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Targeted advertising
Targeted advertising
from Wikipedia
Example of targeting in an online ad system

Targeted[1] advertising or data-driven marketing is a form of advertising, including online advertising, that is directed towards an audience with certain traits, based on the product or person the advertiser is promoting.[2]

These traits can either be demographic with a focus on race, economic status, sex, age, generation, level of education, income level, and employment, or psychographic focused on the consumer values, personality, attitude, opinion, lifestyle, and interests.[1] This focus can also entail behavioral variables, such as browser history, purchase history, and other recent online activities. The process of algorithm targeting eliminates waste.[3]

Traditional forms of advertising, including billboards, newspapers, magazines, and radio channels, are progressively becoming replaced by online advertisements.[4]

Through the emergence of new online channels, the usefulness of targeted advertising is increasing because companies aim to minimize wasted advertising.[4] Most targeted new media advertising currently uses second-order proxies for targets, such as tracking online or mobile web activities of consumers, associating historical web page consumer demographics with new consumer web page access, using a search word as the basis of implied interest, or contextual advertising.[5]

Types

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Companies have technology that allows them to gather information about web users.[4] By tracking and monitoring what websites users visit, internet service providers can directly show ads that are relative to the consumer's preferences. Most of today's websites are using these targeting technologies to track users' internet behavior and there is much debate over the privacy issues present.[6]

Search engine marketing

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Search engine marketing uses search engines to reach target audiences. For example, Google's Remarketing Campaigns are a type of targeted marketing where advertisers use the IP addresses of computers that have visited their websites to remarket their ad specifically to users who have previously been on their website whilst they browse websites that are a part of the Google display network, or when searching for keywords related to a product or service on the Google search engine.[7] Dynamic remarketing can improve targeted advertising as the ads can include the products or services that the consumers have previously viewed on the advertisers' websites within the ads.[8]

Google Ads includes different platforms. The Search Network displays the ads on 'Google Search, other Google sites such as Maps and Shopping, and hundreds of non-Google search partner websites that show ads matched to search results'.[8] 'The Display Network includes a collection of Google websites (like Google Finance, Gmail, Blogger, and YouTube), partner sites, and mobile sites and apps that show adverts from Google Ads matched to the content on a given page.'[8]

These two kinds of advertising networks can be beneficial for each specific goal of the company, or type of company. For example, the search network can benefit a company to reach consumers actively searching for a particular product or service.

Other ways advertising campaigns can target the user is to use browser history and search history. For example, if the user types promotional pens into a search engine such as Google, ads for promotional pens will appear at the top of the page above the organic listings. These ads will be geo-targeted to the area of the user's IP address, showing the product or service in the local area or surrounding regions. The higher ad position is often rewarded to the ad having a higher quality score.[9] The ad quality is affected by the 5 components of the quality score:[10]

When ranked based on these criteria, it will affect the advertiser by improving ad auction eligibility, the actual cost per click (CPC), ad position, and ad position bid estimates; to summarise, the better the quality score, the better ad position, and lower costs.

Google uses its display network to track what users are looking at and to gather information about them. When a user goes to a website that uses the Google display network, it will send a cookie to Google, showing information on the user, what they have searched, where they are from, found by the IP address, and then builds a profile around them, allowing Google to easily target ads to the user more specifically.

For example, if a user goes onto promotional companies' websites often, that sell promotional pens, Google will gather data from the user such as age, gender, location, and other demographic information as well as information on the websites visited, the user will then be put into a category of promotional products, allowing Google to easily display ads on websites the user visits relating to promotional products.[11]

Social media targeting

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Social media targeting is a form of targeted advertising, that uses general targeting attributes such as geotargeting, behavioral targeting, and socio-psychographic targeting, and gathers the information that consumers have provided on each social media platform.

According to the media users' view history, customers who are interested in the criteria will be automatically targeted by the advertisements of certain products or services.[12] For example, Facebook collects massive amounts of user data from surveillance infrastructure on its platforms.[13] Information such as a user's likes, view history, and geographic location is leveraged to micro-target consumers with personalized products.

Paid advertising on Facebook works by helping businesses to reach potential customers by creating targeted campaigns.[14]

Social media also creates profiles of the consumer and only needs to look at one place, the user's profile, to find all interests and 'likes'.

E.g. Facebook lets advertisers target using broad characteristics like gender, age, and location. Furthermore, they allow more narrow targeting based on demographics, behavior, and interests (see a comprehensive list of Facebook's different types of targeting options[15]).

Television

[edit]

Advertisements can be targeted to specific consumers watching digital cable,[16] Smart TVs, or over-the-top video.[17] Targeting can be done according to age, gender, location, or personal interests in films, etc.[18]

Cable box addresses can be cross-referenced with information from data brokers like Acxiom, Equifax, and Experian, including information about marriage, education, criminal record, and credit history. Political campaigns may also match against public records such as party affiliation and which elections and party primaries the view has voted in.[17]

Mobile devices

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Since the early 2000s, advertising has been pervasive online and more recently in the mobile setting. Targeted advertising based on mobile devices allows more information about the consumer to be transmitted, not just their interests, but their information about their location and time.[19] This allows advertisers to produce advertisements that could cater to their schedule and a more specific changing environment.

Content and contextual targeting

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The most straightforward method of targeting is content/contextual targeting. This is when advertisers put ads in a specific place, based on the relative content present.[6] Another name used is content-oriented advertising, as it corresponds to the context being consumed.

This targeting method can be used across different mediums, for example in an article online, purchasing homes would have an advert associated with this context, like an insurance ad. This is usually achieved through an ad matching system that analyses the contents on a page or finds keywords and presents a relevant advert, sometimes through pop-ups.[20]

Sometimes the ad matching system can fail, as it can neglect to tell the difference between positive and negative correlations. This can result in placing contradictory adverts, which are not appropriate to the content.[20]

Technical targeting

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Technical targeting is associated with the user's own software or hardware status. The advertisement is altered depending on the user's available network bandwidth, for example, if a user is on a mobile phone that has a limited connection, the ad delivery system will display a version of the ad that is smaller for a faster data transfer rate.[6]

Addressable advertising systems serve ads directly based on demographic, psychographic, or behavioral attributes associated with the consumer(s) exposed to the ad. These systems are always digital and must be addressable in that the endpoint that serves the ad (set-top box, website, or digital sign) must be capable of rendering an ad independently of any other endpoints based on consumer attributes specific to that endpoint at the time the ad is served.

Addressable advertising systems, therefore, must use consumer traits associated with the endpoints as the basis for selecting and serving ads.[21]

Time Targeting

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According to the Journal of Marketing, more than 1.8 billion clients spent a minimum of 118 minutes daily- via web-based networking media in 2016.[22] Nearly 77% of these clients interact with the content through likes, commenting, and clicking on links related to content. With this astounding buyer trend, advertisers need to choose the right time to schedule content, to maximize advertising efficiency.

To determine what time of day is most effective for scheduling content, it is essential to know when the brain is most effective at retaining memory. Research in chronopsychology has credited that time-of-day impacts diurnal variety in a person's working memory accessibility and has discovered the enactment of inhibitory procedures to build working memory effectiveness during times of low working memory accessibility. Working memory is known to be vital for language perception, learning, and reasoning[23][24] providing us with the capacity of putting away, recovering, and preparing quick data.

For many people, working memory accessibility is good when they get up toward the beginning of the day, most reduced in mid-evening, and moderate at night.[25]

Sociodemographic targeting

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Sociodemographic targeting focuses on the characteristics of consumers. This includes their age, generation, gender, salary, and nationality.[6] The idea is to target users specifically and to use this collected data, for example, targeting a male in the age bracket of 18–24. Facebook and other social media platforms use this form of targeting by showing advertisements relevant to the user's demographic on their account, this can show up in the forms of banner ads, mobile ads, or commercial videos.[26]

Geographical and location-based targeting

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This type of advertising involves targeting different users based on their geographic location. IP addresses can signal the location of a user and can usually transfer the location through ZIP codes.[6] Locations are then stored for users in static profiles, thus advertisers can easily target these individuals based on their geographic location.

A location-based service (LBS) is a mobile information service that allows spatial and temporal data transmission and can be used to an advertiser's advantage.[27] This data can be harnessed from applications on the device (mobile apps like Uber) that allow access to the location information.[28]

This type of targeted advertising focuses on localizing content, for example, a user could be prompted with options of activities in the area, for example, places to eat, nearby shops, etc. Although producing advertising off consumer location-based services can improve the effectiveness of delivering ads, it can raise issues with the user's privacy.[29]

Behavioral targeting

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Behavioral targeting is centered around the activity/actions of users and is more easily achieved on web pages.[30][31] Information from browsing websites can be collected from data mining, which finds patterns in users' search history. Advertisers using this method believe it produces ads that will be more relevant to users, thus leading consumers to be more likely influenced by them.[32]

If a consumer was frequently searching for plane ticket prices, the targeting system would recognize this and start showing related adverts across unrelated websites, such as airfare deals on Facebook. Its advantage is that it can target individual interests, rather than target groups of people whose interests may vary.[6]

When a consumer visits a website, the pages they visit, the amount of time they view each page, the links they click on, the searches they make, and the things that they interact with, allow sites to collect that data, and other factors, to create a 'profile' that links to that visitor's web browser. As a result, site publishers can use this data to create defined audience segments based on visitors who have similar profiles.

When visitors return to a specific site or a network of sites using the same web browser, those profiles can be used to allow marketers and advertisers to position their online ads and messaging in front of those visitors who exhibit a greater level of interest and intent for the products and services being offered.

Behavioral targeting has emerged as one of the main technologies used to increase the efficiency and profits of digital marketing and advertisements, as media providers can provide individual users with highly relevant advertisements. On the theory that properly targeted ads and messaging will fetch more consumer interest, publishers can charge a premium for behaviorally targeted ads and marketers can achieve.

Behavioral marketing can be used on its own or in conjunction with other forms of targeting.[15] Many practitioners also refer to this process as "audience targeting".[33]

While behavioral targeting can enhance ad effectiveness, it also raises privacy concerns.[34] Users may feel uncomfortable with the idea of their online behavior being tracked and used for advertising purposes. Striking a balance between personalization and privacy is crucial.[35]

Onsite

[edit]

Behavioral targeting may also be applied to any online property on the premise that it either improves the visitor experience or benefits the online property, typically through increased conversion rates or increased spending levels. The early adopters of this technology/philosophy were editorial sites such as HotWired,[36][37] online advertising[38] with leading online ad servers,[39] retail or another e-commerce website as a technique for increasing the relevance of product offers and promotions on a visitor by visitor basis. More recently, companies outside this traditional e-commerce marketplace have started to experiment with these emerging technologies.

The typical approach to this starts by using web analytics or behavioral analytics to breakdown the range of all visitors into several discrete channels. Each channel is then analyzed and a virtual profile is created to deal with each channel.

These profiles can be based around Personas that gives the website operators a starting point in terms of deciding what content, navigation, and layout to show to each of the different personas. When it comes to the practical problem of successfully delivering the profiles correctly this is usually achieved by either using a specialist content behavioral platform or by bespoke software development.

Most platforms identify visitors by assigning a unique ID cookie to every visitor to the site thereby allowing them to be tracked throughout their web journey, the platform then makes a rules-based decision about what content to serve.

