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Talking Demographics: How to Identify Gender in a Mobile Environment

Talking Demographics: How to Identify Gender in a Mobile Environment

Female or male? For placing the right advertisement for a selected audience, the gender question remains a difficult subject in in-app advertising. The implementation in a real-time, big-data environment is challenging and requires continuous investment in data science know-how and hardware setup.

The programmatic advertisement industry offers a multitude of different audience targeting options, mostly required and valued by branding advertisers. There are traditional direct publishers, which display ads within a specific publisher environment. They depend on publisher-centric audience research (e.g. questionnaires) or pure common sense to determine needed audiences for future campaigns. In contrast, there is a technical audience targeting approach, which is enabled by a purely programmatic setup. It connects a big number of publishers and uses the power of algorithms to predict matching audiences in real-time, independent of the publisher environment. Consequently, targeting requirements within the modern online and mobile display world are more precise than ever. However, suppliers of potential advertisement space (mobile websites or applications) are more diverse and unreliable than traditional approaches like TV or print magazines, which are using market agreed audience data to predict and bill audience reach.

Within mobile, millions of applications and websites offer their users’ screen-real estate as advertisement space on an impression-by-impression basis. Specialized solution providers, so-called demand-side platforms (DSPs) are responsible for executing advertising campaigns on a technical basis and for delivering on the promise of hyper-precise targeting without alienating end-users. At the same time, strict regulations need to be followed to ensure the privacy and protection of the personal data of end-users.

Adello is receiving and processing several billion potential impressions per day. A significant fraction of them are produced by mobile applications and therefore often contain pseudonymized device identifiers like the Unique Device Identifier (iOS) and the Google Advertising ID (Android).

"Our stated goal is to use this data together with potential gender information to derive insights about the relation of application usage and gender. Therefore, it is crucial to have consistent and reliable data".

Dr. Bastian Kronenbitter, Head of Data Science at Adello Technologies GmbH

If you are seeking more profound knowledge, download our free Whitepaper and get deep insights on identifying gender in a mobile environment.

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