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Behavioral Targeting

Everything about Behavioral Targeting


What is behavioral targeting ?

Behavioral targeting is an advertising technique used by online marketers, advertisers, and publishers to present targeted ads to consumers. This is done by gathering information about their browsing behavior, for example the pages that have been visited, time spent on the site, the clicks made, and overall interaction with the site. This knowledge is then used to deliver ads that are specifically relevant to the user.

How does behavioral targeting work?

Behavioral targeting is based on customer insights such as web browsing activity collected over a period of time. This is quite different from the more common targeting method of displaying ads matched to the specific content of an individual page or to all users in general. With behavioral targeting, this would mean that two people could see vastly different ads when viewing the identical webpage at the same time.

An example of behavioral targeting would be the following:

A person who frequently visits sporting sites might encounter ads for soccer tickets or basketball shoes on a webpage about the local business economy, while another who visits travel sites regularly might see ads for vacation packages or travel agents. While not at all related to the content of the website about the local economy, these ads are still specifically targeted to a particular user at a particular time.

To accurately track online behavior, websites use small pieces of data called cookies. Cookies are small files of letters and numbers stored either in the temporary memory of a computer and are deleted automatically once the browser is closed, or they are placed onto users’ hard drive to gather data about electronic shopping, online searches, page visits and more. They allow a website to recognize a user’s preference when they return to a website (language, shopping basket contents, etc.).

Data used for behavioral targeting

Behavioral targeting utilizes several pieces of data which allows for the creation of a user persona or profile that can be used to segment the audience into certain types. Here are two pieces of data that advertisers and publishers use to create targeted ads:

IP address and geolocation
One of the most basic segmentations advertisers make is targeting audiences by where they live. For example, ads pertaining to discounts and sales offerings can be targeted to a specific region.

The recency of the visit
A repeat visitor can be targeted by showing related content based on what they have search for in the past. This is an effective strategy for retargeting consumers who have already shown an interest in a product or service, moving them further towards conversion.

Benefits of behavioral targeting

Because behavioral targeting is about delivering the right message to the right customer at the right time, it is preferred by both advertisers and internet users. Below are the benefits of behavioral targeting:

Higher clicks
Behavioral targeting generates higher click numbers because it allows advertisers to reach out directly to a particular demographic or niche, which means that less of the marketing budget is spent on trying to attract and convince people to buy, and more of it used in transforming visitors into customers. Hosting advertisements that consumers find more interesting means that they are far more likely to click and consider the products and services offered.

Converts Visitors to Customers
By monitoring and tracking online consumer behavior, learning about their interests and preferences, advertisers are able to deliver the right sales strategy to the right people. In other words, behavioral targeting allows advertisers to give customers exactly what they want. Although it may not appear to every member of your target audience, it does give advertisers a greater chance of appealing to customers who are further along in the sales funnel and are more likely to convert.

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Technical Specifications

The table below shows how FaceMatch SDK performs on the Labelled Faces in the Wild (LFW) dataset:

FPRTPRThreshold (Inverse of distance)
0.10.99900 ±0.002130.55448
0.010.99667 ±0.005370.59791
0.0010.99367 ±0.006050.62989

FPR = False Positive Rate
TPR = True Positive Rate

These results are an indication only and are based on the specific dataset Labelled Faces in the Wild. Customers can expect similar performance, with possible variations due to hardware and the availability of annotated data.