Self-learning onsite behavioral targeting systems will monitor visitor response to site content and learn what is most likely to generate a desired conversion event. Some good content for each behavioral trait or pattern is often established using numerous simultaneous multivariate tests. Onsite behavioral targeting requires a relatively high level of traffic before statistical confidence levels can be reached regarding the probability of a particular offer generating a conversion from a user with a set behavioral profile. Some providers have been able to do so by leveraging their large user base, such as Yahoo!. Some providers use a rules-based approach, allowing administrators to set the content and offers shown to those with particular traits.

According to research behavioral targeting provides little benefit at a huge privacy cost — when targeting for gender, the targeted guess is 42% accurate, which is less than a random guess. When targeting for gender and age the accuracy is 24%.[40]

Network

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Advertising networks use behavioral targeting in a different way than individual sites. Since they serve many advertisements across many different sites, they can build up a picture of the likely demographic makeup of internet users.[41] Data from a visit to one website can be sent to many different companies, including Microsoft and Google subsidiaries, Facebook, Yahoo, many traffic-logging sites, and smaller ad firms.[42]

This data can sometimes be sent to more than 100 websites and shared with business partners, advertisers, and other third parties for business purposes. The data is collected using cookies, web beacons and similar technologies, and/or a third-party ad serving software, to automatically collect information about site users and site activity. Some servers even record the page that referred you to them, the websites you visit after them, which ads you see, and which ads you click on.[43]

Online advertising uses cookies, a tool used specifically to identify users, as a means of delivering targeted advertising by monitoring the actions of a user on the website. For this purpose, the cookies used are called tracking cookies. An ad network company such as Google uses cookies to deliver advertisements adjusted to the interests of the user, control the number of times that the user sees an ad, and "measure" whether they are advertising the specific product to the customer's preferences.[44]

This data is collected without attaching the people's names, addresses, email addresses, or telephone numbers, but it may include device identifying information such as the IP address, MAC address, web browser information, cookie, or other device-specific unique alphanumerical ID of your computer, but some stores may create guest IDs to go along with the data.

Cookies are used to control displayed ads and to track browsing activity and usage patterns on sites. This data is used by companies to infer people's age, gender, and possible purchase interests so that they can make customized ads that you would be more likely to click on.[45]

An example would be a user seen on football sites, business sites, and male fashion sites. A reasonable guess would be to assume the user is male. Demographic analyses of individual sites provided either internally (user surveys) or externally (Comscore\Netratings) allow the networks to sell audiences rather than sites.[46] Although advertising networks were used to sell this product, this was based on picking the sites where the audiences were. Behavioral targeting allows them to be slightly more specific about this.

Research on targeted advertising

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In the work titled An Economic Analysis of Online Advertising Using Behavioral Targeting,[31] Chen and Stallaert (2014) study the economic implications when an online publisher engages in behavioral targeting. They consider that the publisher auctions off an advertising slot and are paid on a cost-per-click basis. Chen and Stallaert (2014) identify the factors that affect the publisher's revenue, the advertisers' payoffs, and social welfare. They show that revenue for the online publisher in some circumstances can double when behavioral targeting is used.

Increased revenue for the publisher is not guaranteed: in some cases, the prices of advertising and hence the publisher's revenue can be lower, depending on the degree of competition and the advertisers' valuations. They identify two effects associated with behavioral targeting: a competitive effect and a propensity effect. The relative strength of the two effects determines whether the publisher's revenue is positively or negatively affected. Chen and Stallaert (2014) also demonstrate that, although social welfare is increased and small advertisers are better off under behavioral targeting, the dominant advertiser might be worse off and reluctant to switch from traditional advertising.

In 2006, BlueLithium (now Yahoo! Advertising) in a large online study, examined the effects of behavior-targeted advertisements based on contextual content. The study used 400 million "impressions", or advertisements conveyed across behavioral and contextual borders. Specifically, nine behavioral categories (such as "shoppers" or "travelers")with over 10 million "impressions" were observed for patterns across the content.[47]

All measures for the study were taken in terms of click-through rates (CTR) and "action-through rates" (ATR), or conversions. So, for every impression that someone gets, the number of times they "click-through" to it will contribute to CTR data, and every time they go through with or convert on the advertisement the user adds "action-through" data.

Results from the study show that advertisers looking for traffic on their advertisements should focus on behavioral targeting in context. Likewise, if they are looking for conversions on the advertisements, behavioral targeting out of context is the most effective process.[47] The data helped determine an "across-the-board rule of thumb";[47] however, results fluctuated widely by content categories. Overall results from the researchers indicate that the effectiveness of behavioral targeting is dependent on the goals of the advertiser and the primary target market the advertiser is trying to reach.

Process

[edit]

Through the use of analytic tools, marketers attempt to understand customer behavior and make informed decisions based on the data.[48] E-commerce retailers use data driven marketing to try and improve customer experience and increase sales. One example cited in the Harvard Business Review is Vineyard Vines, a fashion brand with brick-and-mortar stores and an online product catalog. The company has used an artificial intelligence (AI) platform to gain knowledge about its customers from actions taken or not taken on the e-commerce site. Email or social media communications are automatically triggered at certain points, such as cart abandonment. This information is also used to refine search engine marketing.[49]

Advertising provides advertisers with a direct line of communication with existing and prospective consumers. By using a combination of words and/or pictures the general aim of the advertisement is to act as a "medium of information" (David Ogilvy[50]) making the means of delivery and to whom the information is delivered most important. Advertising should define how and when structural elements of advertisements influence receivers, knowing that all receivers are not the same and thus may not respond in a single, similar manner.[51]

Targeted advertising serves the purpose of placing particular advertisements before specific groups to reach consumers who would be interested in the information. Advertisers aim to reach consumers as efficiently as possible with the belief that it will result in a more effective campaign. By targeting, advertisers can identify when and where the ad should be positioned to achieve maximum profits. This requires an understanding of how customers' minds work (see also neuromarketing) to determine the best channel by which to communicate.

Types of targeting include, but are not limited to advertising based on demographics, psychographics, behavioral variables, and contextual targeting.

Behavioral advertising is the most common form of targeting used online. Internet cookies are sent back and forth between an internet server and the browser, which allows a user to be identified or to track their progressions. Cookies provide details on what pages a consumer visits, the amount of time spent viewing each page, the links clicked on; and searches and interactions made.

From this information, the cookie issuer gathers an understanding of the user's browsing tendencies and interests generating a profile. By analyzing the profile, advertisers can create defined audience segments based upon users with similar returned information, hence profiles. Tailored advertising is then placed in front of the consumer based on what organizations working on behalf of the advertisers assume are the interests of the consumer.[52]

These advertisements have been formatted to appear on pages and in front of users that they would most likely appeal to based on their profiles. For example, under behavioral targeting, if a user is known to have recently visited several automotive shopping and comparison sites based on the data recorded by cookies stored on the user's computer, the user can then be served automotive-related advertisements when visiting other sites.[53]

Behavioral advertising is reliant on data both wittingly and unwittingly provided by users and is made up of two different forms: one involving the delivery of advertising based on an assessment of user's web movements; the second involving the examination of communication and information as it passes through the gateways of internet service providers.[citation needed]

Demographic targeting was the first and most basic form of targeting used online. involves segmenting an audience into more specific groups using parameters such as gender, age, ethnicity, annual income, parental status, etc. All members of the group share a common trait.

So, when an advertiser wishes to run a campaign aimed at a specific group of people then that campaign is intended only for the group that contains those traits at which the campaign is targeted. Having finalized the advertiser's demographic target, a website or a website section is chosen as a medium because a large proportion of the targeted audience utilizes that form of media.[citation needed]

Segmentation using psychographics Is based on an individual's personality, values, interests, and lifestyles. A recent study concerning what forms of media people use- conducted by the Entertainment Technology Center at the University of Southern California, the Hallmark Channel, and E-Poll Market Research- concludes that a better predictor of media usage is the user's lifestyle.

Researchers concluded that while cohorts of these groups may have similar demographic profiles, they may have different attitudes and media usage habits.[54] Psychographics can provide further insight by distinguishing an audience into specific groups by using their traits. Once acknowledging this is the case, advertisers can begin to target customers having recognized that factors other than age for example provide greater insight into the customer.

Contextual advertising is a strategy to place advertisements on media vehicles, such as specific websites or print magazines, whose themes are relevant to the promoted products.[55]: 2  Advertisers apply this strategy to narrow-target their audiences.[56][55] Advertisements are selected and served by automated systems based on the identity of the user and the displayed content of the media. The advertisements will be displayed across the user's different platforms and are chosen based on searches for keywords; appearing as either a web page or pop-up ads. It is a form of targeted advertising in which the content of an ad is in direct correlation to the content of the webpage the user is viewing.

Retargeting

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Retargeting is where advertisers use behavioral targeting to produce ads that follow users after users have looked at or purchased a particular item. An example of this is store catalogs, where stores subscribe customers to their email system after a purchase hoping that they draw attention to more items for continuous purchases.

The main example of retargeting that has earned a reputation from most people is ads that follow users across the web, showing them the same items that they have looked at in the hope that they will purchase them. Retargeting is a very effective process; by analyzing consumers activities with the brand they can address their consumers' behavior appropriately.[57]

The major psychographic segments

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Personality

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Every brand, service, or product has itself a personality, how it is viewed by the public and the community and marketers will create these personalities to match the personality traits of their target market.[1] Marketers and advertisers create these personalities because when consumers can relate to the characteristics of a brand, service, or product they are more likely to feel connected to the product and purchase it.[citation needed]

Lifestyle

[edit]

Advertisers are aware that different people lead different lives, have different lifestyles and different wants, and needs at different times in their consumer's lives, thus individual differences can be compensated for Advertisers who base their segmentation on psychographic characteristics promote their product as the solution to these wants and needs. Segmentation by lifestyle considers where the consumer is in their life cycle and which preferences are associated with that life stage.[citation needed]

Opinions, attitudes, interests, and hobbies

[edit]

Psychographic segmentation also includes opinions on religion, gender, politics, sporting and recreational activities, views on the environment, and arts and cultural issues. The views that the market segments hold and the activities they participate in will have an impact on the products and services they purchase and it will affect how they respond to the message.

Alternatives to behavioral advertising and psychographic targeting include geographic targeting and demographic targeting

When advertisers want to efficiently reach as many consumers as possible, they use a six-step process.

  1. identify the objectives the advertisers do this by setting benchmarks, and identifying products or proposals, identifying the core values and strategic objectives. This step also includes listing and monitoring competitor's content and creating objectives for the next 12–18 months.
  2. The second step understanding buyers, is all about identifying what types of buyers the advertiser wants to target and identifying the buying process for the consumers.
  3. Identifying gaps is key as this illustrates all of the gaps in the content and finds what is important for the buying process and the stages of the content.
  4. Content is created and the stage where the key messages are identified and the quality bench line is discussed.
  5. Organizing distribution is key for maximizing the potential of the content, these can be social media, blogs, or google display networks.
  6. The last step is vital for an advertiser as they need to measure the return on investment (ROI) there are multiple ways to measure performance, these can be tracking web traffic, sales lead quality, and/ or social media sharing.

Alternatives to behavioral advertising include audience targeting, contextual targeting, and psychographic[58] targeting.

