Predatory advertising
Predatory advertising, or predatory marketing, can be largely understood as the practice of manipulating vulnerable persons such as children, or adults with cognitive issues into unfavorable market transactions through the undisclosed exploitation of these vulnerabilities. The vulnerabilities of persons/populations can be hard to determine, especially as they are contextually dependent and may not exist across all circumstances. Commonly exploited vulnerabilities include physical, emotional, social, cognitive, age, and financial characteristics.
Predatory marketing campaigns may also rely on false or misleading messaging to coerce individuals into asymmetrical transactions. The history of the practice has existed as long as general advertising, but particularly egregious forms have accompanied the explosive rise of information technology. Massive data analytics industries have allowed marketers to access previously sparse and inaccessible personal information, leveraging and optimizing it through the use of savvy algorithms.
Some common examples include for-profit college industries, "fringe" financial institutions, political micro-targeting, and elder/child exploitation. Many legal actions have been taken at different levels of government to mitigate the practice, with various levels of success.
Vulnerable populations
Predatory advertising depends, in large part, on the deliberate exploitation of individuals based on specific traits, life circumstances, or membership within certain groups. The "vulnerabilities" created by these characteristics are context-dependent, meaning they vary between markets and transactions. In other words, an individual with some or any of these traits is not rendered universally vulnerable within the marketplace. Furthermore, not all marketing or advertisements targeting these traits are necessarily "predatory," as the condition for the practice relies primarily on the intent of the advertiser. This distinction can be especially opaque given marketing's natural tendency—even within ethical bounds—to identify the "pain points" of potential consumers. Nonetheless, it can be helpful to delineate the most common forms of vulnerability. Some of the most common avenues of exploitation are:- Physical Vulnerability, wherein certain biological or physiological traits render an individual less likely to engage in market transactions from a fair position. Examples of this may include the targeting of overweight individuals with ineffective weight loss supplements, or the advertisement of unregulated "medical" devices to those suffering from degenerative or other painful diseases.
- Cognitive Vulnerability, wherein cognitive deficiencies render an individual unable to fully comprehend and process advertising information that may be deceptive or manipulative. Examples of this are not limited to the cognitively disabled, and may include advertising that targets minors or the elderly.
- Motivational Vulnerability, wherein certain individual traits or extraordinary personal circumstances may inhibit a person's ability to resist or properly negotiate certain market advances. Examples of this may include the advertisement of price-inflated funeral services to freshly grieving individuals
- Social Vulnerability, wherein the social circumstances of an individual greatly increases their propensity to engage in unfavorable transaction. Examples of this include the marketing of for-profit colleges to combat veterans struggling to find gainful employment.
- Emotional Vulnerability, wherein the emotional states of individuals—temporary or persisting—are leveraged by advertisers to sell products that purportedly address these emotional ills. This avenue of exploitation has become especially pertinent as marketer access to data on individual users has become increasingly comprehensive, and algorithms have been able to return relevant advertisements in almost real-time.
- Economic Vulnerability, wherein an individual's economic circumstances either limits their ability to engage in alternative market transactions, or increases the chances they will be susceptible to other predatory marketing schemes. Examples of this include the marketing of high-interest payday loans to financially unstable individuals, who may have no other options.
Deception tactics
False statements
These include claims or presentations that are demonstrably false, often statistics or other empirical claims. For example, a for-profit college claims "98% of our graduates find employment within one month of graduation!" when in fact this is untrue.Omission
Statements made about a product or service which fail to include material information that is relevant to the claim being made. For example, a commercial suggests that "clinical trials have proven the effectiveness of a product" when in fact the clinical trial measured the effectiveness of the product in a different context or metric that the one being advertised.Implication
Statements that are made which may be true, but which are intended to lead the consumer to reach erroneous inferences. These may capitalize on a lack of information about the product or service, or the contextual environment of the consumer. They can be further classified as:- Ambiguous statements or claims, which utilize unclear language or narratives to suggest product superiority. For example, a claim is made that the product is a much "better" alternative to a similar product, but there is no metric for "better."
- Atypical statements or claims, which cite results of product utilization that fall well outside of the normal outcome. For example, a diet pill company claims you can lose up to 30 pounds in one month, when the result is both unusual and/or achieved by other methods.
