Solving for Hijacked Attribution in Performance Marketing

Performance marketing has always been data driven, with vanity matrices guiding the performance KPI. It's been an exciting role for 21st century marketers to identify, target and retarget customers based on their actions and behaviour.

Often this actions and behaviour of consumers is understood by data attribution. Each action of consumer creates a data footprint for him/er in the data attribution. This attribution is often used by marketers to define their KPI in performance campaigns.

Last click attribution and multi click attribution has been current models of adoption for marketers define paying criteria for traffic providers. You would often find marketers running KPI's ranging from CPM to CPT, which is equivalent of feeding your entire sales funnel with some form of traffic.

Why are these models proving to be insufficient to fight the menace of advertising fraud?

Data attribution is often hijacked.

How?

Fake mobile apps or a publisher(app or web-based) working with 2 or more ad-houses or networks.

Example : On my android device, suppose I have a Youtube app and a local news application, Now, if I saw an advertisement on youtube, and showed interest in the service being provided by the advertiser. The local news application, acting as a user tracking app, would inject a last click if I start using the service provided by the advertiser, claiming the traffic. In turn, making commission. While, the conversion really happened on Youtube.

This form of ad fraud often called click injection is rampant among B2C advertisers, and often impacts minimum 20% spends on ad-networks.

Why it is a problem?

Hijacking user conversions for high value payouts in form of CPT campaign, has always been a playground of big time fraudsters. A network of these form of mobile apps are deployed intentionally. Who is hijacking and what is the source of hijacking is a mystery question for advertisers globally.

How can Com Olho help?

Com Olho neither uses rule based detection nor uses any form of blacklist to identify fraudulent traffic. We use sophisticated machine learning algorithms to segregate traffic based on degree of programmatic manipulation, which allows us to decide if a particular traffic provider has been hijacking traffic from other inventories i.e social networks.

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