Effectiveness

[edit]

Targeting aims to improve the effectiveness of advertising and reduce the wastage created by sending advertising to consumers who are unlikely to purchase that product. Targeted advertising or improved targeting may lead to lower advertising costs and expenditures.[3]

The effects of advertising on society and those targeted are all implicitly underpinned by the consideration of whether advertising compromises autonomous choice.[59]

Those arguing for the ethical acceptability of advertising claim that, because of the commercially competitive context of advertising, the consumer has a choice over what to accept and what to reject.

Humans have the cognitive competence and are equipped with the necessary faculties to decide whether to be affected by adverts.[60] Those arguing against note, for example, that advertising can make us buy things we do not want or that, as advertising is enmeshed in a capitalist system, it only presents choices based on consumerist-centered reality thus limiting the exposure to non-materialist lifestyles.

Although the effects of target advertising are mainly focused on those targeted, it can also affect those outside of the target segment. Its unintended audiences often view an advertisement targeted at other groups and start forming judgments and decisions regarding the advertisement and even the brand and company behind the advertisement, these judgments may affect future consumer behavior.[61]

The Network Advertising Initiative conducted a study[62] in 2009 measuring the pricing and effectiveness of targeted advertising. It revealed that targeted advertising:

  • Secured an average of 2.7 times as much revenue per ad as non-targeted "run of network" advertising.
  • Twice as effective at converting users who click on the ads into buyers

However, other studies show that targeted advertising, at least by gender,[1] is not effective.

One of the major difficulties in measuring the economic efficiency of targeting, however, is being able to observe what would have happened in the absence of targeting since the users targeted by advertisers are more likely to convert than the general population. Farahat and Bailey [63] exploit a large-scale natural experiment on Yahoo! allowing them to measure the true economic impact of targeted advertising on brand searches and clicks. They find, assuming the cost per 1000 ad impressions (CPM) is $1, that:

  • The marginal cost of a brand-related search resulting from ads is $15.65 per search but is only $1.69 per search from a targeted campaign.
  • The marginal cost of a click is 72 cents, but only 16 cents from a targeted campaign.
  • The variation in CTR lifts from targeted advertising campaigns is mostly determined by pre-existing brand interest.

Research shows that Content marketing in 2015 generated 3 times as many leads as traditional outbound marketing, but costs 62% less[64] showing how being able to advertise to targeted consumers is becoming the ideal way to advertise to the public. Other stats show that 86% of people skip television adverts and 44% of people ignore direct mail, which also displays how advertising to the wrong group of people can be a waste of resources.[64]

Benefits and disadvantages

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Benefits

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Proponents of targeted advertising argue that there are advantages for both consumers and advertisers:

Consumers

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Targeted advertising benefits consumers because advertisers can effectively attract consumers by using their purchasing and browsing habits this enables ads to be more apparent and useful for customers. Having ads that are related to the interests of the consumers allows the message to be received in a directly through effective touchpoints. An example of how targeted advertising is beneficial to consumers is that if someone sees an ad targeted to them for something similar to an item they have previously viewed online and were interested in, they are more likely to buy it.

Consumers can benefit from targeted advertising in the following ways:

  • More effective delivery of desired product or service directly to the consumer:[65] Having assumed the traits or interests of the consumer from their targeting, advertisements that will appeal to engage the customer are used.
  • More direct delivery of a message that relates to the consumer's interest:[65] Advertisements are comfortably delivered to the customer, whether it be jargon or a certain medium, the delivery of the message is part of the consumer's 'lifestyle'

Intelligence agencies

[edit]

Intelligence agencies worldwide can more easily, and without exposing their personnel to the risks of HUMINT, track targets at sensitive locations such as military bases or training camps by simply purchasing location data from commercial providers who collect it from mobile devices with geotargeting enabled used by the operatives present at these places.[66]

Location data can be extremely valuable and must be protected. It can reveal details about the number of users in a location, user and supply movements, daily routines (user and organizational), and can expose otherwise unknown associations between users and locations.

— National Security Agency

Advertiser

[edit]

Advertisers benefit from target advertising are reducing resource costs and creating more effective ads by attracting consumers with a strong appeal to these products. Targeted advertising allows advertisers to reduce the cost of advertisement by minimizing "wasted" advertisements to non-interested consumers. Targeted advertising captivates the attention of consumers they were aimed at resulting in higher return on investment for the company.

Because behavioral advertising enables advertisers to more easily determine user preferences and purchasing habits, the ads will be more pertinent and useful for consumers. By creating a more efficient and effective manner of advertising to the consumer, an advertiser benefits greatly in the following ways:

  • More efficient campaign development:[65] By having information about the consumer an advertiser can make more concise decisions on how to best communicate with them.
  • Better use of advertising dollar:[65] A greater understanding of the targeted audience will allow an advertiser to achieve better results with an advertising campaign
  • Increased return on investment: Targeted advertisements will yield higher results for lower costs.

Using information from consumers can benefit the advertiser by developing a more efficient campaign, targeted advertising is proven to work both effectively and efficiently.[67] They don't want to waste time and money advertising to the "wrong people".[3] Through technological advances, the internet has allowed advertisers to target consumers beyond the capabilities of traditional media, and target significantly larger amount.[68]

The main advantage of using targeted advertising is that it can help minimize wasted advertising by using detailed information about individuals who are intended for a product.[69] If consumers produce these ads that are targeted at them, it is more likely they will be interested and click on them. 'Know thy consumer', is a simple principle used by advertisers, when businesses know information about consumers, it can be easier to target them and get them to purchase their product.

Some consumers do not mind if their information is used, and are more accepting of ads with easily accessible links. This is because they may appreciate adverts tailored to their preferences, rather than just generic ads. They are more likely to be directed to products they want, and possibly purchase them, in return generating more income for the business advertising.

Controversies

[edit]

Targeted advertising has raised controversies, most particularly regarding privacy rights and policies. With behavioral targeting focusing on specific user actions such as site history, browsing history, and buying behavior, this has raised user concern that all activity is being tracked.

Privacy International, a UK-based registered charity that defends and promotes the right to privacy across the world, suggests that from any ethical standpoint such interception of web traffic must be conditional on the based on explicit and informed consent, and action must be taken where organizations can be shown to have acted unlawfully.[citation needed]

A survey conducted in the United States by the Pew Internet & American Life Project between January 20 and February 19, 2012, revealed that most Americans are not in favor of targeted advertising, seeing it as an invasion of privacy. Indeed, 68% of those surveyed said they are "not okay" with targeted advertising because they do not like having their online behavior tracked and analyzed.

Another issue with targeted advertising is the lack of 'new' advertisements of goods or services. Seeing as all ads are tailored to be based on user preferences, no different products will be introduced to the consumer. Hence, in this case, the consumer will be at a loss as they are not exposed to anything new.

Advertisers concentrate their resources on the consumer, which can be very effective when done right.[70] When advertising doesn't work, the consumer can find this creepy and start wondering how the advertiser learned the information about them.[26] Consumers can have concerns over ads targeted at them, which are too personal for comfort, feeling a need for control over their data.[71]

In targeted advertising privacy is a complicated issue due to the type of protected user information and the number of parties involved. The three main parties involved in online advertising are the advertiser, the publisher, and the network. People tend to want to keep their previously browsed websites private, although users 'clickstreams' are being transferred to advertisers who work with ad networks. The user's preferences and interests are visible through their clickstream and their behavioral profile is generated.[72]

As of 2010, many people have found this form of advertising to be concerning and see these tactics as manipulative and a sense of discrimination.[72] As a result of this, several methods have been introduced to avoid advertising.[4] Internet users employing ad blockers are rapidly growing in numbers. The average global ad-blocking[73] rate in early 2018 was estimated at 27 percent. Greece is at the top of the list with more than 40% of internet users admitting to using ad-blocking software. Among the technical population ad-blocking reaches 58%.[74]

Privacy and security concerns

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Targeted advertising raises privacy concerns. Targeted advertising is performed by analyzing consumers' activities through online services such as HTTP cookies and data mining, both of which can be seen as detrimental to consumers' privacy. Marketers research consumers' online activity for targeted advertising campaigns like programmatic and SEO.

Consumers' privacy concerns revolve around today's unprecedented tracking capabilities and whether to trust their trackers. Consumers may feel uncomfortable with sites knowing so much about their activity online. Targeted advertising aims to increase promotions' relevance to potential buyers, delivering ad campaign executions to specified consumers at critical stages in the buying decision process. This potentially limits a consumer's awareness of alternatives and reinforces selective exposure.

Consumers may start avoiding certain sites and brands if they keep getting served the same advertisements and the consumer may feel like they are being watched too much or may start getting annoyed with certain brands. Due to the increased use of tracking cookies all over the web, many sites now have cookie notices that pop up when a visitor lands on a site. The notice informs the visitor about the use of cookies, how they affect the visitor, and the visitor's options in regarding to what information the cookies can obtain.

As of 2019, many online users and advocacy groups were concerned about privacy issues around targeted advertising, because it requires aggregation of large amounts of personal data, including highly sensitive data, such as sexual orientation or sexual preferences, health issues, and location, which is then traded between hundreds of parties in the process of real-time bidding.[75][76]

This is a controversy that the behavioral targeting industry is trying to contain through education, advocacy, and product constraints to keep all information non-personally identifiable or to obtain permission from end-users.[77] AOL created animated cartoons in 2008 to explain to its users that their past actions may determine the content of ads they see in the future.[78]

Canadian academics at the University of Ottawa Canadian Internet Policy and Public Interest Clinic have recently demanded the federal privacy commissioner investigate online profiling of Internet users for targeted advertising.[79]

The European Commission (via Commissioner Meglena Kuneva) has also raised several concerns related to online data collection (of personal data), profiling, and behavioral targeting, and is looking to "enforce existing regulation".[80]

In October 2009 it was reported that a recent survey carried out by the University of Pennsylvania and the Berkeley Center for Law and Technology found that a large majority of US internet users rejected the use of behavioral advertising.[81] Several research efforts by academics and others as of 2009 have demonstrated that data that is supposedly anonymized can be used to identify real individuals.[82]

In December 2010, online tracking firm Quantcast agreed to pay $2.4M to settle a class-action lawsuit for their use of 'zombie' cookies to track consumers. These zombie cookies, which were on partner sites such as MTV, Hulu, and ESPN, would re-generate to continue tracking the user even if they were deleted.[83] Other uses of such technology include Facebook, and their use of the Facebook Beacon to track users across the internet, to later use for more targeted advertising.[84] Tracking mechanisms without consumer consent are generally frowned upon; however, tracking of consumer behavior online or on mobile devices is key of digital advertising, which is the financial backbone to most of the internet.

