“Don’t target everyone.” is the golden rule of sales since the dawn of advertising. Even before the time of algorithms, knowing the right audience for your product has been top priority for advertisers. The difference is that now the process of audience intelligence gathering is a bit more advanced - we can now use automated systems which process and synthesis extensive amount of audience data, transforming it into concrete actionable insights. As we already pointed out algorithms are the driver behind this process but it is the statistical analysis they perform that produces the end results.
Machine Learning algorithms improve the ad buying tactics by enriching them with more comprehensive user data. In simple terms this means they navigate the advertising efforts with better informed knowledge on the targeted customers - who they are, what they are most likely to be interested in, when is the optimal time to connect with them, etc. This is still quite a lot of information, so one way to pack it up all neatly and use it in concrete strategies, is to cluster it into groups - aka data clustering.
In its essence clustering means synthesizing a group of abstract objects into classes of similar objects. In advertising this is what helps marketers discover distinct groups in their customer base. So all the collected consumer data that we mentioned earlier can be further refined and assigned into separate groups (i.e. audience clusters.) that share similar characteristics. For example, in one of these audience clusters there would be group of consumers that share the same purchasing patterns. In such case audience clustering allows the examination of collective buying behaviour instead of focusing on individual preferences.
Audience clustering improves the way brands position themselves in the market. By grouping consumers with similar psychometric, demographic, geographic or socio-economic attributes in the same group, advertisers are able to improve the flexibility and relevancy of their marketing strategies. Another key advantage of clustering is that is adaptable to changes and can be further enriched - for example, while exploring the market, algorithms can continuously update a given group with new relevant users.
And last but not least - something interesting that Machine Learning algorithms also do is that they can predict how the users in a defined cluster can react to a given ad. With this knowledge in hand, advertisers are able to show specific ads only to those groups of users that are most likely to be interested in their offer. In the competitive digital market this is a true game-changing advantage.