From the many, not the few: on microtargeting

An increasingly prominent aspect of life online is a practise that has been christened by Harvard business theorist Shoshana Zuboff as ‘surveillance capitalism’. This involves the extraction of behavioural data about individuals with the intention of targeting those individuals in ways that can be manipulative.

 

As we’ve seen with incidents like the Cambridge Analytica scandal earlier this year, this has also spilled over directly in to politics – though it might be said that all such interactions ought to be considered seriously as novel political phenomena.

The key principle behind this new trend for ‘microtargeting’ is the ability to target people as individuals in a particular moment in time. Rather than treating these individuals as rational actors presented with information, the underlying mechanism and pitch for microtargeting tends more towards manipulation.

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This practise represents an undermining of agency – specifically setting out to target people, expose them to information or fake news, and employ systems of reward or enticement based on things of which they are likely not be fully aware. Byung-Chul Han and others have argued that this can “intervene in psychological processes” and operate “faster than free-will”.

 

Signify are diametrically opposed to such an approach. There is little research benefit in such an individually-led approach, and we’d argue that in marketing terms it is always more interesting to find large or quickly growing trends than focusing on private individuals. We are therefore committed to a maxim of using data from the many, not the few. This is not a luddite pretence that modern levels of data to not exist, but a guideline to make sure that we use it with consideration and restraint.

 

We aim to study aggregated groups, looking at what motivates people in a general sense. We will practise thoughtful consideration of how our findings might be used, rather than engaging in the intrusive algorithmic targeting of individuals based on a constant monitoring of their behaviour.