In March 2011, it was reported that the online ad industry would begin working with the Council of Better Business Bureaus to start policing itself as part of its program to monitor and regulate how marketers track consumers online, also known as behavioral advertising.[85]

Microphone surveillance theories

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Since at least the mid 2010s, many users of smartphones or other mobile devices have advanced the theory that technology companies are using microphones in the devices to record personal conversations for purposes of targeted advertising.[86] Such theories are often accompanied by personal anecdotes involving advertisements with apparent connections to prior conversations.[87] Facebook has denied the practice, and Mark Zuckerberg denied it in congressional testimony.[88] Google has also denied using ambient sound or conversations to target advertising.[89] Technology experts who have investigated the claims have described them as unproven and unlikely.[89][90][91] An alternative explanation for apparent connections between conversations and subsequent advertisements is the fact that technology companies track user behavior and interests in many ways other than via microphones.[92]

In December 2023, 404 Media reported that Cox Media Group was advertising a service to marketing professionals called "Active Listening", which involved the ability to listen to microphones installed in smartphones, smart TVs, and other devices in order to target ads to consumers.[93][94] A pitch deck promoting the capability stated that it targeted "Google/Bing" and that Cox Media Group was a Google Premier Partner.[95] Meta, Amazon, Google, and Microsoft all denied using the service.[96] In response to questions from 404 Media, Google stated that it had removed Cox Media Group from its Partners Program after a review.[95] Cox Media removed the material from their website and denied listening to any conversations.[97]

History

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Contemporary data driven marketing can be traced back to the 1980s and the emergence of database marketing, which increased the ease of personalizing customer communications.[98]

See also

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References

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Further reading

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Revisions and contributorsEdit on WikipediaRead on Wikipedia
from Grokipedia
Targeted advertising is the practice of selecting and displaying advertisements to specific or groups based on collected about their demographics, behaviors, interests, and activities, aiming to enhance ad and efficiency over mass-market approaches. This method relies on technologies such as , tracking pixels, and algorithmic profiling to segment audiences, originating from early demographic targeting in print and broadcast media but proliferating with digital platforms in the and 2000s, including milestones like the 1994 launch of ads and Google's 2000 AdWords for search-query-based delivery. Empirical analyses indicate targeted advertising substantially outperforms untargeted alternatives, with studies showing roughly double the impact on responses like brand searches and purchase intent, while enabling advertisers to lower costs and improve return on ad spend through reduced waste. Economically, it supports free or subsidized content by generating revenue from precise matching of ads to users, potentially passing savings to via lower product prices, though it has ignited debates over due to pervasive and risks of inference-based profiling that reveal sensitive attributes without explicit . Regulatory responses, including laws and deprecation of third-party trackers, reflect ongoing tensions between these efficiency gains and concerns about user autonomy and .

Fundamentals

Definition and Core Principles

Targeted advertising constitutes a form of promotional delivery wherein advertisements are selectively presented to subsets of consumers based on compiled regarding their attributes, such as demographics, behavioral patterns, and inferred interests, with of elevating ad pertinence over undifferentiated . This approach predominantly operates within digital ecosystems, leveraging algorithmic processing of user-generated trails—including visits, search queries, and transaction histories—to construct individualized or cohort-specific profiles. Unlike traditional , which scatters messages broadly irrespective of recipient alignment, targeted variants prioritize precision matching to mitigate waste and amplify response likelihoods. At its foundation, the mechanism hinges on from disparate touchpoints, such as , device IDs, and third-party trackers, which furnish inputs for segmentation algorithms that classify users into affinity groups. These segments inform systems in programmatic environments, where ad exchanges impressions to the highest-value bidder aligned with the user's profile, ensuring ads resonate with contextual or historical signals of demand. The principle of maximization underpins claims, positing that ads concordant with user predispositions yield superior click-through rates—empirically documented at 2-3 times those of non-targeted counterparts in controlled studies—by reducing informational between commercial intent and consumer propensity. Causal underpinnings derive from the asymmetry in information access: advertisers exploit observable proxies for unexpressed preferences to simulate direct market signaling, akin to in , wherein varied willingness-to-pay is captured through differentiated exposure. Verification of profile accuracy occurs iteratively via engagement metrics, refining models through feedback loops that correlate exposure with outcomes like purchases or attributions. This iterative calibration embodies a core tenet of adaptive efficiency, though it presupposes and algorithmic fidelity, vulnerabilities noted in analyses of tracking inaccuracies exceeding 20% in cross-device scenarios. Empirical validation from platforms indicates targeted campaigns achieve return on ad spend (ROAS) uplifts of 50-100% relative to baselines, attributable to diminished scatter and heightened conversion funnels.

Economic Rationale from First Principles

Targeted advertising emerges from the economic imperative to allocate scarce resources efficiently in markets characterized by information asymmetries between producers and consumers. fundamentally serves to disseminate product information, mitigate search costs, and facilitate matching between ; however, in non-targeted systems, resources are dissipated on broad audiences where only a fraction may hold relevant preferences, leading to suboptimal outcomes for advertisers who face diminished returns on impressions served to uninterested parties. By contrast, targeting leverages observable —such as demographics, behaviors, or contexts—to predict consumer interest, enabling a more precise allocation of ad inventory to high-value users, which causally enhances match quality and reduces wasteful expenditure. This precision yields direct efficiency gains for advertisers through elevated (ROI), as targeted campaigns achieve higher click-through rates, conversion probabilities, and revenue per impression compared to non-targeted alternatives. Empirical analyses demonstrate that behaviorally targeted ads generate approximately 2.7 times the revenue per ad impression relative to non-targeted "run-of-network" displays, reflecting improved causal linkages between exposure and purchase intent. Restrictions on targeting data, such as interventions, have been shown to reduce ad by up to 65% in controlled field experiments, underscoring the causal role of data-driven in sustaining ROI amid competitive for attention. For publishers and platforms, these dynamics translate to elevated ad revenues—evidenced by U.S. advertising expenditures reaching $69.2 billion in 2017, comprising 35% of total ad spend—allowing cross-subsidization of free or low-cost content services that consumers value highly, with median annual benefits estimated at over $8,000 for and $17,000 for search functionalities. From a broader welfare perspective, targeted advertising promotes by intensifying price competition among advertisers vying for matched slots, potentially lowering consumer prices as firms optimize outreach to marginal buyers. While non-targeted approaches necessitate higher aggregate ad volumes to achieve equivalent reach, targeting minimizes such proliferation, curbing ad fatigue and preserving user attention as a finite resource; this mechanism not only bolsters advertiser profitability but also sustains the viability of ad-supported ecosystems without necessitating alternative funding models like subscriptions or paywalls.

Historical Evolution

Pre-Digital Foundations

Targeted advertising predated digital technologies, relying on rudimentary data sources such as census records, subscription lists, and purchase histories to segment audiences by demographics like age, income, location, and occupation. In the late , print media enabled basic targeting through publications aimed at specific groups; for instance, magazines like , launched in , catered to female homemakers, allowing advertisers to reach women with products for household and family needs. Newspapers facilitated localized targeting via classified sections and regional distribution, while billboards and posters used geographic placement to appeal to passersby in high-traffic areas relevant to the product, such as ads near factories. Direct mail emerged as a key method for personalized targeting in the , with launching the first mail-order catalog in 1872, distributed to rural farmers via lists compiled from agricultural journals and , Roebuck & Co. expanding this model in 1893 with catalogs targeting isolated consumers based on postal addresses and inferred rural demographics. By the , businesses routinely sent coupon-bearing mailers and catalogs using purchased lists, marking an early form of response tracking through redemption rates. brokers proliferated in the mid-20th century, aggregating data from sources like magazine subscriptions, credit reports, and retail records to sell segmented lists, enabling advertisers to target by socioeconomic profiles—though accuracy was limited by manual compilation and self-reported data. Broadcast media introduced scale to demographic targeting in the . Radio , beginning commercially in the , leveraged program formats and time slots for rough segmentation; daytime shows targeted housewives with domestic goods, while evening serials appealed to families. advanced with the Audimeter device in the , but systematic demographic data emerged via diaries and surveys. , from the first sponsored ad in , built on this with Nielsen ratings starting in 1950, which quantified viewership by age, gender, and income through household panels, allowing sponsors to buy slots aligned with desired demographics like young adults during . These methods, while less precise than digital tracking due to sampling errors and lack of individual-level data, established the principle of matching ads to audience profiles to improve response rates over mass broadcasting.

Digital Pioneering (1990s-2000s)

The introduction of the in the early 1990s enabled the first forms of digital advertising, shifting from broadcast media to potentially addressable online audiences, though initial implementations were rudimentary. On October 27, 1994, the inaugural web banner ad, sponsored by , appeared on HotWired.com, achieving a 44% but relying on site-specific context rather than individualized targeting. This marked the onset of display advertising, with subsequent banners on sites like Yahoo! in 1994 incorporating basic contextual relevance to page content. Ad serving technologies advanced targeting capabilities in the mid-1990s, coinciding with the browser's introduction of HTTP cookies in 1994, which allowed persistent user identification across sessions for rudimentary tracking. , founded in 1995 and launching its platform in 1996, pioneered scalable ad networks by aggregating inventory from multiple publishers and enabling criteria-based targeting, such as by demographics, , or time of day, alongside ROI measurement tools. By 1996, DoubleClick's systems facilitated dynamic ad rotation and performance analytics, addressing banner fatigue as click-through rates plummeted from highs of 40% to under 1% by the late 1990s, prompting refinements like pop-up ads in 1997 and early behavioral signals via cookies. The early 2000s saw intent-based targeting dominate through search advertising, leveraging user queries for precise relevance. In 1998, GoTo.com (later Overture) introduced the first pay-per-click auction model, ranking ads by bid amount tied to keywords, which targeted ads to explicit searcher interests. Google AdWords, launched on October 23, 2000, refined this with a quality-score-adjusted auction, prioritizing ad relevance to queries over bids alone, starting with 350 advertisers and generating $70 million in revenue by 2001. This approach, emphasizing cost-per-click over impressions, boosted efficiency—ad click-through rates averaged 2-5% for search versus under 0.5% for banners—and scaled rapidly, with AdWords handling millions of daily auctions by mid-decade, fundamentally prioritizing user intent as a causal driver of conversion over demographic proxies. Concurrently, platforms like Amazon from 1994 onward used purchase and browsing data for personalized product ads, prefiguring broader behavioral targeting, though web-wide implementation lagged until ad exchanges in the late 2000s.

Maturation and Scaling (2010s-Present)

The marked the maturation of targeted advertising through the integration of analytics, algorithms, and mobile ecosystems, shifting from rudimentary cookie-based tracking to sophisticated behavioral and cross-device profiling. Platforms like and refined audience segmentation using vast user interaction datasets, enabling predictive modeling for ad relevance; for instance, Facebook's Custom Audiences, launched in 2012, allowed advertisers to upload customer lists for lookalike targeting, boosting conversion rates by matching user similarities via proprietary algorithms. Programmatic advertising platforms proliferated, automating ad auctions via (RTB), which by 2015 accounted for over 50% of digital display ad buys in mature markets, reducing manual negotiations and enhancing efficiency through millisecond decision-making on user signals like browsing history and device IDs. Scaling accelerated as digital ad revenues exploded, driven by smartphone penetration exceeding 80% in developed economies by mid-decade and the dominance of walled gardens like (with 28% global digital ad share in 2019) and Meta. U.S. ad grew from approximately $60 billion in 2010 to $225 billion by 2023, with targeted formats comprising the majority via search, social, and video channels; globally, programmatic spend—predominantly targeted—reached $595 billion in 2024, reflecting a over 20% since 2015 amid surges and video ad expansions like YouTube's TrueView in 2010. This expansion was causal: advertisers prioritized ROI, with studies showing targeted campaigns yielding 2-3 times higher engagement than non-targeted ones, substantiated by data from demand-side platforms (DSPs) like . Privacy regulations introduced friction, compelling adaptations without halting growth. The EU's (GDPR), effective May 25, 2018, mandated explicit for personal data processing, curtailing third-party cookie use and reducing targeted ad impressions in by up to 20% initially, as publishers shifted to consent management platforms (CMPs). California's Consumer Privacy Act (CCPA), enforced from January 1, 2020, mirrored this by granting opt-out rights, prompting U.S. firms to anonymize data aggregates. Apple's iOS 14.5 update in April 2021 deprecated the (IDFA) via App Tracking Transparency (ATT), requiring opt-in prompts that yielded low compliance rates (around 20-30% in gaming apps), leading to signal loss and estimated 30-60% revenue drops for mobile app advertisers reliant on cross-app tracking. Industry responses emphasized resilience: advertisers pivoted to first-party data from loyalty programs, contextual signals (e.g., page content matching via ), and probabilistic modeling to infer without identifiers, sustaining scaling as global digital ad spend hit $694 billion in 2024. Meta's ad revenue, for example, rose from $113.6 billion in 2022 to $131.9 billion in 2023 despite signal deprecation, via aggregated event measurement and AI-driven conversions. By 2025, hybrid approaches—blending consented behavioral data with proposals like Google's Topics —underpin continued maturation, with programmatic projected to exceed $800 billion by 2028, underscoring targeted advertising's economic primacy amid regulatory evolution.

