With the rapid growth of 'digital media time' spent on mobile, it is now extremely important to make sure that ads reach the right audience at the right time. Most ad budgets are now spent on carefully targeted rather than wide-reaching campaigns intended for anyone, and device detection is an essential ingredient in making this successful.

Companies measure the accuracy of their ad targeting

By the end of 2022, the programmatic digital display ad market is set to be worth more than $100 billion. In 2021 over 75% of programmatic video advertising spend in the US was focused on mobile ads. This emphasis on targeted advertising has also resulted in an increased reliance on attribution and measurement software for these campaigns to assess ROI – however, much of this analysis is focused on metrics surrounding call to actions, but not what influences these call to actions.

How device detection fits in the ad tech space

The fact that many brands have excellent understanding of targeting audiences effectively means that more accurate and granular targeting techniques are required to stay on top in the ad tech business. A solution for detecting all devices is necessary for every piece of the ad tech puzzle, including DSPs (advertisers and ad agencies), ad exchange platforms (marketplaces) and SSPs (publishers).

From an advertiser’s perspective, more granular device targeting options make it possible to reach a carefully selected audience that is the most likely to engage with the ads. It is also important to ensure that the ads are optimized for the devices they are seen on.

From an ad tech provider’s point of view, accurate and up-to-date device data minimizes the margin of error in terms of device targeting, especially new models that can get widely popular overnight. Building and maintaining your own your own device detection method for this purpose is a time-consuming effort that is prone to errors in the form of misdetections.

In the RTB (Real-Time Bidding) environment, a device detection solution should be deployed locally in order to take advantage of the highest speeds and the lowest server footprint available (see the short guide to choosing between cloud and local device detection).

How device detection works

Device detection solutions, such as DeviceAtlas, typically work by parsing User-Agent (UA) strings which is the standard way for the device to “introduce” itself to the server. They analyse UA strings and then look them up using high-speed APIs to return any property for any device. In order to detect devices, sometimes it is necessary to analyze other HTTP headers as well.

Which device properties should be available for ad targeting and campaign reporting

Device targeting and campaign reporting can be as detailed as the number of data points available. In terms of today’s standards, it is essential to support targeting using at least the following properties:

• Device type (which can get as granular as IoT, smart TVs, cameras, game consoles, etc.)
• Mobile operating system and mobile browser
• Screen size and screen resolution
• Device make and model (e.g. Apple iPhone 12)
• Carrier identification and connectivity level
• Device year of release (e.g. targeting the newest devices available)
• Device capabilities such as NFC, LTE band, etc.

According to the DeviceAtlas Mobile Web Reports, device fragmentation is still an issue which is growing rapidly. There are hundreds of different screen resolutions and diagonal screen sizes out there e.g., an iPhone 8 measures 4.7-inches in size compared to the 6.8-inch screen on the Samsung Galaxy S21+. There are now also more than 1,000 device manufacturers producing phones and tablets.

With an accurate, high-speed, low server footprint device detection solution, it is possible for ad tech players to provide advertisers with a great understanding of today's complex device landscape, so that they know exactly where their ad dollars are spent.