Background: Informed Advertising & The Duopoly
Established in 2011, Persona.ly is a global programmatic ad-tech company specialising 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.
Persona.ly’s algorithms use different variables to inform bid requests and optimise returns for advertisers. These include:
- Temporal and environmental data e.g. 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 he would see in that session) and usual usage hours, along with several other features
- Demographic data - reported or predicted age, gender and geo-location
Facebook and Google
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 Persona.ly, meaning they can’t use it to optimise their bidding strategy – they can only rely on the data available in the bid-stream, their data enrichments, and predictions made of off that data.
Data and Artificial Intelligence
By 2018, Persona.ly’s artificial intelligence (AI) algorithms were fully optimised for using data from the bid-steam and aggregated data they collect in their DMP, recognising the differences in markets as diverse as Japan and the United States. Data from the bid-stream includes:
- Operating System (OS) and version
- User Agent
- The app the ad is going to be shown in
Aggregated data Persona.ly 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 Persona.ly questioned if there might be additional sources of information they could add to their algorithms to further improve performance for advertisers.
The Challenge: The Device Dimension
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 manufactures to differentiate their products. Could these device factors also relate to the apps users are likely to install and use?
Data scientists at Persona.ly 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 optimise 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 like GSM Arena, Amazon or Flipkart 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: Device Detection from 51Degrees
Also established in 2011, 51Degrees provides the fastest and most accurate solution for device identification, optimised for web, app and network operators. Clients such as Tencent, Disney and eBay, alongside over 1.5 million websites, have access to data which includes device price, age, battery capacity and screen size, all in real time.
51Degrees 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 4 million queries per second, handling complex Apple and Android devices.