Targeting Methods

Demographic and Sociodemographic Approaches

Demographic targeting segments audiences based on core attributes such as age, , and parental status to tailor advertisements to groups with statistically correlated purchasing patterns. For instance, platforms like enable advertisers to select age ranges (e.g., 18-24 or 65+), genders, and household income brackets (e.g., top 10% earners), drawing from user-declared data and algorithmic inferences to predict relevance. This approach originated in analog media but gained precision in digital advertising from the mid-1990s, when display ads began incorporating basic demographic filters alongside contextual cues. Sociodemographic targeting expands this framework by integrating social and economic variables, including education level, occupation, , , and family size, to refine segments further. Data acquisition combines self-reported profiles from sign-ups with third-party aggregators that model traits from transaction records and , though post-2018 regulations like the EU's GDPR have curtailed cross-border data sharing and mandated consent. Advertisers apply these in programmatic systems, where bids prioritize users matching criteria like "college-educated females aged 25-34 in urban areas" for products such as or consumer goods. Empirical evidence underscores both utility and limitations: demographic targeting boosts ad visibility and click-through rates by aligning with broad behavioral averages, yet peer-reviewed studies show it elevates visual attention without proportionally enhancing evaluations or purchase intentions. Accuracy remains a critical flaw; analyses of programmatic platforms reveal that gender-plus-age targeting hits only 24% precision on average, while sociodemographic labels like "parents" misidentify up to 70% of recipients who lack children, eroding ROI through wasted impressions. These inaccuracies stem from outdated inferences and signal loss in privacy-focused environments, prompting reliance on hybrid methods for causal in conversions.

Behavioral and Interest-Based Targeting

Behavioral targeting utilizes data on users' online actions—such as browsing histories, search queries, click patterns, and purchase records—to segment audiences and serve advertisements presumed to align with their demonstrated preferences. This approach relies on the that past behaviors signal likely future interests, enabling advertisers to prioritize over broad demographic casts. Tracking occurs primarily through first-party and third-party , device identifiers, and pixels that log interactions across sites and apps, aggregating these into profiles managed by data platforms. Interest-based targeting overlaps substantially with behavioral methods but emphasizes categorizing users into predefined interest clusters, such as "automotive enthusiasts" or "health-conscious consumers," inferred from behavioral signals rather than self-declared data. For example, repeated visits to fitness websites or searches for workout gear might tag a user for sports apparel ads, with platforms like Meta distinguishing behavioral signals (e.g., app usage) from interest labels derived from aggregated patterns. Unlike purely demographic targeting, this method processes dynamic data streams to refine segments in real-time, often via algorithms that predict affinity scores from behavioral trajectories. Implementation involves four core steps: data capture from user sessions, analysis to identify patterns (e.g., frequency of category-specific engagements), audience segmentation into cohorts sharing behavioral traits, and ad delivery through demand-side platforms that bid on inventory matching those profiles. Retargeting, a prominent , specifically follows users who interacted with a —such as viewing products without purchase—and re-exposes them to related ads on other sites, leveraging short-term intent signals. Pioneered in the late 1990s as firms began amassing user logs, these techniques scaled with networks in the early 2000s, though efficacy depends on data accuracy and cross-device linkage, which fragmented post-2010s with mobile shifts and ad blockers. Empirical assessments, including those from regulatory analyses, confirm behavioral methods yield measurable lifts in click-through rates—often 2-3 times higher than random targeting—by exploiting observable action correlations, though returns diminish with saturation and user of tracking. Regulations like the EU's and California's CCPA impose requirements, reflecting tensions between precision gains and privacy costs, yet adoption persists due to the direct mapping of behaviors to commercial intent.

Contextual and Content Matching

Contextual targeting in advertising involves selecting and displaying advertisements based on the semantic content, keywords, topics, or themes of the webpage or media where the ad appears, rather than personal user data. This method analyzes the surrounding editorial or video content in real time to match ads to inferred user interests at that moment, such as placing ads on a page discussing destinations. Content matching, often used interchangeably or as a , emphasizes algorithmic alignment between ad creative and page elements like headlines, articles, or metadata to ensure thematic relevance. Implementation relies on natural language processing (NLP), machine learning classifiers, and keyword extraction to categorize page content into hierarchies of topics or sentiments. For instance, platforms scan for entities like brands or categories without tracking individual browsing history, enabling placement on sites with matching themes, such as automotive ads on car review articles. Advanced systems incorporate semantic analysis to detect nuances, avoiding literal keyword mismatches, and integrate with programmatic bidding for automated decisions. This approach gained prominence with privacy regulations like GDPR in 2018 and the phasing out of third-party cookies announced by in 2020, positioning it as a compliant alternative to behavioral targeting. Empirical studies indicate contextual targeting can achieve click-through rates up to 50% higher and conversion rates 30% better than non-contextual ads in certain scenarios, attributed to immediate relevance that aligns with active user attention. A 2022 study on mobile contextual ads found positive effects on purchase intention mediated by perceived ad relevance and reduced intrusiveness, with attitudes toward advertising improving when content fit was high. However, performance varies; a 2011 analysis modeled contextual auctions showing publishers may underinvest in quality content without behavioral signals, potentially leading to lower overall efficiency compared to data-driven methods in competitive markets. Industry reports from firms like Integral Ad Science note its cost-effectiveness due to lower data overhead, though it risks overgeneralization on ambiguous pages. Key advantages include enhanced brand safety, as ads avoid adjacency to harmful content based solely on page context rather than user profiles, and compliance with data protection laws without consent requirements for personal tracking. Drawbacks encompass scalability challenges in dynamic content environments and potential for less precise audience reach, with empirical viability as a behavioral substitute depending on AI advancements in content understanding.

Location, Device, and Time-Based Techniques

Location-based targeting employs geographic data, such as GPS coordinates from mobile devices, IP addresses, or Wi-Fi signals, to deliver advertisements relevant to a user's physical proximity to businesses or events. Techniques include geotargeting, which serves ads to broad areas like cities, and geofencing, which creates virtual boundaries around specific sites to trigger ads when users enter, such as promotions for nearby stores. For instance, a restaurant might target users within 500 meters to 3 miles with discount offers, optimizing reach for local foot traffic. Empirical evidence indicates that location familiarity enhances response rates, with click-through rates increasing over 26% at revisited locations compared to first-time visits. Device-based targeting segments audiences by hardware and software attributes, including mobile versus desktop usage, operating systems like or Android, or device models, to align ads with platform-specific behaviors and capabilities. Advertisers may prioritize mobile for impulse-driven purchases or desktop for detailed , as mobile exposure has been shown to boost ad awareness by 80% and recall by 133% relative to tablets and display formats in cross-device studies. links user identities across gadgets via identifiers like or logins, enabling consistent messaging, though regulations increasingly limit such practices. This method refines bidding in programmatic systems, where granular device data improves audience precision without relying solely on signals. Time-based targeting, often termed , schedules advertisements according to temporal patterns, such as hours of the day, days of the week, or seasonal events, to match user availability and receptivity. Platforms analyze historical data to identify peak engagement windows—for example, delivering ads during evening leisure hours or B2B content on weekdays—reducing waste on off-peak displays. Effectiveness varies by ; studies show time-based ads perform better when combined with data, as consumer mobility influences responsiveness, with non-personalized messages underperforming in mismatched scenarios. Real-time adjustments via programmatic bidding further enhance outcomes by responding to live behavioral shifts. These techniques often integrate for compounded precision: a fitness app might geofence gym vicinities, target smartphones during morning commutes, and daypart for post-work hours, leveraging causal links between spatiotemporal context and purchase intent. While boosting relevance, they raise concerns, prompting opt-in requirements in regions like the under GDPR, though adoption persists due to measurable lifts in conversion rates from localized, timely delivery.

Psychographic Segmentation

Psychographic segmentation categorizes consumers in targeted advertising based on psychological attributes, including values, attitudes, interests, lifestyles, opinions, and traits, rather than observable demographics or behaviors alone. This method seeks to match advertisements to underlying motivations driving purchase decisions, enabling more resonant messaging that appeals to emotional or aspirational drivers. For example, lifestyle-oriented segments might respond to ads emphasizing adventure or status, while value-driven groups prioritize or tradition. Key variables in often revolve around activities, interests, and opinions (AIO framework), alongside and influences on . Advertisers derive these insights from self-reported surveys, online engagement patterns, and inferred profiles from browsing history or content consumption. The VALS (Values and Lifestyles) system, pioneered by in 1978 and periodically updated, exemplifies structured psychographic classification by dividing U.S. adults into eight segments—such as Innovators (successful, high-resource innovators) and Experiencers (young, enthusiastic trend-followers)—based on (ideals, achievement, or self-expression) and resources. This framework has informed ad strategies for brands targeting specific psychotypes, like for Achievers or practical items for Makers. In digital targeted advertising, integrate with platforms' algorithms to refine audience pools; for instance, inferred interests from "likes" or forum participation enable real-time ad customization. A 2019 empirical study in Applied Sciences analyzed data and found psychographic variables, including traits, explained variance in purchase intent more effectively than demographics for apparel and , with model accuracy improving by up to 15% when combining psychographics with behavioral data. Similarly, a 2024 analysis of case studies highlighted psychographic tailoring boosting campaign ROI through enhanced relevance, as seen in wellness brands segmenting by health-conscious attitudes to achieve 20-30% higher click-through rates. Data acquisition for raises accuracy and issues, as much relies on proxies like AI-inferred sentiment from text rather than direct input, potentially amplifying biases from unrepresentative training datasets. Regulations such as the EU's GDPR (effective 2018) and evolving U.S. state laws have curtailed broad profiling, prompting shifts toward consented or aggregated data. Despite these, psychographic approaches persist in programmatic advertising, where they complement behavioral targeting to forecast responsiveness, though causal evidence for uplift remains context-dependent and requires validation against control groups to distinguish correlation from persuasion effects.

