Cross-Device Matching Approaches

For decades, advertisers tried to reach masses by sending their messages through conventional communication platforms, such as television. For advertisers, being able to reach millions instantly was a great opportunity. Starting from the 60s, the marketing communication world has been shaped around the mass media.

As new communication technologies change the way we live, the popularization of the internet led to a more fragmented media environment and this revolutionary change had a huge impact on the everyday needs of an ordinary human being. 

Online marketing technologies provided powerful tools for marketers to have a better understanding of the consumer. To a certain extent, sending personalized messages through personalized channels to consumers has become possible.

But in the early days of the internet, everyone was using just one screen to get online: the computer screen. Thus, collecting data from one device was enough to target someone on the internet. But the internet landscape has changed a lot over the years, after smartphones became a standard. Almost all of us are connected to the internet through multiple screens.

The number of screens is getting higher and higher everyday. A good portion of internet users have a  desktop computer, a laptop, a tablet, and a smartphone. Some of us also have IoT devices like a smart TV or a washing machine. We also started using wearable technologies like smartwatches.

People are looking at more than one online screen these days. So it’s not possible to target someone by just targeting one device anymore. To have an optimized targeting, an effective cross-device matching approach is a must. In the future, we won’t target devices; we’ll target only people.

At first sight, having multiple devices to deal with can look like a threat to marketers. But this challenge comes with great opportunities as well. Collecting data from multiple devices will tell us more about someone’s life. It will make it much easier to design truly personalized experiences. But at this point, the most important challenge to overcome is to create meaningful connections between devices.

Let’s assume you have a computer and a smartphone. You use these devices everyday on different occasions. Each device provides a marketer with different insights about you. But the marketer has to identify “your” devices to have a better understanding of you.

Different Approaches

There are different approaches to this device identification process. The most common approach is the use of a single credential on different devices. If you have a Facebook account and if you login your account via both a computer and a smartphone, Facebook will interrelate your devices under a common name. This approach is called “deterministic approach”.

But what if you share your computer with a family member? The data provided by your computer can easily be irrelevant. Moreover, deterministic approach may fail if the user is not a subscriber of Facebook. So, audience coverage might be a problem here.

For that kind of situation, the cross-device attribution approach is the solution. This approach is based on probabilistic prediction and it tries to identify devices by matching behavioral and physical traits of a user.

In the cross-device attribution approach, devices with similar patterns are matched. This probability-based approach is less accurate than the deterministic approach, but has a wider range of audience since it does not require any subscription.

Data collection is a crucial factor in successful targeting at the same time privacy is the major concern. Although collected data is anonymous, European Union set up new regulations regarding privacy concerns and imposed some limitations on collected data in 2018 which is called GDPR. One of the new regulations here is to give users option of consent or decline of data collection. There are two different types of data collected: physical data and behavioral data.

Physical data consists of physical aspects of the device such as type, screen size, location, IP, the operating system of the device, browser, etc.

Behavioral data includes browsing history, URLs of the sites visited, the content of the websites, etc.

All these data help us to create profiles for each device. By matching devices with similar profiles, we have a better understanding of the person using these devices.

How Do We Implement at ReklamStore?

At Reklamstore, we are always trying to develop more sophisticated ways to have more efficient targeting algorithms. That’s why we implemented our cross-device approach to our user segments.

We used to segment our user base by their limited behavioral characteristics. Our categorization was mostly interest-based and single device focused. Now, thanks to our new approach of matching devices, we have a much better understanding of our users. Not only our categories are extended, but also impressions and conversion rates in our campaigns are remarkably amplified . Now we are offering our marketers, publishers, and consumers a smarter platform.

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