For members who have not logged in to LinkedIn, the LinkedIn cookie is absent, so identification would not be possible without the probabilistic inferences described here. For members who are not logged in to LinkedIn, we seek to infer the association between the member and the device.
Our identity graph technology does not seek to infer interests for any individual we can identify. LinkedIn Marketing Solutions only personalizes ads for our members. We do not seek to profile non-members, and we also do not create or enhance behavioral profiles of members with off-LinkedIn data.
Below are examples of key observation data fields that might be collected to probabilistically infer identity.
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Cookies on a mobile or desktop browser, Google Ad ID on Android
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Operating system, device make and model (User Agent)
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IP address
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Time of access (Time Stamp)
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Page URL or application name, as applicable
The probabilistic identity graph technology forms likely associations of IDs (such as a Google Ad ID on Android, cookies on browsers, or IP on CTV and iOS) to the same device and devices to the same user or group. More importantly, this is done such that we don’t have in this system an understanding of which specific user (as identified by their member information) or group is involved.
This inference of identity is used to serve relevant ads on and off LinkedIn, measure the effectiveness of ads, and provide analytics that don't identify you.