What is targeted advertising?
So what is targeted advertising? Targeted advertising is a marketing strategy that focuses on delivering advertisements to specific customers or a group of customers. The origins of targeted advertising can be traced back to the early days of print media. Advertisers would understand the broad demographics of a publication’s readers and would adjust their messaging or ad placements based on these factors. For example, a cosmetics company may have chosen to focus on advertising in magazines that were mostly read by women.
This approach was similarly applied to TV and direct mail advertising, and there were no major disruptions until the rise of the internet. Companies were able to use cookies to collect data about consumer preferences and behavior and then serve ads based on their browsing history. This capability would eventually lead to the rise of programmatic advertising, which automates the buying and placement of ads using this data, optimizing advertising efforts.
Another big change was introduced by search engines like Yahoo!, and later Google—Search Engine Marketing (SEM). This enabled advertisers to bid on keywords and display ads to users who were actively searching for a related product or service.
The next big leap forward happened in 2004, with the rise of the big social media platforms. This gave advertisers access to highly granular user data, which allowed them to more precisely target users based on their interests and preferences.
Despite the many benefits of personalized ads, there was a big downside: user privacy. Data collection became too pervasive, and customers became increasingly concerned about the lack of transparency surrounding its use. These concerns define the modern targeted advertising landscape. This led to the Google initiative Privacy Sandbox, and the development of more privacy-friendly targeted advertising techniques, in close collaboration with companies like us here at RTB House.
We have always taken a proactive approach to privacy-friendly advertising. For example, our approach has never relied upon mixing data from different advertisers. We have continued this approach by enthusiastically participating in the Privacy Sandbox project. Rather than attempting to cling to third-party cookies, we adjusted our advanced Deep Learning algorithms to allow them to more effectively understand user preferences in the absence of third-party cookies and deliver ads relevant to their interests. Our approach makes it possible to avoid privacy-invasive workarounds like fingerprinting, which many major industry players still use.
How does targeted advertising work?
Targeted advertising is all about collecting and analyzing data about a user, or preferably a group of users. Advertisers use this data to understand and predict what adverts customers are likely to be interested in. This can range from comparatively simple techniques, such as Search Engine Marketing, to sophisticated campaigns powered by Artificial Intelligence.
A typical process looks like this:
Data collection—A company gathers or purchases data about a user or group of users.
Data analysis—This data is analyzed, and any useful insights are recorded.
Audience segmentation—An advertiser’s audience can be segmented based on these insights, and this is used to determine what ads to send to whom.
Ad placement and personalization—Advertisers use this information to determine where to display ads and to personalize the content of the ads to the interests of their recipient.
Optimization and tracking—Yet more data is collected about an ad’s performance, which is used to optimize future adverts.
Let’s put this into a real-world scenario. Let’s say that you have just purchased an entry-level but high-quality, mountain bike. Your interactions with your favorite ecommerce site have shown that you’re serious about this hobby but just starting out, so you probably need some additional safety gear. A retargeting campaign would highlight helmets, knee and elbow pads, and other gear that might help to make your ride safer as you learn. This provides value to the user by recommending valuable products and the ecommerce platform, which can sell more products.
In modern marketing, this process happens rapidly and is assisted by technology. For example, RTB House uses Deep Learning algorithms to automatically tweak campaigns for maximum effectiveness. It is also able to infer user interest based on data provided by a user, for example, by searching for or purchasing, a product (like a mountain bike) on their website.
What is the main goal of targeted advertising?
The goal of targeted advertising is twofold. The first aim is to ensure that adverts are reaching the right people at the right time, whether that’s by picking the right publication to advertise on or using the right data to target people in a social media context.
The second aim is to personalize that advert to an individual user or groups of users. Personalization is so important that 62% of consumers would consider switching brands if they don’t receive a personalized experience from that company.
So, in a nutshell—the goal is to reach the right customers at the right moment to spur a purchase. To achieve this, an effective targeted advertising campaign leverages various strategies to ensure optimal ad placement and to enhance personalization, aiming to engage consumers more effectively and increase the likelihood of conversion.
What types of targeted advertising are there?
The sheer volume of data available to marketers means that there are many different types of targeted advertising available. Generally, marketers will want to use a variety of techniques to reach users and will combine some, or all, of these targeting methodologies.
Demographic Targeting—enables advertisers to target specific groups based on demographic data. This includes age, gender, income level, education, and more. Demographic targeting is a powerful tool, but marketers need to be careful to avoid certain targeting techniques that can be considered harmful, for example, Meta has removed certain problematic demographic targeting options. Demographic targeting has come under specific fire from regulators due to concerns over stereotype-based targeting and GDPR concerns.
Geographic targeting—lets advertisers target ads based on location. This can be particularly useful for some marketplace or classifieds apps which might want to connect with users in specific cities with local offerings. It can also help advertisers to tailor messages based on cultural preferences.
Interest-based targeting—helps advertisers reach users based on their predicted interests or hobbies, usually based on data gathered about their online activity.
Contextual Targeting—is one of the oldest forms of targeted advertising, but it remains effective in a modern context. It involves placing ads with specific publications based on their audience; for example, an ad for football jerseys might appear on a website related to sport.
Retargeting—is the bread and butter of many marketing campaigns. It involves displaying ads to users who have previously interacted with a brand’s product, website, or app, and trying to re-engage them based on that interaction to complete a desired action.
