include reduced competitiveness for small and medium-sized (SMBs), which rely disproportionately on cost-effective digital ads for . Personalized supports €526 billion in annual economic value and 6 million jobs, yet restrictions under DSA and GDPR risk eroding this by inflating ad expenses and limiting reach, particularly for SMBs lacking resources for compliant alternatives. Analyses indicate that curbing personalized ads could drastically alter models, raising operational costs and potentially increasing prices through diminished ad-subsidized free services. While proponents cite gains, from regulatory impact assessments reveal these interventions often amplify inequalities, favoring large incumbents with compliance scale over innovative entrants.

Future Directions

AI integration and predictive advertising

Artificial intelligence integration in online advertising primarily occurs through algorithms applied to programmatic platforms, where AI automates ad auctions and optimizes targeting in real-time. These systems process vast datasets on user behavior, device usage, and contextual signals to execute bids milliseconds before an ad impression, enhancing over manual processes. By 2025, AI-driven programmatic advertising incorporates generative models for dynamic ad creative generation, adapting visuals and copy based on predicted user preferences. Predictive advertising leverages AI to forecast consumer actions, such as purchase likelihood or churn risk, by analyzing historical data patterns via techniques. For instance, models in platforms like those from StackAdapt use to segment audiences by anticipated response rates, allocating budgets toward high-conversion prospects and reducing waste on low-engagement segments. A 2024 study demonstrated that approaches in programmatic ecosystems improved ad targeting accuracy by modeling user trajectories, achieving up to 20-30% lifts in click-through rates compared to rule-based methods. In practice, companies like deploy AI for predictive insights in , where algorithms simulate campaign outcomes to refine strategies pre-launch, minimizing trial-and-error costs. statistics indicate that 78% of businesses incorporated AI into functions by Q3 2024, with predictive tools cited for enabling hyper-personalized campaigns that anticipate needs rather than react to them. However, reliance on third-party data for these predictions has prompted shifts toward first-party signals amid regulations, though AI's causal inference capabilities continue to drive causal attribution models for ad effectiveness. This evolution underscores AI's role in causal realism for ad performance, prioritizing empirically validated predictions over correlative assumptions. Emerging trends encompass autonomous media buying, where AI agents manage full campaign cycles—including planning, bidding, optimization, and measurement—with minimal human intervention, leveraging agentic systems for real-time decision-making. Privacy-focused AI targeting integrates contextual analysis and federated learning to deliver relevant ads without accessing personal identifiers, aligning with consent-based ecosystems and reducing reliance on deprecated tracking methods. Conversely, the proliferation of AI agents in search and personal assistance introduces disruptions, as these entities often resolve user queries directly without engaging ads, diminishing traditional click-through metrics and prompting reevaluation of performance indicators beyond impressions.

Privacy tech like zero-party data

Zero-party data refers to information that consumers intentionally and proactively share with brands, such as preferences, purchase intentions, or survey responses, distinguishing it from passively collected first- or third-party data. This approach emerged as a response to regulations and the deprecation of third-party cookies, enabling advertisers to personalize campaigns without relying on cross-site tracking. In online advertising, brands collect zero-party data through mechanisms like interactive quizzes on websites, preference centers in apps, or opt-ins, where users explicitly to sharing details in exchange for tailored recommendations or discounts. The effectiveness of zero-party data lies in its alignment with consent-based models, potentially improving ad relevance and conversion rates by revealing user motivations directly, unlike inferred data from behavioral tracking. For instance, a 2023 EY analysis highlighted its role in enhancing customer experiences while respecting rights, as it avoids the ethical pitfalls of . Adoption has grown amid regulatory pressures; a 2025 survey indicated that 55% of marketers anticipate zero-party data becoming more critical over the subsequent two years, driven by needs for compliant post-GDPR and CCPA enforcement. However, challenges include issues, as collecting sufficient volumes requires ongoing user engagement incentives, and data quality can suffer from self-reported inaccuracies or reluctance to share beyond surface-level preferences. Related complement zero-party data by minimizing data exposure in advertising ecosystems. Privacy-preserving techniques, such as in data clean rooms, allow advertisers to match audiences without revealing raw , as outlined in a 2025 Network Advertising Initiative primer on PETs for digital ads. , which targets based on page content rather than user profiles, further reduces reliance on while maintaining relevance, though it may yield lower precision than consented granular insights. Despite these advances, critics note that zero-party and similar tech do not fully replicate the scale of legacy tracking, potentially increasing acquisition costs for advertisers by 20-30% in transition phases, based on industry estimates from privacy-focused analyses. Overall, these tools represent a shift toward user-centric models, but their long-term viability depends on technological integration and consumer trust, with low baseline trust levels—74% of consumers skeptical of brands per data—posing ongoing hurdles.

Decentralized and blockchain-based alternatives

Decentralized alternatives to centralized online advertising employ technology to facilitate ad transactions, verifiable impressions, and fraud-resistant mechanisms without relying on data aggregators or tracking cookies. These systems distribute control across networks of nodes, enabling advertisers, publishers, and users to interact directly via smart contracts that automate bidding, payment, and verification processes. By recording ad data on immutable ledgers, reduces intermediaries' fees, which can account for up to 30-50% of ad spend in traditional models, and minimizes risks like invalid traffic from bots, estimated at 20-40% of digital ad impressions globally. A leading implementation is the ecosystem within the Brave browser, introduced in 2017 to tokenize user attention and redistribute ad revenue. Users voluntarily opt into viewing non-tracking ads, earning 70% of the revenue in cryptocurrency, while publishers receive payments proportional to verified engagement time rather than inferred demographics. This privacy-centric model avoids cross-site profiling, with Brave reporting over 50 million monthly active users by 2023, though BAT's market utility depends on token liquidity and network effects for sustained adoption. Platforms like AdEx provide blockchain-based programmatic ad exchanges using the ADX token for transparent auctions and settlement. Launched as an protocol, AdEx enables advertisers to target Web3-native audiences—such as crypto wallet holders—through decentralized identifiers, bypassing centralized brokers and incorporating proofs via on-chain validation of views and clicks. Its supports direct publisher-advertiser deals, potentially cutting costs by eliminating opaque supply chains, though scalability constraints on layer-1 blockchains have prompted integrations with layer-2 solutions for faster transactions. Broader applications target ad through features like decentralized identity verification and timestamped proofs of delivery, preventing tactics such as domain spoofing where fraudulent sites mimic legitimate . For example, unique on-chain identifiers for publishers ensure ad placements occur on authenticated domains, with pilots demonstrating up to 90% reductions in disputed impressions compared to legacy systems. Despite these advantages, challenges persist, including high gas fees during peak network usage and limited , limiting penetration to niche sectors like crypto marketing as of . Empirical evaluations indicate potential annual savings of $5-10 billion in mitigation if scaled, but real-world deployment remains experimental amid regulatory scrutiny of tokens.

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