Technical Mechanisms

Data Acquisition and Processing

Data acquisition in targeted advertising primarily involves collecting from online interactions across websites, applications, and devices. First-party is gathered directly by publishers or advertisers from their own platforms, such as website visits, search queries, and purchase histories, enabling initial user profiling. Third-party , sourced from external providers like data brokers or , supplements this by aggregating behaviors observed on multiple unrelated sites, often through cross-site tracking mechanisms. Additional inputs include location derived from , IP addresses, , and device sensors, as well as offline matched via probabilistic or deterministic identifiers. Technical mechanisms for acquisition rely on tools like HTTP cookies, where first-party cookies are set by the visited domain to track session-based activities, while third-party cookies, embedded via ad scripts from external domains, enable persistent cross-site user identification. Tracking pixels—small invisible images loaded from ad servers—log user actions such as page views and clicks, transmitting data back for behavioral analysis. Mobile apps utilize software development kits (SDKs) and device IDs like IDFA () or AAID (Android) to capture app usage, while browser fingerprinting combines attributes such as screen resolution, fonts, and plugins to uniquely identify users without traditional cookies. Processing begins with ingestion into data management platforms (DMPs), which unify disparate data streams from first-, second-, and third-party sources into centralized repositories. DMPs perform cleaning, deduplication, and to create anonymized user segments based on inferred attributes like interests, demographics, and purchase intent, often employing algorithms for pattern recognition and prediction. Data is then segmented into audiences for activation in ad campaigns, with real-time processing pipelines enabling dynamic updates in programmatic environments. This pipeline integrates structured data (e.g., timestamps, IDs) with unstructured signals (e.g., content consumption), ensuring scalability for billions of daily interactions while adhering to varying regulatory constraints on identifiable information.

Algorithmic Implementation and Retargeting

Algorithmic implementation in targeted advertising centers on models that predict user responses to ads, enabling precise personalization and bidding. Supervised learning algorithms, including for baseline CTR estimation and advanced methods like (e.g., ) or deep neural networks, analyze features such as user demographics, browsing patterns, ad creatives, and contextual signals to forecast click probabilities and conversion rates. These models are trained on historical data, incorporating techniques like to handle sparse, high-dimensional inputs common in ad ecosystems. In real-time bidding environments, demand-side platforms deploy these models within milliseconds to compute bid values, often using formulas that multiply predicted CTR by expected post-click value and adjust for auction competition and budget constraints. variants, such as algorithms, further refine decisions by balancing exploration of new ad variants against exploitation of known performers, optimizing long-term campaign returns. Retargeting extends these implementations by algorithmically segmenting users based on prior engagements, tracked via client-side scripts like pixels that set cookies or identifiers upon events such as product views or abandoned carts. Demand-side platforms integrate this data from platforms to create dynamic audiences, applying higher bid multipliers or dedicated scoring thresholds to prioritize these segments in ad auctions across publisher networks. To enhance effectiveness, retargeting algorithms incorporate propensity scoring—estimating baseline purchase likelihood from interaction data—and uplift modeling to isolate incremental ad impact, targeting only users with positive expected treatment effects as determined by causal inference techniques like regression discontinuity design. Frequency controls and time-decay functions prevent overexposure, while cross-device maintains user continuity despite fragmented tracking. Studies report retargeting yields conversion lifts of 70% relative to standard display advertising, driven by the causal of recency-based signals, though outcomes vary with quality and audience freshness.

Programmatic and Real-Time Bidding

Programmatic advertising encompasses the automated purchase and sale of digital ad using software platforms, algorithms, and to match advertisers with publishers based on targeting parameters such as user demographics, , and . This approach supplanted manual negotiations by enabling scalable, data-driven transactions across demand-side platforms (DSPs), supply-side platforms (SSPs), and ad exchanges. Originating in the mid-2000s with early ad exchanges, programmatic gained prominence around 2007-2008 as DSPs emerged to aggregate and optimize bids, reducing human intervention and improving efficiency in ad allocation. Real-time bidding (RTB), a core subset of programmatic, facilitates for individual ad impressions occurring in under 100-200 milliseconds as a webpage or app loads. In this process, a publisher's SSP sends a bid request via an , embedding anonymized user data—including , device IDs, location, browsing history, and targeting signals—to potential buyers. DSPs, on behalf of advertisers, receive these requests and apply algorithmic rules to assess impression value against campaign goals, such as cost-per-thousand-impressions (CPM) thresholds or return-on-ad-spend (ROAS) predictions, before submitting automated bids. The exchange then awards the impression to the highest valid bid, often using second-price where the winner pays the second-highest bid plus a increment, ensuring the ad renders seamlessly without perceptible delay. RTB differs from broader programmatic methods like programmatic direct or private marketplaces (PMPs), which involve fixed-price deals or invite-only auctions without open, per-impression competition, offering advertisers more control over premium but less price dynamism. Technically, RTB protocols like OpenRTB standardize bid requests in format, specifying fields for user segments, ad sizes, and floor s to enable precise matching. Low-latency infrastructure, including and in-memory databases, is essential to handle billions of daily auctions, with failure to bid or win resulting in fallback to default ads or unsold . By 2024, RTB drove the majority of programmatic , contributing to global programmatic ad spend exceeding $595 billion, with U.S. display ads reaching 88.2% programmatic penetration. This mechanism enhances targeting accuracy by integrating first-party and third-party data in real time, though it raises concerns over data privacy due to the granularity of shared signals.

Empirical Effectiveness

Key Studies and Metrics

A 2009 study commissioned by the Network Advertising Initiative analyzed data from major ad networks and found that behaviorally targeted display advertisements generated 2.68 times more revenue per ad impression than non-targeted "run of network" ads, with conversion rates of 6.8% versus 2.8%. The analysis, based on over 900,000 ad impressions, also indicated that targeted ads were more than twice as effective in converting users to purchasers. In a peer-reviewed experiment published in , Bleier and Eisenbeiss examined personalized retargeting banners on an site, demonstrating that ads tailored to users' prior browsing behavior, when combined with appropriate timing and non-obtrusive placement, more than doubled click-through rates relative to untailored ads. Their field study, involving randomized exposure to over 250,000 users, highlighted the causal role of in enhancing ad , though diminished with excessive obtrusiveness or poor content relevance. Subsequent research has qualified these gross metrics by accounting for endogeneity and . A 2012 study by Blake, Nosko, and Tadelis, using proprietary ad auction data, decomposed observed lifts in brand searches from targeting: selection effects—where ads reach higher-intent users—accounted for 77% of the average lift, while the true causal treatment effect explained the remaining 23%, with stronger causal impacts (up to 69% of lift) among users who converted. This underscores that while targeting amplifies reach to responsive audiences, isolating incremental value requires randomized holdout designs to mitigate . A 2023 empirical of Apple's 2021 app tracking transparency changes, which restricted targeted advertising, found that the led to a 68% drop in ad revenue for affected apps and substantial user abandonment rates, implying that targeting's removal reduced overall campaign viability and underscoring its role in sustaining conversions and . Conversion lift studies from platforms like typically report retargeting yields 2- to 3-fold increases in purchase probability for exposed cohorts versus controls, though these platform-specific metrics often rely on proprietary geo-experiments or matched-market tests rather than fully public .
StudyKey MetricTargeted vs. Non-TargetedMethodology
NAI (2009)Revenue per impression2.68x higherObservational ad network (900k+ impressions)
Bleier & Eisenbeiss (2015)>2x higher (optimized personalization)Randomized field experiment (250k+ users)
Blake et al. (2012)Causal lift in conversions23-69% of observed (net of bias)Ad with
Johnson (2023)App retention/revenue post-ban68% revenue dropQuasi-experimental ( policy change)

Comparative Performance Data

A 2010 study by the Network Advertising Initiative analyzed data from major ad networks and found that behaviorally targeted advertisements generated an average of 2.68 times more revenue per ad impression compared to non-targeted run-of-network ads, reflecting both higher advertiser and improved effectiveness metrics such as click-through rates. This multiplier was consistent across participating networks, with behavioral targeting yielding approximately twice the average price per ad and twice the performance in terms of user engagement. Empirical analyses of click-through rates (CTR) further demonstrate targeted advertising's superiority. A simulation-based study on sponsored reported that behavioral segmentation could improve CTR by up to 670% relative to unsegmented approaches, though average gains in real-world deployments are typically lower, around 2-3 times. Industry benchmarks from analyses corroborate this, indicating targeted ads achieve CTRs up to 5.3 times higher than non-personalized counterparts, driven by alignment with user interests and behaviors. Comparisons between behavioral and contextual targeting reveal nuanced trade-offs. While behavioral methods leverage historical data for persistent relevance, contextual approaches match ads to immediate page content without cross-site tracking. A 2019 publisher revenue analysis estimated only a 4% uplift from behavioral over non-personalized (often contextual) ads, equating to an incremental $0.00008 per ad, suggesting in mature markets where contextual baselines have improved via AI. However, combined hybrid strategies in experimental settings have shown synergistic effects, with behavioral augmentation boosting contextual CTR by 20-50% in select campaigns.
Targeting TypeKey MetricPerformance Multiplier vs. Non-TargetedStudy YearSource
Behavioral per ad impression2.68x2009 data, published 2010Network Advertising Initiative
BehavioralClick-through rate (CTR)Up to 7.7x (670% improvement) in segmented scenariosUndated simulationSponsored search analysis
Behavioral vs. ContextualPublisher uplift1.04x2019Ad tech study
Return on investment (ROI) data remains advertiser-specific but consistently favors targeting. Causal inference models from ad platform experiments indicate behavioral targeting enhances conversion rates by 1.5-3 times over random exposure, though attribution challenges and ad fatigue can erode gains over repeated impressions. Recent programmatic bidding analyses (2020-2024) confirm targeted campaigns yield 20-40% higher ROI in e-commerce, contingent on data quality and audience scale. These metrics underscore targeted advertising's empirical edge, though post-GDPR and CCPA environments have prompted shifts toward privacy-compliant variants with sustained but moderated performance uplifts.

Factors Influencing Outcomes

The effectiveness of targeted advertising, as gauged by metrics like click-through rates, conversion rates, and , hinges on the precision of data matching between ads and user profiles, with empirical analyses showing that higher match quality correlates with elevated performance due to reduced ad irrelevance. Restrictions on behavioral data access, such as those from privacy regulations including the EU's enacted in 2018, empirically degrade targeting accuracy and ad outcomes, as evidenced by post-regulation declines in match quality and advertiser revenue. Ad creative attributes exert substantial influence, with research applying the advertising value model identifying informativeness, , irritation levels, , , and as determinants of perceived ad value, which in turn drives attitudes toward the ad and purchase intentions among consumers exposed to retargeted formats. User-level variables, including persuasion knowledge—awareness of manipulative intent—and coping , moderate responses, often leading to heightened and lower when users perceive ads as overly intrusive or mismatched. Platform dynamics, such as algorithmic bid adjustments in real-time auctions and competition intensity, further shape outcomes, where superior targeting signals enable higher win rates but are vulnerable to ad fatigue from repetitive exposure, empirically linked to diminished returns over time. External policy interventions, like bans on ads, have been shown to precipitate sharp drops in user retention and feature development for ad-dependent apps, underscoring the causal role of -driven in sustaining viability. Consumer trust in handling also plays a mediating role, with studies revealing that concerns amplify irritation and erode effectiveness, particularly when over-collection of user leads to perceived inaccuracies or false claims.

