Current Situation

Media planning is the practice of placing advertisements on chosen platforms and channels to catch the right people at the right time with the right message. Being just one part of the larger, constantly changing advertising field, how media planning has evolved throughout the past has mirrored the larger media and advertising trends at play. The practice of media planning is driven by data; instead of playing a guessing game, advertisers use data about how consumers interact with media and the world to determine many aspects of their media plans.

Data comes in handy in multiple steps of the process. It can help planners determine the demographics of who is buying their product most, or even psychographics of who would be most easily swayed to buy the products advertised. After determining target audiences, data can be used to inform the selection of which media channels are best suited to reach them. If only a small percentage of a target audience watches, for example, broadcast television, then media planners can use that data to understand that it would not be an effective channel to reach them. This leads into budget allocation, which goes hand-in-hand with the types of data used to select channels. Media planners allocate budget according to the percentage of their target audiences that are on certain channels and how often they can be found there. Data can also be used as KPIs, or Key Performance Indicators, to let the media planners know how the campaign is going; these KPIs can then be used to adjust media channels and budget to get a different outcome.

As we shift towards a more digital and integrated marketing focus with the introduction of more platforms where media can be placed (for example, social media, streaming, and online ads are all different iterations of digital), the way professionals approach media planning has also changed. According to Media Planning: A Brand Management Approach, it is not enough to just plan advertising media in today’s communications world (2). Due to this, the book claims that media planning has become brand communications planning due to four specific shifts.

This first shift is planners gaining a broader role in the media process since they now take on more than simply advertising support. While in the past, the advertising itself was created first (usually a simple print piece) and then the media planning was done after, this has changed with the convergence of data and technology resulting in the importance of truly understanding channels. In recent years, there has been an understanding that media planning is just as important as the creative aspect of advertising, because there is no way you can make someone interested in your product if they don’t even see the ad in the first place.

The second shift is that instead of simply reaching an audience with an ad and gaining impressions, the goal nowadays is to influence people to do something. This, of course, depends on the goal of the campaign; it can be anything from awareness to participation in an online activation.

The third shift is multimedia strategy to multichannel strategy. Consumers’ media behaviors have become much more fragmented- instead of spending one hour watching prime-time television and one hour reading the newspaper per day, they now spend multiple hours across different devices, and further they split their time on each device between different apps or platforms. Each of these platforms play a different role in their life. For example, if your target audience goes on TikTok to watch quick videos when their attention span is low, a 30-second ad spot will most likely not break through to them in any meaningful way. Media planners know this, and that knowledge is needed throughout the creative process so the content created can align with the placement.

The fourth is the shift from placing only paid advertising to impacting multiplatform content. These days, anything can become a medium where advertising can live. It is no longer simple paid advertising spots- it’s much more complicated. Further, the globalization through online media makes media planners have to think about which platforms will best reach their audiences across the world. With this comes the idea of cultural context. An ad that may be seen as funny in one region may be seen as tasteless in another, and advertisers must know how to navigate this.

Looking at this through the lens of C-scape: Conquer the forces changing business today, the current state of advertising, specifically media planning, must account for the changing identity of all that we know as media. Consumers have changed, which both causes and is caused by the changes already outlined above. They have adjusted to the sheer amount of media that is in the space today. People are exposed to around 5,000 ads per week, whereas in the early days of media planning, this number would be much lower (Media Handbook). Further, the average person spends 145 minutes per day on social media, during which they are exposed to countless pieces of media, including personal posts, sponsored posts, and paid advertisements (Forbes).

This brings us to the convergence of content. The types of content that media planners may use have begun to overlap. There is a much higher stress on the idea of paid, owned, and earned media, and how these three types of media play into each other. In the past, earned media was just that, but with influencer marketing on social media, every piece of earned media is an opportunity to further cultivate a brand online. There are so many new channels to view advertisements: phones, gaming consoles, virtual reality, etc. With these, it is so much easier for users to create their own content and companies to use this earned content as part of their media plan. So, brands have much more content to work with, and therefore many more options of where to place their advertisements.

While in the past, people had to wait each week to watch the new episode of their favorite television show or watch movies in the physical theater when they were released, consumers now have so much more control over how and when they view their media. Streaming services have given rise to the “binge-watching” culture, which allows consumers to feel more connected to their content and produces more data on what the consumer is watching to potentially be used in advertising.

Due to these ways that the landscape is changing, the digital space is becoming so oversaturated that consumers are consuming more content but interacting with it less. What we once knew as social networks, which put a focus on the connections users made, is now social media, with users consuming content more than they interact with others. Even though the average usage time has steadily risen over the past decade, the average clickthrough rate on social media was 1.3% in 2021, but it decreased to 1.21% in 2022 and looks as though it will continue to decrease.