- Conjectural statements or claims, which lack substantive evidence or cannot be made with certainty. For example, a commercial promises a "100% satisfaction guarantee" despite its being impossible to ensure.
- Manipulative statements or claims, which cite characteristics of the product or service that may not differentiate it from market standards, but create an illusion of product superiority. For instance, a sugar soda may highlight that it is fat-free, when in fact all sodas contain no fat content.
Accessing personal information
Data collection
The explosive growth of information technologies throughout the 21st century has brought with it entirely new privacy concerns, especially surrounding the collection and usage of personal data. As reliance on digital platforms has become almost necessary for participation in modern life, individuals have been asked to relinquish large amounts of personal information, either through direct submission or by inference from user engagement. Although access to personal information is generally agreed upon by participants, as outlined in end-user permissions agreements, questions of informed consent have brought forth numerous legislative efforts, including propositions to increase clarity in consent forms, as well as efforts to establish clear bounds of data usage.The commodification of this data, which is highly valued across a number of sectors, has driven the exponential rise of a "data brokering" industry. Barring established industry norms and regulations, such as those in healthcare, finance, or other similarly protected sectors, data collected by individual entities like
or Facebook, as well as that collected by third party brokerage agencies such as Acxiom, can have a wide range of applications. Though many of these are relatively benign or even positive, often being utilized to tailor personalized user-experiences, the availability of such data to unethical marketers has inflamed problems of predatory advertising.
Data extraction and aggregation occurs over a vast network of platforms and businesses. Much of the information originates from discrete sources, including social media engagement, loyalty programs and purchasing history from online retailers, web browser queries, government records, and mobile application usage and preferences. Information gathered consists of many personal data points, ranging from available payment methods to health conditions. In the case of large technology platforms, especially for whom a large part of the revenue stream is composed of ad sales, this information may often be sold—either directly to advertisers or to third party brokerage firms. These firms specialize in the aggregation and categorization of data from a number of sources, which is then sold on the market to advertisers and other interested parties.
The process of categorization is especially important to understanding the avenues of exploitation made possible by comprehensive data aggregates. A 2013 report by the Federal Trade Commission found that data brokerage companies compiled individuals into groups with labels such as: "Zero Mobility," "Credit Crunched: City Families," "Rural and Barely Making It," "Enduring Hardships," and "''Tough Start: Young Single Parents."''
Algorithmic targeting
Whereas information pertaining to consumer vulnerabilities has been inferred through proxies for some time, such as the targeting of certain demographics based on specific television viewership, the drastic increase of direct access to information around the individual—especially coupled with methods of direct-to-consumer personalized advertisements—has intensified the accuracy and potency of predatory advertisement campaigns.This information then allows advertisers to engage in online behavioral targeting, wherein advertisements are delivered to individuals based on personal information previously extracted from various sources. Complex algorithms, coupled with the aggregation of previously discrete data, have allowed advertisers to not only target increasingly precise individual characteristics, but also to draw inferences about individuals based on statistical corollaries requiring massive data sets. One consequence of this is that traditionally protected information, such as health outcomes, race, or private financial histories, can be inferred with greater certainty without ever collecting data on the specific item in question.
Once data has been collected, aggregated, and categorized, the connection between advertiser and consumer can be made. These are often fostered by intermediaries such as DoubleClick, a Google-owned company that offers marketers a wide range of websites to display their advertisements. The use of these intermediaries relieves websites of having to sell individual ad space, allowing algorithms to instead display personalized ads to users based on a complex mix of desirable metrics. This practice has sometimes been called "micro-targeting." While this process optimizes the ability to provide users with an individualized experience, it alleviates much of the culpability traditionally placed on ad-revenue dependent platforms to monitor their ad placements. Furthermore, when the algorithms are built using grouping labels such as those listed in the previous section, advertisers looking to target and exploit specific characteristics can easily reach the most vulnerable populations.
It's important to note that the use of algorithms may result in such targeted advertisement despite being built without any malicious intent. Those utilizing Machine Learning will "train" themselves to display advertisements that result in user-engagement based on prior interactions, which may reinforce and increase the rate at which vulnerable populations receive advertisements that "speak" to those vulnerabilities.