data notes:
1. Data collection and profiling

Targeting starts with data collection, which provides a foundation for obtaining statistical knowledge about humans and predicting their behaviour. This process is both troubling and fascinating, and there exist many excellent investigations into how Facebook collects and analyses users’ data. (Our favorite is Vladan Joler and SHARE Lab’s series Facebook Algorithmic Factory).

For the purposes of this report, we will only cover the most common sources of data that are relevant to political targeting:

Data provided by users (e.g. profile information, interactions, content uploaded);
Observations of user activity and behaviour on and off-Facebook. This ranges from metadata (e.g. time spent on the website), to the device used (e.g. IP addresses), to GPS coordinates, to browsing data collected via Facebook’s cookies and pixels on external websites;
Data from other Facebook companies, like Instagram, Messenger, Whatsapp, and Facebook Payments;
Information collected from Facebook partners and data brokers such as Acxiom and Datalogix (discontinued in 2018).

All of this data is analysed by algorithms and compared with data from other users, in the search for meaningful statistical correlations. Facebook’s algorithms can detect simple behavioural patterns (such as users’ daily routines, based on location) and social connections. But thanks to big data analysis, Facebook is also able to infer hidden characteristics that users themselves are not likely to reveal: their real purchasing power, psychometric profiles, IQ, family situation, addictions, illnesses, obsessions, and commitments. According to some researchers, with just 150 likes, Facebook is able to make a more accurate assessment of users’ personality than their parents. A larger goal behind this ongoing algorithmic analysis is to build a detailed and comprehensive profile of every single user; to understand what that person does, what she will do in the near future, and what motivates her.

  1. Ad targeting and optimisation

Determination of the relevant audience (ad targeting)

After an advertiser creates an ad campaign and selects criteria for people they wish to reach, it is Facebook’s task to determine which users match this profile. All users who fulfill advertisers’ criteria belong to “relevant audience,” which should not be confused with “targeted audience” (see below).

Depending on how the advertiser selected their target audience, Facebook will have slightly different tasks:

Demographics, location, or attributes: Facebook will compare advertisers’ criteria with individual user profiles and determine which users meet these requirements;
Lookalike audience: Facebook identifies common qualities of individuals who belong to the so-called seed audience (e.g. their demographic data or interests). Then, with the use of machine learning models, Facebook identifies users who are predicted to share the same qualities;
Custom audience list: Facebook matches personal data uploaded by the advertiser with information it has already collected about users (e.g. emails used for login or phone numbers used for two-factor authentication).

Determination of the targeted audience (ad optimisation)

As we mentioned above, an advertiser’s budget usually is not sufficient to reach all Facebook users who match criteria they selected when creating a given campaign (i.e. reach everybody in the “relevant” audience). Therefore, Facebook - with the use of algorithms - makes one more choice: selects users from the relevant audience who should see a given ad (and make it to the “targeted” audience). This is what we call “ad optimisation.” In theory, this process should give the advertisers the best possible result for the money they spend.

In order to select the targeted audience, Facebook take the following factors into consideration:

Optimisation goal selected by the advertiser (e.g. awareness, consideration, conversion);
Frequency capping (Facebook will sometimes disregard advertisers who recently showed an ad to a particular user in order not to flood the user with ads from the same advertiser);
Budget considerations (e.g. daily capping or bid capping);
Ad relevance score (on the scale from 1 to 10), which is calculated by the analysis of:
Estimated action rates: predictions on the probability that showing an ad to a person will lead to the outcome desired by the advertiser (e.g. that it will lead to clicks or other engagement);
Ad quality: a measure of the quality of an ad as determined by many sources, including feedback from people viewing or hiding the ad and assessments of low-quality attributes in the ad (e.g. too much text in the ad's image, withholding information, sensational language, and engagement bait).