AI Targeted Advertisement

The majority of web users have come across “personalized” news feeds, personally recommended material, customized advertising, and tailored “sponsored articles,” which are all typical elements of users’ daily web experiences. In most cases, algorithmic profiling is essential to targeting: in order to determine what is of “personal relevance,” users’ behaviors, click-throughs, inferred identification indicators, and other signals must be profiled and categorized. Gender, age, ethnicity, lifestyle and consumption preferences, language, facial recognition, location, political leanings, music and film preferences, wealth, credit status, employment history, home ownership, relationship status list goes on — are just a few of the categories that targeting mechanisms use to profile people. Through the creation of “like-to-like users,” who are aggregated with and compared to other user groups, these profiles are made profitable and valuable.

AI are used in today’s advertising to target and distribute adverts in real time throughout the internet. Though you might not always notice it, AI is essential to the infrastructure that supports the advertising goods on many platforms. AI is frequently used by contemporary programmatic systems to control real-time ad buying, selling, and placement.

In fact, artificial intelligence is used by all digital ad platforms and exchanges to control the real-time buying and selling of advertising. Programmatic exchanges, third-party networks, and advertising on social media sites like Facebook, Instagram, and Snapchat are all included in this.

I’m sure you have heard how Target’s coupon algorithm sent baby items coupons to to high school girl leading the the child’s father to go to the Target store(Hill):

These advertisement AI have been trained to a level that they are able to predict future habits based on previous training data; small changes in behavior can be used by these AI to predict what you would likely purchase in the future.

With 80% of frequent buyers indicating they only do business with brands that tailor their experience, making ads more relevant and personalized is quickly becoming a major focus. Consumers expect all of their interactions with brands or vendors to be individualized, with 47% saying that brands should do a better job of matching their engagement activities with their preferences(IBM).

Advertisers can enhance their relationships with customers, strengthen their bond with their brand, and improve the purchasing experience by using a customized AI solution like conversational marketing. Using an AI conversational marketing solution to engage with consumers and prospects has grown in popularity since 71% of customers expect businesses to speak with them in real-time. Brands benefit from personalized experiences by increasing return on investment and fostering stronger bonds with customers(IBM).

In the recent years, there has been a rise in public and governmental pressure to ensure that platforms use data targeting in a responsible manner. The use of Facebook’s audience-selection tools to “wrongfully target” particular groups or “wrongfully exclude” certain groups from seeing ad material is prohibited, according to a new anti-discrimination policy that was established in 2019. Google also discourages the use of extremely narrow or specific audiences and prohibits tailored advertising based on a user’s core or intrinsic self-identity, belief systems, or personal struggles(Kant). Additionally, the corporation has introduced stronger political advertising regulations that limit microtargeting and declared a shift from “individualized targeting” to “group targeting.”

What does “wrongful targeting” entail in this context? In some sense, all types of targeting could be viewed as exclusionary because targeting naturally entails categorizing and separating audiences. Thus, some degree of discrimination is hard-coded into audience targeting.

Therefore, determining audience identities “correctly” is a challenging undertaking that is inherently entwined with sociocultural, economic, and cultural prejudices. But what if trying to get user identity “right” is the incorrect perspective? Professor of Cross-Platform Media Carina Westling contends that platforms are not just able to comprehend their users by looking at their demographics and identification markers to predict audiences. She suggests that platforms should comprehend online users through their collective, dynamic agencies — groups of users who move around and make decisions in a (digital) space — rather than through their qualities as identity-marked, individual agents(Kant). This strategy entails platforms managing or valuing their audiences as dynamic flows rather than objects with fixed demographics.

Marketing initiatives can be more easily tailored to a specific audience thanks to AI. Marketers can make sure they are sharing the appropriate creative with the appropriate audience at the appropriate time. AI also makes it simpler for marketers to scale their initiatives. However, there are certain drawbacks to be aware of. Although personalization and targeted advertising have numerous benefits, they can also seriously infringe on customers’ right to privacy and be discriminatory to a degree.

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