Case study


Established in 2011, is a global programmatic ad tech company specializing in the delivery of mobile ads to users who are likely to yield a high Life Time Value (LTV) for their partnered advertisers.

Using a combination of machine learning and data science with playable and interactive rich media ads, user acquisition doesn't end at installs. It continues to in-app events such as registrations, reaching specific levels in a game and in-app purchases eventually leading to an LTV calculation.'s algorithms use different variables to inform bid requests and optimize returns for advertisers. These include:

  • Temporal and environmental data (the time of day or the day of the week).
  • Behavioural data (what other apps the user uses, the context the ad will be shown in, the tendency to click ads in general, the session depth (if it's the first ad they would see in that session), and usual usage hours, along with several other features).
  • Demographic data (reported or predicted age, gender, and geo-location).

Nearly 60% of digital advertising is controlled by Facebook and Google (popularly referred as the 'Duopoly'). Likes and dislikes, among many other metrics, are used by Facebook to provide advertisers with individual's behavioural preferences. Google makes use of search history for a similar purpose. With consent from the individual, both will provide demographic data such as age and gender. That said, this information isn't shared with DSPs like, meaning they can't use it to optimize their bidding strategy – they can only rely on the data available in the bid-stream, their data enrichments, and predictions made from that data.

By 2018,'s artificial intelligence (AI) algorithms were fully optimized for using data from the bid-steam and aggregated data they collect in their DMP, recognizing the differences in markets as diverse as Japan and the United States. Data from the bid-stream includes:

  • Location
  • Operating System (OS) and version
  • User-Agent
  • The app the ad is going to be shown in

Aggregated data collect in their DMP includes:

  • Data from the bid-stream
  • Interaction with their ads
  • Predicted age and gender (where this information isn't included in the request)

However, machine learning results are only as effective as the information provided; the more knowledge they have to work with, the more relevant their output is going to be. Therefore questioned if there might be additional sources of information they could add to their algorithms to further improve performance for advertisers.

The Challenge

Consumers increasingly purchase electronics for purposes beyond telephone calls, messaging, and social media. Video streaming, real-time gaming, audio, and 3D are increasingly important factors used by device manufacturers to differentiate their products. Could these device factors also relate to the apps users are likely to install and use?

Data scientists at had access to the very basic device information provided via the Open Real-Time Bidding (OpenRTB) bid stream used by publishers, Sell Side Platforms (SSP), Ad Exchanges, Demand Side Platforms (DSP), and advertisers to trade programmatic advertising. They felt that the device type information (Smartphone or Tablet) was not sufficiently accurate or comprehensive enough to further optimize advertising for their clients. Bid request data quality varied based on the upstream companies involved.

A market analysis was undertaken to identify solutions that could consistently add as much additional information as possible about devices, browsers, apps, and operating systems to the machine learning algorithms.

The team knew that solutions which scrape websites were not going to provide data as accurate as one where devices are researched manually and verified against sources of authority such as a manufacturer, vendor or network operator.

The Solution

51Degrees device identification was selected due to their manually researched, comprehensive, and relevant property set, as well as a track record of innovation, support for web and app environments, micro-second detection performance, and a flexible commercial model.

The solution was easy to integrate into the OpenRTB bid stream supporting over four million queries per second, handling complex Apple and Android devices.

51Degrees’ Device Detection solution enriched our existing data and gave us access to more granular segments than we could previously tap into. These segments allow us to adjust and make our bidding activity more effective for our clients. Ofir PasternakCEO and Founder,

The Result

The device data provided by 51Degrees enables to find more granular segments which means they can make more informed decisions and improve overall performance. analyzed two billion ad requests from Japan and three billion from the US during December 2018. During this time, some unsuspected attributes were identified as being significant in terms of improving performance:

  • Pixels Per Inch (PPI). In the US market, where mostly social casino apps were advertised, higher Click Through Rates (CTR) were observed for devices with lower PPI. This led to the conclusion that social casino apps do not require high end displays to be engaging and usable.
  • Device Memory or RAM. When devices were grouped by 2GB increments, a higher CTR was observed for devices with larger memory. This was relevant for both action titles in Japan and in social casino apps in the US, showing that generally, the more RAM the device has, the more lenient the user is towards engagement.
  • Battery. The battery life of a device helped to define the person holding the phone. For example, gamers need a longer battery life on their devices, to allow for extended playtime and the increased power demands of the game. During the requests analysed, there was a negative correlation between the battery life of devices and the win-rate experienced.
  • Fraud. were pleasantly surprised to discover 51Degrees could help uncover fraudulent advertising requests. By identifying malformed User Agent strings and not bidding on suspicious ones, 7% of poor-quality bid requests are now filtered out.
  • Release Year. In the US market, the device release year had a negative correlation with's win rate, meaning that the newer the device, the lower the win rate. While in Japan, the win rate seems to be unaffected by how recently the device was released. This showed that US buyers are already keen to use the release year as a feature, and that APAC buyers are not, which allowed to focus their bids on newer devices without increasing their bids, leading to significantly improved performance.

By suspending bidding on requests from devices with some features, and paying more for others, were able to improve performance by 15% within a few months and help deliver more engaged users to their advertisers.

Further improvements are expected as the data is applied at a more granular level to specific campaigns and apps.

What is certainly clear is that advertisers faced with an agency asking them, "which devices do you want to advertise on" need to rethink their choice of question., supported by 51Degrees, are using device intelligence to inform their strategy and, as such, are a great choice for app advertisers seeking an innovative partner applying the most advanced techniques.

51Degrees led the way in device detection with app support and comprehensive hardware specifications at least one year before more established competitors. I’m so proud to be able to explain how these innovative features are adding value to James RosewellCEO and Founder, 51Degrees

About Device Detection

Device detection empowers a web page to understand a great deal of information about the device that is visiting the site prior to making a decision about the content to send to it. This could include information on browser type and capability, processing power of the device, and screen resolution, to name but three of thousands of categories. Responsive web design alone is not enough.

Device detection is an important facet of personalization but does not require a user to register or share personal preferences. Armed with this information, websites can send content that is not only optimized for detailed device specifications but also contextualizes the likely environment of the user, delivering a better user experience.

If you wish to benefit from 51Degrees Device Detection, ask us about a free trial today.