Last updated on July 28, 2018, 1:14 PM

Online marketing targeting options and methods at a glance

How can I address/target my target group efficiently? It’s a question that comes up very often in marketing in general and of course also in online marketing. In order to take a closer look at the various targeting options, I will give a brief overview of what is possible in the colorful digital marketing world.

Table of Contents:

Targeting by sociodemographic characteristics Targeting by keywords / search terms Targeting by context and semantic matches Targeting on statistical twins / lookalike target groups Conclusion

Targeting by socio-demographic characteristics

Data is constantly being collected in many online platforms and services. We use these services such as Google Search, Facebook, Twitter and co and they diligently collect data about our use and behavior. This data allows a certain image of each user to be created and certain socio-demographic characteristics can be assigned to these anonymized data points.

By using services such as Gmail or Facebook, where you also log in, this data is condensed into an even more precise socio-demographic picture of an anonymized user, which can then be used by advertisers for targeting in the advertising networks of these platforms. Cookies that are set (even when you are not logged in) can also collect a lot of data. The GDPR now at least gives the wild cookie setting more transparency, but since most users simply click away the “annoying” message, a huge amount of data continues to be collected.

Age, gender, profession, income, marital status, interests, … These characteristics (and often many more) can be used in various advertising tools as targeting for online advertising. The country and language of the user can usually be easily determined from the browser used and can also be used for targeting in the online marketing advertising tools.

Google Display Network – targeting options

Since Google, for example, cannot always collect all data from every user, algorithms are also used to make extrapolations in order to be able to put together certain target groups for certain characteristics. Many other providers also use similar mechanisms to provide advertisers with the largest possible pool of potential advertising recipients. “Statistical twins” are often spoken of here.

Here is a very good article from t3n magazine on the subject of Google and the data that is collected. If you want to see what Google knows about you, I recommend this page for an overview of the topics for personalized advertising for you :)

On Facebook, users (voluntarily) reveal a lot more data and by filling out their profile and with every small “like” on a post and a fan page, the anonymized image of a user becomes even more compact. This gives the advertiser a much larger selection of targeting options.

Here is a small example of how far you can go into detail using the data that users have entered on Facebook themselves or characteristics that have been assigned to them based on their behavior on Facebook.

Thanks to this relatively precise targeting, Facebook has increasingly developed into a strong performance channel over the last few years through which a lot of things can be “sold”.

However, if the respective advertising channel can only “determine” a few characteristics of the users that divide them into certain advertising-relevant categories, and therefore only a few targeting criteria can be selected, the target group is of course somewhat unclear. Therefore, channels that don’t “hit” quite as sharply are more suitable for branding and awareness measures.

You can actually apply this quite easily to the print world. Of course, a daily newspaper has a much broader distribution than a specialist medium such as a magazine for vintage cars. Large display networks that are often marketed by media agencies do not manage to provide precise targeting criteria, so they are more suitable for branding measures. For advertising channels that know their users very well, such as: With Facebook you can go much more precisely in the direction of performance.

The targeting options described here, especially on Facebook, are also referred to as behavioral targeting because you can classify and target the behavior of users.

Targeting by keywords/search terms

This targeting method has been made famous by the Google search engine and AdWords ads. Here it is possible to target for exact search terms and various combinations of terms. Here you catch the user in a slightly more advanced phase of a purchase project (the customer journey) and are therefore a little closer to the user’s direct purchase/conclusion/conversion.

When someone googles “compact camera comparison”, the user is already further along in the thinking process about purchasing a new compact camera in the future. In contrast, the user may have seen a creative display ad in advance with a subject that aroused the interest/desire for a new compact camera with great vacation photos.

Here is an example for the search term “car insurance”

The first 4 results in the listing are ads that targeted the keyword “car insurance” or combinations thereof. Keyword targeting is also available in other search engines such as Bing, but in German-speaking countries Google is clearly ahead, which is why I always talk about Google.

The Google AdWords system and the text ads based on keyword targeting offer an incredible number of options to target the user in their search queries. Over the years, the system has become increasingly complex and is almost a data jungle of setting options.

Therefore, some caution is required here as I have often experienced that the nice Google supervisors often make very interesting settings for SMEs at the beginning, which after a while often bring Google more (money) than the SMEs.

Keyword targeting (especially via Google) is and remains a perfect way to do performance marketing and precisely calculate the costs for a new user who has made a purchase / conversion / concluded a contract or fulfilled another goal for the advertiser.

Targeting by context and semantic matches

This targeting method became increasingly popular for display marketing from around 2015. Here, the advertiser specifies, for example, a topic or specific keyword that should be covered on the pages where the advertising should then be played out.

To do this, the websites where the ads are to be displayed must also be very well analyzed in order to really offer an accurate selection for advertisers. Therefore, linguistically complex analysis algorithms run over all pages (and their texts) that are available in the advertising network in order to categorize them. Your display can then run advertising there.

The advantage of this method is that (theoretically) there are no embarrassing misplacements of display ads. For example, if an airline’s advertisement for the next vacation is placed next to an article about a plane crash, or similar faux pas that are very damaging to the image occur.

But here too it is more about display ads and therefore branding measures that appeal more to users at the beginning of their customer journey. To put it briefly in an example: The user doesn’t even know that he needs a pair of new running shoes, but if he sees a nice pair next to the well-written article about marathon preparation, he might already slide into the next phase of the customer journey and soon google for new running shoes.

Targeting statistical twins / lookalike target groups

Through the data collection described in the first point using cookies, certain characteristics can be assigned to individual users and their behavior (on the web) can also be tracked. This digital fingerprint of a user can also be compared with all user data that an advertising network has in the system. This allows the targeting option of lookalike target groups or statistical twins to be offered. So that the whole thing isn’t too abstract, here’s a small example.

Hans Wurst visits our website for cash registers. We have installed Google Analytics and Google Adwords on the website and after informing the user about this fact and giving their consent, a cookie is set. The user reads through the offer on the website, continues to surf the web and research for a few days, but then comes back to our site a few days later and buys the cash register software. Google can assign certain characteristics to this anonymized user who has completed a purchase on our website, such as: he is a small business owner, male, technically experienced, … Google also recognizes this combination of characteristics in xy other users in Austria. This now makes it possible to target a lookalike target group of our satisfied buyer named Hans Wurst with ads.

Conclusion

That was a tiny insight into what is possible when it comes to targeting, although the surface has only been scratched here. Online marketing is constantly evolving and moving extremely quickly. What worked well yesterday may be obsolete tomorrow. I still hope I was able to show a little of the differences and how important it is to know as many methods and channels as possible in order to make the right decisions for a tailor-made online marketing campaign.