What is behavioral targeting, and how does it work?

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behavioral targeting

Behavioral targeting implies marketing tools that yield more accurate results than regular targeting thanks to user data analysis. Such methods involve collecting data about users, their preferences, purchasing habits, and only then displaying ads. That way the advertiser gets a more relevant audience, while the user sees ads that match their interests and requests.

How Does Behavioral Targeting Work?

Basically, behavioral marketing infrastructure consists of multiple market participants. They create an ongoing process of collecting and analyzing data that is invisible to the user. The whole process from collecting data to showing relevant ads can be divided into several basic steps.

Collecting cookies

When users create new profiles or visit websites, temporary cookies are placed on their devices. Through them, the sites can get an understanding of what sort of users come, and load the settings of the pages where the visitors stopped to take a look.

Creating a behavioral profile of the visitor

Based on the analysis of cookies, it is possible to obtain information about the pages the user visited, their browsing time, device geography, and their search queries. Based on this data, behavioral profiles of visitors are created, which are subsequently used for ad targeting purposes.

Identifying target consumer groups

The compiled user profiles are grouped together according to similar behavioral factors. Advertising tools that use behavioral marketing get information about the shopping patterns, interests, likes and dislikes of members of these specific audience segments.

Using this information in ad campaigns

Data management platforms communicate target audiences to demand-side platforms. By combining information from users and requests from advertisers, participants in RTB auctions compete for the price of showing the user the most relevant ad. A price is charged for showing a visitor an ad that matches their behavioral profile. It is formed based on the demand of advertisers to display similar ad campaigns and other factors such as device type, GEO, interests, potential paying capacity. Basically, it is a completely self-regulating, market-driven mechanism performed entirely by algorithms.

Behavioral targeting tools

The most important element of this chain are data management platforms. DMPs can store and analyze many different types of data. The actions processed by the DMP include the following datasets:

  • periods of pages being viewed: by analyzing the time people spent on viewing pages, you can understand how relevant this or that content is for the user;
  • clicks and interactions with website elements: based on this data, you can get information about what content is more attractive to the client, what elements they find the most attractive and identify the conversion rate for specific ads;
  • search queries on websites: this is the most important part of the data, since it can be used to understand what the user is looking for, when and how often;
  • frequency of visits: the number of visits can give you an understanding of how interesting the product is for the user, and even try to motivate them to purchase it or redirect them to a more suitable offer;
  • purchase and shopping cart history: this information forms the main profile of the interests and purchasing habits of users, ad networks offer most of their interest targeting options thanks to this data;
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Based on this data, DMPs identify target user groups, which are subsequently put up for RTB auctions to display relevant ads.

Types of Behavioral Targeting

The main types of behavioral targeting: network and local. Let’s take a look at them in more detail.

Local targeting

Local targeting means selecting the most relevant ads based on the visitor’s actions on this particular website. The system analyzes the user’s behavior and their clicks. Based on this data, it builds potential behavioral models and chooses which ad to show. For example, if a visitor in an online clothing store chooses predominantly male items, the system can conclude that the person is male and start offering items for that sex. The data used for this targeting is collected only on the basis of visits to this site and is not transmitted to third-party resources.

Network targeting

In network targeting, user data is more often collected from all across the Internet and is concentrated in data management platforms or with providers. Data is also collected by popular resources like social networks or search engines. When an advertiser or webmaster launches an ad campaign and sets up certain targeting settings, this is the data they use. It can be gathered from multiple websites and used when launching ads on different advertising platforms. It also usually includes a more extensive list. This is what distinguishes network targeting from local targeting.

Benefits of Behavioral Targeting

A large advantage of behavioral targeting is that it is useful to all sides of the market: both advertisers and consumers.

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Advantages for advertisers

Studies show that ad campaigns launched based on behavioral targeting data receive 59% more clicks compared to regular ads. Such ads pop up at the right place and time and show the user the most relevant offer.

On average, behavioral targeting doubles your ad performance. The user is shown ads on topics that they have already viewed or are interested in at the moment. This helps advertisers return old customers or attract new ones who are in the process of selecting an offer.

Because of the previous two advantages, the advertiser ends up with a higher ROI. The higher the engagement of users in advertising, the greater the conversion rate. Higher conversion rates result in more sales and lower ad costs.

Advantages for users

Research shows that 71% of respondents claimed to prefer personalized ads based on their needs and habits. And more than 40% are even ready to share their personal data for this purpose. This proves that the user has no wish to be shown the least relevant offers, and is even ready to make concessions to prevent this.

Shopping becomes easier with more personalized offers. Algorithms can offer the user a product that they chose, but forgot to buy. Or they just couldn’t decide, and sometimes the algorithm presents offers that the client didn’t even know about at all. With the development of smart algorithms for RTB auctions and targeting, the user will receive the most relevant offers, which will make overall shopping as relaxing as possible.

Reminders and alerts for new products and promotions will further simplify the shopping experience. Moreover, the data regarding which products and services the user has enabled notifications for can be used to offer that person even more relevant products.

This article was written in partnership with Galaksion.

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