Due to this oversaturation of digital media, advertisers are looking to out-of-home options to really make an impact on consumers in a way that will not get lost in the digital noise. Out-of-home (OOH) advertising refers to, as the name suggests, advertising that is placed outside of the home. This can include billboards, outdoor posters, experiential advertising that is located on streets, or any other advertising that is in public spaces. It is important to note that OOH advertising can be digital, it just cannot be located on consumers’ personal devices.

Prediction of New Trend

The use of AI surveillance technology has become a common technology used specifically in the military and government industries. It has a negative connotation, specifically for Western Democratic countries, due to the fact that these technologies are most famously used in countries like China for civilian surveillance. In reality, there are many uses for these types of technologies; as of 2019, at least seventy-five out of 176 countries globally were actively using AI technologies for surveillance purposes. These uses include smart city/safe city platforms (fifty-six countries), facial recognition systems (sixty-four countries), and smart policing (fifty-two countries) (Feldstein 7).

Further, liberal democracies are more likely to employ AI surveillance technology than closed autocratic states, at 51% and 37%, respectively (Feldstein 9). This goes to show that it is less connected to the type of government that is utilizing the technology and more connected to the goals of government structures themselves. In early 2023, France passed a controversial bill that will allow them to use this technology heavily to monitor and scan for potential threats in crowds at the upcoming 2024 Paris Olympics.

It is fair to have concern over the way that governments are deploying these technologies. While in the United States, companies like IBM and Cisco are creating and exporting surveillance AI to other democratic countries, Chinese AI is largely exported to other autocratic countries with similar government structures and goals. These technologies come from companies such as Huawei, Hikvision, Dahua, and ZTE. China supplies AI surveillance technology in sixty-three countries, which are most commonly autocracies and weak democracies. These countries are more likely to import Chinese AI surveillance technology in times of political unrest; pointing to the fact that they are using this technology to monitor what citizens are doing with respect to consolidating political control within a state.

Further than the simple idea of surveillance in government situations, there are very specific ways these systems work. When researching AI surveillance, one concept came up time and time again: soft and hard biometrics. Biometric data is unique human characteristics that can be used for automated recognition- this can include biological and behavioral characteristics. Soft biometrics, while including information such as hair color, height, age, gender, and more, it does not include enough information to identify the subject. Hard biometrics, on the other hand, include identifiable characteristics such as fingerprints or detailed facial data. Soft biometrics can be obtained from large distances without subject cooperation; hence, they become beneficial in the case of mass surveillance of public spaces (Science Direct).

Beyond the collection, there are so many ways to use this biometric data. Machine learning algorithms can give us large-scale data for how people with certain features interact with the world- where they go, what they do, etcetera. According to the United States Department of Homeland Security, the U.S. Government uses hard biometric data for identity assurance; anything from linking a person to criminal evidence to confirming someone’s identity.

One branch of soft biometrics is the idea of Emotional AI. This is the act of using technology to “sense, learn about and interact with human emotional life.” This practice stems from the field of affective computing in the 1990s, which allowed computers to identify certain emotions based on facial features, gestures, and vital signs of humans. The way that computers learned about this was through a process called machine learning, where named outputs are given to certain inputs until the computer effectively learns how to identify them.

In considering the capabilities of these technologies, it is easy to see how they can soon be introduced into the media world, specifically advertising. Advertising is all about the data that helps them understand how to get an audience to do what they want. The first step to connecting with a target audience is knowing where to find them and how to break through to them, and AI surveillance can provide this data.

Using surveillance AI to monitor what people do and where they go is not a novel idea. Neither is using AI to understand a human’s emotions or behaviors. But how could these two things come together in the media planning space? And why do I foresee this happening?

As mentioned before, media planning relies on an understanding of how consumers interact with media and more broadly, the world around them. As the digital space becomes more and more oversaturated with content, coupled with the fact that content was forced to be online during the COVID-19 pandemic, the content that truly makes an impact on consumers exists in the physical space.

So, what better way to understand the consumer journey than observing it, in real time, while gaining insights about consumers throughout with data gained from AI surveillance? Think of it like web cookies, but in real life. Cookies track where a consumer has visited online to better understand their lifestyle: if they visited websites about dog care, they probably have a dog. If they are browsing for winter coats, they probably live or are going somewhere that is cold. But what if there was a way to understand that consumer journey, except it was in real life? AI surveillance technology can give advertisers that information.