Economic and Market Impacts

Benefits to Advertisers and Efficiency

Targeted advertising improves (ROI) for advertisers by delivering higher engagement and conversion metrics compared to non-targeted approaches. Behavioral targeting yields click-through rates (CTRs) 5.3 times higher than standard advertising, while retargeting achieves 10.8 times higher CTRs for previously exposed consumers. Search-based targeted ads demonstrate positive ROI, particularly in acquiring new users, as evidenced by econometric analysis of auction data. Conversion efficiency is markedly enhanced, with targeted search advertising averaging 4.40% conversion rates versus 0.57% for broader display formats. Retargeting further boosts purchase likelihood among early-funnel consumers by reinforcing intent signals through repeated exposure. These outcomes stem from data-driven matching of ads to user profiles, minimizing irrelevant impressions and concentrating spend on high-intent audiences. Operational efficiency arises from cost reductions and optimized . targeted ads cost $3 to $10 per thousand impressions (CPM), substantially below $22 or more for traditional media. Performance-based pricing models, such as cost-per-click (CPC), align payments with outcomes, curtailing upfront waste from scattershot campaigns. For small and medium enterprises (SMEs), digital platforms lower entry barriers by providing scalable inventory and precise targeting, enabling competitive reach without proportional budget increases. This precision curtails "scattering losses" inherent in mass advertising, directing budgets toward empirically responsive segments.

Consumer Welfare and Price Effects

Targeted advertising enhances welfare primarily by improving the match between advertisements and consumer preferences, thereby reducing search costs and enabling more efficient market outcomes. Empirical analyses indicate that behaviorally targeted display ads yield higher click-through rates compared to non-targeted ones, with studies reporting increases of up to 2-3 times in effectiveness metrics. This relevance fosters informed decision-making, allowing consumers to discover products or services aligned with their needs without extensive untargeted exposure, which aligns with economic models where advertising acts as a signal reducing . In competitive markets, such efficiency can translate to broader surplus, as firms compete more aggressively for matched audiences, potentially subsidizing free digital services through ad without direct consumer payments. However, the welfare impact incorporates countervailing forces, including the potential for personalized enabled by data-driven targeting. Theoretical frameworks identify three channels: enhanced product matching (positive for surplus), extraction via tailored prices (negative), and indirect effects on firm strategies; simulations suggest that matching benefits often dominate in scenarios with moderate data precision and horizontal , yielding net gains in utility excluding considerations. Empirical investigations, such as those estimating surplus differences from ad exposure, confirm that targeted ads can elevate overall welfare by facilitating access to lower-cost or better-suited options, though gains vary by segment and . For instance, in search friction models, targeted advertising lowers equilibrium prices when search costs are moderate, as it intensifies among sellers for informed buyers, but may elevate prices under high search costs by segmenting markets more finely. Regarding direct price effects, targeted generally exerts downward pressure on consumer-facing s through heightened and reduced advertising waste, with digital ad efficiencies—such as programmatic bidding—lowering overall costs that can propagate to end-users under . Evidence from macroeconomic analyses supports this, showing that cheaper, precise targeting correlates with increased and free media provision, benefiting consumers via indirect subsidies rather than explicit fees. Conversely, when targeting enables —charging higher rates to high-valuation consumers identified via data—it can diminish surplus for those segments, as firms capture more rents without uniform pricing; models predict this effect strengthens with convex but is mitigated in concave cases or with regulatory constraints. Real-world data from ad platform experiments indicate that while risks exist, aggregate reductions from better matching prevail in fragmented markets, though vulnerable groups with low valuations may face elevated effective costs if ads reinforce inelastic segments. Overall, empirical consensus leans toward positive net moderation, contingent on competitive intensity and data use scope.

Platform and Publisher Revenue Dynamics

Targeted advertising platforms, such as and Meta, derive substantial revenue from auction-based systems where advertisers bid on user-specific impressions informed by behavioral data, enabling higher effective CPMs through improved match quality. In , total U.S. internet advertising revenue reached $258.6 billion, up 14.9% from 2023, with alone accounting for $102.9 billion, predominantly captured by , which holds an 80.2% share of the PPC market. 's ad business generated over 77% of its $305.6 billion total revenue in 2023, fueled by targeted formats that yield an average 8:1 return on ad spend for advertisers, incentivizing higher bids and platform yields. Publishers, including news sites and content creators, integrate targeted ads via supply-side platforms (SSPs) and ad networks, receiving a portion of programmatic after intermediary fees, which typically range from 10-20% for SSPs alone. Digital advertising comprises 67% of top publishers' streams, with 89% anticipating growth in 2024 amid rising ad spend, though programmatic deals yield lower CPMs of $1-5 compared to $10-20 for direct sales. Behavioral targeting modestly benefits some publishers, with 33% reporting increased revenues from enhanced ad relevance, while 45% observe no significant change due to platform-dominated data flows that prioritize walled-garden inventory. Revenue dynamics reflect an interdependent yet asymmetric , where platforms leverage proprietary for superior targeting, capturing disproportionate value—evident in Google's dwarfing competitors—while publishers face margin compression from auction opacity and fee layers, prompting shifts toward direct deals and first-party strategies. Programmatic private marketplaces have seen upticks for select publishers in 2024, but open auctions remain volatile, with total publisher vulnerable to regulations reducing third-party targeting efficacy by up to 61% in some cases before adaptations like contextual shifts restored gains. This structure sustains overall market expansion but concentrates economic power, as platforms' scale in amplifies their elasticity relative to fragmented publishers.

Societal Benefits

Enhanced Relevance and Reduced Waste

Targeted advertising enhances by using consumer —such as past behaviors, search queries, and demographics—to deliver promotions aligned with individual preferences and needs, rather than generic messages to broad audiences. This matching process improves ad for recipients, who encounter offers more likely to address their actual demands, thereby decreasing the cognitive and temporal costs associated with sifting through irrelevant promotions. Empirical analyses confirm that such precision elevates metrics; for example, behavioral targeting has been shown to increase click-through rates by up to 67% relative to untargeted approaches, as consumers respond more favorably to pertinent content. From an advertiser's standpoint, this translates to reduced , as resources are allocated toward high-potential audiences instead of diffused across indifferent or incompatible groups. Economic models demonstrate that targeted strategies curtail the volume of impressions needed to achieve equivalent reach among viable prospects, minimizing expenditures on non-converting exposures and optimizing return on spend. Field experiments further substantiate this efficiency: restrictions on targeting , such as those imposed by regulations, degrade match and elevate costs, underscoring the causal link between data-driven precision and mitigation. In aggregate, these dynamics foster a more efficient , where fewer irrelevant ads clutter digital spaces, potentially alleviating user and ad while preserving competitive pressures that could pass cost savings to consumers through stabilized or reduced product prices. However, outcomes depend on accurate signals; miscalibrated targeting can inadvertently amplify waste, though robust from controlled studies affirms net gains in and under standard implementations.

Support for Free Digital Content

Targeted advertising sustains free by enabling platforms and publishers to monetize user attention more efficiently than contextual or non-targeted methods, thereby offsetting production and distribution costs without requiring direct payments from consumers. In , U.S. digital ad totaled $259 billion, with targeted formats contributing disproportionately due to their higher return on ad spend, allowing services like search engines, , and news outlets to remain accessible at no cost. This model aligns with consumer preferences, as 85% of U.S. adults favor an ad-supported providing free content over a subscription-based system where access is paywalled. Surveys further reveal that 86% of users acknowledge as the funding mechanism for free online services, with 80% preferring ads to personal payments and viewing unrestricted access as a social benefit, particularly for lower-income households. By increasing ad and click-through rates, targeted approaches reduce the volume of ads needed to generate equivalent , minimizing user disruption while supporting content creation; for instance, publishers can forgo aggressive paywalls that would otherwise exclude non-subscribers. Empirical valuation places the annual benefit of such free, ad-funded services at approximately $1,400 per American user. Limitations on targeting, such as bans or restrictions, diminish ad efficacy and publisher earnings, potentially eroding the free content ecosystem and widening access disparities.

Promotion of Competition and Innovation

Targeted advertising lowers for smaller advertisers and new market entrants by enabling precise audience segmentation at lower costs compared to traditional broad-reach media, which often requires substantial budgets for exposure. This efficiency allows small and medium-sized businesses (SMBs) to compete with larger firms by minimizing ad wastage and focusing spend on high-intent consumers, with SMB advertisers allocating 67% of their budgets to digital formats including search, social, and display ads. For instance, digital targeting facilitates product launches or entry by matching ads to interested users, reducing overall expenses and intensifying as firms vie for better matches. Empirical data underscores enhanced , as 58% of small businesses now rely on digital channels for , reflecting growth in their participation driven by targeted tools that yield measurable returns without advantages held by conglomerates. Behavioral targeting, in particular, boosts click-through rates by 5.3 times over non-targeted ads, empowering SMBs to achieve comparable to larger advertisers and expanding overall advertiser diversity in online markets. This democratization contrasts with legacy media, where fixed costs for TV or print slots disproportionately favored incumbents, and supports a broader where 45% of small businesses plan increased digital ad investments amid proven attribution exceeding $50,000 annually for over half of users. The mechanism fosters innovation by incentivizing advancements in ad technologies, such as programmatic buying and (RTB), which automate efficient ad delivery and comprise over 50% of display ad revenue streams. Platforms innovate in data analytics and matching algorithms to capture advertiser spend, while the targeted model sustains free content ecosystems, spurring creative ad formats and that refine consumer engagement without inflating inventory costs. Consequently, improved targeting correlates with heightened inter-platform rivalry, as evidenced by escalating investments in AI-driven , which enhance match quality and drive iterative tech upgrades across the ad tech stack.

Criticisms and Risks

Privacy and Data Security Issues

Targeted advertising relies on the aggregation of extensive , including browsing histories, geolocation, device identifiers, and inferred interests, collected via , pixels, and SDKs embedded in apps and websites. This process often involves sharing with hundreds of third-party intermediaries in (RTB) auctions, where user profiles are bid on in milliseconds, exposing identifiers like IP addresses and device IDs to bidders without robust anonymization. Studies have documented vulnerabilities in these systems, such as unencrypted transmissions and observable data leaks, enabling external parties to reconstruct user profiles from auction metadata. Data security risks materialize through breaches and unauthorized access in ad tech ecosystems. For instance, RTB protocols have been shown to inadvertently disclose sensitive attributes, with empirical analyses revealing that up to 94% of bids in some systems contain traceable , increasing re-identification risks. In 2022, the U.S. (FTC) charged (now X) with deceptively using users' two-factor phone numbers—intended for —to build ad targeting profiles, affecting millions without disclosure, resulting in a settlement requiring enhanced practices. Broader ad tech exposures, such as attacks on ad networks, have facilitated campaigns; between 2020 and 2024, incidents like the 2023 MGM Resorts breach indirectly tied to ad-related access compromised 10.6 million guest records, highlighting third-party vulnerabilities. Regulatory scrutiny underscores these issues, with the FTC's September 2024 staff report on and video streaming platforms documenting "vast " for targeted ads, including cross-app tracking and data retention exceeding necessity, recommending limits on such practices to mitigate harms like and . Empirical surveys of mobile targeted advertising identify persistent risks from fingerprinting techniques that evade deprecation, with 2022 analyses showing over 70% of apps leaking data to trackers despite user opt-outs. While platforms claim aggregated data usage, causal evidence from protocol dissections indicates frequent non-compliance, amplifying security threats in an environment where ad tech handles billions of daily transactions.