Lookalike audience targeting—Advertisers can target audiences that share similar characteristics or behaviors with their existing customers, as they are likely to have similar interests or needs.
All these targeting methods differ in their accuracy and effectiveness. For example, demographic targeting often relies on broader group characteristics such as age, gender, and income, which might not always accurately reflect a user’s preferences or needs.
To understand this, think about a woman who might be satisfied with her own wardrobe, but wants to see her husband in something a little more stylish than his trademark gray hoodie. In demographic targeting, because she is a woman, she might be targeted with ads for purses or high heels, aligning with common interests attributed to her demographic.
This doesn’t work because it doesn’t reflect her actual shopping intent. AI-powered retargeting can bridge this gap between knowledge and intent by adapting to her real-time behaviors. If she has recently visited a website looking at smart casual clothing for men, a professional retargeter would recognize this and show her ads for men’s smart casual wear instead. This approach is more precise, ensuring that ads are better personalized, leading to higher conversion rates and improved ROI compared to strategies that primarily use demographic targeting.
What are the benefits of targeted advertising?
Targeted advertising benefits both producers and consumers. From the producer side, it helps them reduce the costs associated with advertising campaigns by only serving ads to people who are actually interested in their product. Additional savings can be made through the use of personalized ads, which help to improve the overall ROI of a campaign. This means that companies can sell more for less money.
From the customer’s perspective, targeted ads can help to reduce the amount of noise that they see in the advertising landscape. Rather than being bombarded with irrelevant ads for products they don’t want or need, they’ll instead see useful adverts for something that could meaningfully improve their lives or just bring them a little joy.
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Despite these upsides, there is one major problem with this whole process: privacy.
Why is privacy a big concern in targeted advertising?
Over the past few years, concerns have been raised about the sheer volume of user data being collected in the name of better ads. Particular concerns have been raised about cross-site tracking with third-party cookies. Regulators are worried that it can be used to build a very accurate picture of a specific person.
This in itself isn’t an insurmountable problem. After all, targeted ads provide value to all stakeholders. However, no organization is perfect, and a data breach could lead to a treasure trove of user data finding its way into the wrong hands. Additionally, users don’t trust many of the organizations that process their data and want personalized ads without high-level profiles being created.
Finally, there is the risk of personalized ads being used for nefarious purposes. For example, while ads for shoes add value, there have been cases of targeted ads being used to push specific political agendas or by foreign powers to influence elections. To prevent this, legitimate retargeting companies, like RTB House, will not offer targeted political advertising.
Consumers have become increasingly aware of this, are generally more cautious about what data they share, and want more transparency about how it is used.
Why are third-party tracking cookies being targeted?
Third-party tracking cookies are considered to be particularly risky. They can be used to collect information across many different, unrelated websites by third parties unaffiliated with the website owners. This can make scarily accurate profiles of individual internet users. These profiles can then be later shared, re-sold, and circulated around global data markets.
The modern advertising industry relies heavily on third-party cookies for targeted advertising, which has created a dilemma. It is impossible to continue using them, but for a long time, advertisers had no reliable alternative to deliver targeted ads.
Google stepped in to solve this challenge with the Privacy Sandbox initiative.
Targeted advertising in the cookieless future
The Privacy Sandbox is designed to provide advertisers with a range of tools that allow them to conduct privacy-friendly targeted online advertising with a similar level of accuracy as they had with third-party cookies. It provides the digital advertising industry with a set of tools called APIs, which aim to fulfill key digital advertising use cases, which are currently addressed by third-party cookies, with user privacy as a key plank.
The Privacy Sandbox has over 20 APIs, and two of them are designed to address targeting functionality:
The Protected Audience API—Locks sensitive data about users and their behavior on the device (or in another secure environment) and uses advertiser-curated interest groups of indistinguishable users with the same characteristics for targeting.
The Topics API—Assigns users with interest labels based on their high-level browsing history. These labels can be later privately retrieved and used by advertisers.
In addition to the tools provided by the Privacy Sandbox, marketers will be able to fall back on some privacy-friendly classics. For example, contextual advertising will likely be more important and provide a simple way for advertisers to connect with groups of users.
The AdTech industry is constantly evolving and has adapted and transformed countless times in response to the shifting advertising landscape over the past two decades. The shift towards returning control of data back to users is another pivotal moment for the industry and will empower individuals with unprecedented control over their personal information. It is up to AdTech players to embrace the challenge of moving away from one-to-one targeting and build innovative technologies and strategies for the privacy-first era. Companies with a strong focus on data-driven tools, like Deep Learning, are well-placed to navigate this new landscape, demonstrating that the future of targeted advertising remains bright.
Unfortunately, not all cookieless targeting methods are privacy-friendly. Device fingerprinting and other particularly invasive tracking methods are likely to see a brief surge in popularity as they provide a similar non-transparent profile-based approach that is familiar to advertisers who use third-party cookies. These tools are highly controversial, however, and advertisers who rely on them may find themselves struggling in the long term as Google and other tech providers step up their efforts to prevent fingerprinting.
Technology will be the targeted advertising differentiator
This environment is interesting as it changes the playing field for targeted advertising providers. Companies with access to unique technologies will have an immediate lead in the new privacy-friendly world. For example, our Deep Learning algorithms are well suited for handling the complex datasets needed to target users in a cookieless environment.
If you’d like to learn more about how RTB House can help you with targeted advertising, book a consultation with our team today.