A specific American company comes to mind when thinking of the intersection between AI surveillance and media planning: IBM. They currently have both AI-powered threat detection and security technologies. While they haven’t broken into the advertising space with this specific technology yet, they have many technologies that they use to analyze data in the marketing space. They have already begun to infuse AI into other tools they have, such as their marketing analytics solutions. For this, they use data from the campaign, such as spend vs. revenue, to adjust marketing plans in real-time. In my opinion, IBM could be the big player in fusing the practices of AI surveillance and marketing insights from data together. They already have access to both industries, and unlike China, it is in their personal best interest to bring the two together rather than solely focusing on the security implications.

Another player that I can foresee becoming important in this sphere is data protection regulations, specifically the EU regulations. The EU is a leading player in the global technology realm while also imposing some of the most stringent regulations. The EU General Data Protection Directive (GDPR) protects personal data, or in other words, data that could identify a person. This act stresses the importance of explicit consent when personal information is at play, but this would only be true in the case of hard biometrics- pieces of information that could identify consumers. This is specifically important when talking about out-of-home analytics that do not use personal devices when tracking people.

So, while I have laid out the possibility of the future integration of AI surveillance into the media planning space, specifically to aid the shift away from the mitigating impact of digital marketing, there is no way to be sure about any outcome. I’ll be using the scenario method outlined in Futuring: The Exploration of the Future to explore some of the ways this could play out.

What if this AI technology from the government and military space does not end up crossing over into the media space further? I would consider this a surprise-free scenario. While there is a crossover in the goals of both industries regarding this surveillance technology, we have not seen this connection being made yet, so it may continue along the same path.

Taking an optimistic approach, it is possible that some of this data will be considered while understanding the customer decision journey. Maybe governments, particularly in democratic countries, will allow advertisers access to the soft biometric data that they collect, to an extent. This could be the data surrounding a certain event or focused on a certain area.

For the pessimistic approach, it’s possible that the technology is used, specifically in autocratic countries where the technology is already common, to bring targeted propaganda messages to people who may be most susceptible. In countries where citizens don’t have freedom of speech, interacting with these types of messages in any way that is deemed incorrect could threaten their safety.

Looking at the possibility of a disaster scenario brings up concerns of privacy and morality of using these types of AI surveillance technologies, especially in terms of how it could affect consumers. What if this type of technology moves into the healthcare or insurance space to determine prices based on someone’s daily movements; is it fair to track people like this when there is more on the line? If companies use this data to change prices based on what they know a person can afford, that would be immoral and could change the way people live their lives.

A transformation scenario could point to a complete overhaul of the way that we know ads, particularly out-of-home. Cookies use online tracking data to, for instance, save someone’s cart on an online shopping website even when they’ve completely closed out the tab, but what if this happened in real life? When you walk into a store, they already know what you are looking to buy simply due to your public behaviors. Currently, the AI surveillance technology is largely in the hands of government and military entities, with private companies such as IBM creating the technology and then selling it to governments to use. In this scenario, maybe companies like IBM would keep the technology, develop it further, or even sell it to advertising agencies or media conglomerates.

Especially since much of this surveillance is currently used in situations where the average civilian does not have access to the information gained from it, there could be ethical issues with these technologies going forward. This is largely reliant on who has access to the data coming from this technology and how they use it. Under international human rights law, there are three principles that are critical to assessing the lawfulness of a particular surveillance action. First, does the law in the place where the action is happening allow for surveillance? Second, does the surveillance action meet the “necessity and proportionality” international legal standard, which restricts surveillance to situations that are “strictly and demonstrably necessary to achieve a legitimate aim”? Third, are the interests justifying the surveillance action legitimate? Of course, the answers to these questions are very subjective. Something that is a valid reasoning in one country or type of government may be the complete opposite somewhere else.

More than anything, consumers do not like being tracked. They don’t like when company websites ask them if it’s okay to run cookies, even if they don’t know much about what that means for them or their privacy. So, the idea of this type of technology being used may be hard for consumers to accept and would most likely be largely contested by the public.

And of course, what happens if this simply doesn’t happen? What would the alternative be? With new social media platforms and digital channels being created seemingly every day, consumers are constantly exposed new types of content. Though there has been a recent trend of digital media being oversaturated in a way that makes it hard for advertisers to break through the noise, that doesn’t mean that consumers are not using it.

VR was supposed to be big, but it wasn’t. What if another digital platform popped up which brought consumers’ focus back towards the digital space? Or what if a new type of device that supported advertisement was created that became immensely popular, so much so that consumers started installing it into their homes? This would bring the ad focus back into the home. In the ever-changing and increasingly digitized space we live in, there are so many possibilities of what the future of advertising holds.

Integration of Surveillance into Media Planning

Originally published on Medium.com