Potential for Misuse and Manipulation

Targeted advertising's reliance on granular enables actors to craft messages that exploit individual psychological profiles, cognitive biases, and inferred vulnerabilities, potentially influencing decisions in ways that evade conscious scrutiny. For example, by analyzing behavioral signals like browsing history or social interactions, advertisers can infer traits such as or financial stress, tailoring content to heighten emotional responses and reduce rational evaluation. indicates that such activates persuasion knowledge gaps, where consumers underestimate manipulative intent, leading to higher engagement rates compared to non-targeted ads. In political applications, amplifies these risks by directing customized appeals to narrow voter segments, as demonstrated in the 2016 U.S. presidential campaign where harvested data from over 87 million profiles to deliver psychographic ads aimed at swaying undecided individuals through fear- or identity-based triggers. Field experiments confirm targeted political messaging increases or policy support—by approximately 70% when matched to a single attribute like partisanship—though layering multiple traits offers no marginal persuasive edge over simpler targeting. This efficacy persists even when users are warned of microtargeting, suggesting inherent vulnerabilities in human response to tailored content that could undermine democratic deliberation if scaled by sophisticated campaigns. Commercially, misuse manifests in predatory practices, such as directing high-risk financial products to users exhibiting distress signals or unhealthy consumables to demographics like adolescents, thereby capitalizing on developmental or situational weaknesses. Studies on reveal it can exacerbate filter bubbles, isolating users in echo chambers that reinforce preexisting biases and limit exposure to diverse viewpoints, with longitudinal showing reduced behavioral adaptability over time. While platforms impose restrictions on sensitive targeting categories, data brokers' opaque inferences—such as deducing conditions from search patterns—persist, enabling indirect exploitation absent robust verification. Overall, these dynamics highlight causal pathways from to behavioral sway, tempered by evidence that overt manipulation often underperforms subtle, relevance-masked appeals in sustaining long-term influence.

Evidence on Actual Harms vs. Perceived Threats

Empirical studies indicate that while targeted advertising elicits widespread concerns regarding manipulation, erosion, and inflated prices, quantifiable evidence of substantial net harms remains sparse and often counterbalanced by efficiency gains. A nine-year on involving users across 13 countries found no significant difference in median willingness-to-accept compensation for ad exposure versus ad-free access, with values of $31.95 and $31.04 per month, respectively; the 95% excluded disutilities exceeding 10% of baseline platform value, suggesting minimal welfare detriment from ads, including targeted variants. This aligns with broader economic models positing that targeted ads reduce search costs and match to relevant products, potentially enhancing surplus despite informational asymmetries. Some research identifies specific frictions, such as targeted ads directing users to higher-priced options—7.9% above minimum search equivalents for identical products—or lower-quality vendors, as measured by ratings in a controlled experiment with 487 participants. However, these ads still outperformed random alternatives in perceived and purchase intent, implying that harms may stem more from imperfect targeting than inherent malice, with net effects ambiguous absent broader context like . Claims of pervasive manipulation, such as inducing purchases, lack robust causal quantification; instead, heightened click-through rates (up to 2-3 times contextual ads in meta-analyses) reflect improved informational value rather than . Perceived threats, particularly around , dominate public and regulatory discourse, with surveys consistently reporting discomfort over surveillance-like profiling—yet actual incidents of harm, such as directly tied to ad targeting, occur infrequently relative to volume processed. For instance, while breaches like (2017, affecting 147 million) highlight risks in ecosystems underpinning targeting, attribution to ad-specific misuse is rare, and consumer opt-outs or regulations like GDPR have not demonstrably reduced fraud rates post-implementation. In contrast, restricting targeting correlates with tangible consumer setbacks: a study of Apple's 2021 changes, approximating a ban, revealed a 16.7% drop in app feature updates and 36.3% fewer new game releases annually, disproportionately impacting free, ad-supported content availability for undiversified developers. This suggests that perceived gains may impose indirect harms via diminished innovation and content subsidies, underscoring a gap between subjective unease and objective welfare metrics. Academic emphasis on potential downsides, potentially amplified by institutional preferences for interventionist frameworks, warrants scrutiny against these null or positive empirical signals.

Regulatory Landscape and Future Trajectories

Major Regulations and Compliance

The European Union's (GDPR), effective May 25, 2018, governs targeted advertising by mandating explicit consent or another lawful basis for processing used in behavioral profiling and ad delivery, with Article 21 granting individuals the right to object to such processing. The (DSA), fully applicable from February 17, 2024, prohibits very large online platforms from targeting advertisements at minors if they are aware of the user's age with reasonable certainty, while requiring transparency in ad recommender systems, including disclosure of targeting parameters and data sources. Complementing this, the (DMA), effective March 7, 2024, bars designated "gatekeeper" platforms from combining across core services for advertising without user consent and restricts off-platform data use for targeted ads on their services. In the United States, the , amended by the and effective January 1, 2023, empowers consumers to of the "sale" or "sharing" of personal information for cross-context behavioral advertising, with updated regulations approved on September 22, 2025, enhancing requirements for opt-out signals like Global Privacy Control (GPC). The enforces Section 5 of the FTC Act against unfair or deceptive targeted advertising practices, including inadequate disclosures of collection, as seen in ongoing actions against data brokers and ad tech firms for privacy misrepresentations. By 2025, at least 18 states have enacted comprehensive privacy laws mirroring CCPA elements, such as Colorado's restrictions on targeted advertising without opt-in for sensitive inferences, imposing similar mandates and fines up to $20,000 per violation. Compliance with these regulations necessitates robust consent management platforms to obtain granular, freely given for in targeted ads, alongside mechanisms for easy opt-outs and data access requests under GDPR's Article 15. Advertisers must conduct impact assessments for high-risk profiling, pseudonymize or anonymize where feasible to minimize reliance on identifiable , and maintain trails for ad targeting logic to demonstrate accountability. Enforcement has intensified, exemplified by a September 9, 2025, coordinated sweep by and other state attorneys general targeting non-honoring of GPC signals in ad , resulting in settlements and mandated upgrades to tools. Globally, laws like Brazil's LGPD (effective 2020) impose analogous rules for targeted ads, with penalties up to 2% of Brazilian , underscoring the need for geofencing and jurisdiction-specific compliance frameworks.

Emerging Technologies (AI and Privacy Tools)

Artificial intelligence (AI) has advanced targeted advertising through enhanced and hyper-personalization, enabling advertisers to forecast consumer behavior with greater accuracy. algorithms analyze vast datasets to segment audiences based on behavioral patterns, preferences, and real-time interactions, improving ad relevance and click-through rates by up to 20-30% in programmatic platforms. For instance, generative AI tools automate ad creative generation, tailoring visuals and copy to individual users, as seen in platforms like Meta's AI-driven campaigns that dynamically adjust content for engagement. These technologies leverage models trained on historical data to predict purchase intent, reducing ad waste while boosting return on ad spend (ROAS). However, escalating privacy regulations, such as the EU's GDPR and emerging laws, have prompted the integration of (PETs) to sustain targeted advertising without raw exposure. emerges as a key method, allowing ad tech firms to train shared models across decentralized devices—such as smartphones—where only model updates are aggregated centrally, preserving user locality and minimizing breach risks. This approach, implemented in systems like those explored by for mobile ad ecosystems, enables audience modeling for bidding without transferring personal identifiers, with studies showing viable accuracy in non-IID distributions when combined with noise addition. Differential privacy complements these efforts by injecting calibrated statistical noise into datasets or queries, ensuring individual contributions remain indistinguishable while maintaining aggregate for targeting. In ad tech, this technique supports cohort-based advertising, as in Apple's App Tracking Transparency framework adaptations, where parameters (e.g., ε=1-10) balance guarantees against model degradation of 5-15% in metrics. (SMPC) further enables collaborative signal sharing among advertisers and publishers without revealing inputs, facilitating clean-room environments for cross-device graph building. Empirical research indicates PETs like these reduce user-perceived violations in ad delivery by enabling pseudonymous targeting, though real-world efficacy hinges on rigorous parameter tuning to avoid under-protection. Complementing industry-adopted PETs, users can reduce exposure to targeted advertising through practical measures such as accessing platforms via web browsers rather than mobile apps to limit permissions granted to trackers, disabling location services to curb geodata collection, and employing VPNs to mask IP addresses alongside ad blockers to prevent loading of tracking scripts and behavioral profiling. Hybrid AI-PET frameworks are gaining traction, such as adaptive in federated setups for asynchronous ad model training, which dynamically adjusts noise levels to optimize for heterogeneous data sources in real-time auctions. Industry adoption, including by platforms like Viant, projects these technologies will underpin cookieless ecosystems by 2026, potentially restoring 70-80% of pre-privacy-loss addressability through privacy-safe signals. While promising causal reductions in data leakage—evidenced by formal proofs of indistinguishability in implementations—challenges persist in scaling computational overhead, which can increase training times by 2-5x without hardware optimizations. Overall, these innovations reflect a pivot toward privacy-by-design in AI-driven , prioritizing empirical budgets over traditional models.

Projections for 2025 and Beyond

The global digital market, which heavily relies on targeted strategies, is projected to expand from USD 488.4 billion in 2024 to USD 1,164.25 billion by 2030, reflecting sustained demand for data-driven despite constraints. Programmatic , a core mechanism for targeting, is expected to account for 84.9% of ad revenue by 2030, driven by and segmentation efficiencies that minimize ad waste through empirical matching of user interests to products. , often targeted via query intent, anticipates a compound annual growth rate of 8.89% from 2025 to 2030, reaching USD 543.65 billion, as platforms leverage first-principles user behavior signals over deprecated third-party cookies. Google's October 2025 termination of the initiative signals a pivot away from alternatives to cookies, potentially easing immediate disruptions to cross-site targeting while accelerating reliance on AI for predictive modeling from aggregated, anonymized datasets. This shift aligns with broader adoption of AI tools for hyper-personalized ads, where infers preferences from first-party data and contextual cues, enabling causal linkages between content and conversions without granular tracking—forecasts indicate AI integration could boost ad ROI by 20-30% in privacy-compliant environments. However, from phased cookie restrictions shows minimal long-term revenue erosion for major platforms, as advertisers adapt via server-side tracking and zero-party , sustaining targeting efficacy. Regulatory pressures will intensify, with U.S. states enacting comprehensive privacy laws effective 2025 onward—four new laws launched January 1, 2025—mandating opt-in consent for behavioral targeting and limiting data sales, potentially curbing cross-device profiling but spurring innovation in consent management platforms. In the EU, the AI Act's risk-based framework, fully applicable by 2026, will scrutinize high-risk ad algorithms for bias and transparency, favoring verifiable, low-inference models over opaque black-box systems. Overall, targeted advertising's trajectory points to resilient growth at 9-12% CAGR through 2030, predicated on causal realism in data utility outweighing perceived harms, with industry adaptations like contextual AI targeting mitigating compliance costs estimated at 5-10% of budgets.

References

  1. https://www.[researchgate](/page/ResearchGate).net/publication/221023382_How_much_can_behavioral_targeting_help_online_advertising
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