Companies have technology that allows them to gather information about web users. 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.
Search engine marketing 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. 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.
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'. The ad quality is affected by the 5 components of the quality score: • The ad's expected
click-through rate • The quality of the
landing page • The ad/search relevance • Geographic performance • The targeted devices 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.
Social media targeting 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. For example,
Facebook collects massive amounts of user data from surveillance infrastructure on its platforms. 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. 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).
Television Advertisements can be targeted to specific consumers watching
digital cable,
Smart TVs, or
over-the-top video. Targeting can be done according to age, gender, location, or personal interests in films, etc. 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. This allows advertisers to produce advertisements that could cater to their schedule and a more specific changing environment.
Content and contextual targeting 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.
Technical targeting 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.
Time Targeting 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. 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 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.
Sociodemographic targeting Sociodemographic targeting focuses on the characteristics of consumers. This includes their age, generation, gender, salary, and nationality. This data can be harnessed from applications on the device (mobile apps like
Uber) that allow access to the location information. 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.
Behavioral targeting Behavioral targeting is centered around the activity/actions of users and is more easily achieved on web pages. 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. 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. While behavioral targeting can enhance ad effectiveness, it also raises privacy concerns. 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.
Onsite 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, online advertising with leading online ad servers, 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%.
Network 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. 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. 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 the user to them, the websites visited by the user after them, ads seen by the user, and ads being clicked on. 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. 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 the user's 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 people would be more likely to click on. 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. 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 ==