Everything you need to know about…

Everything you need to know about data and audiences – 2nd part

In our previous article, we shared with you the first part of a glossary about data and audiences.
The more sophisticated the technology, the more confusing the jargon… So in order to avoid confusion, discover the second part of our glossary, focusing this time on audiences and their attributes.

Audiences

Observed Audience

Definition: Data about someone who has engaged with a product page, made a specific purchase, or taken another designated action.
How marketers can use it: This audience can be targeted with a high degree of certainty because they have an actual track record of a past purchase or high intent to purchase. Observed audience data can fuel an ad or email campaign aimed at a set of people who you know are further down the sales funnel and will have the highest propensity to purchase.

Modelled Audience

Definition: akes a sample set of people who have purchased a product (or a similar product) and models a new audience of hopefully high-propensity buyers.
How marketers can use it: By modelling an audience (sometimes called “look-alike modelling”), you are creating a new and expanded audience of people who have similar attributes, behaviours and demographics to your actual buyers. The idea is that people with like attributes to your ideal customers will exhibit similar buying behaviour. Modelled audience data can help expand a limited amount of verified buyers to a bigger audience or new geographic area.

Audience Attributes

Descriptive Attributes

Definition: Data based on actual behaviours and demographics used for building a picture of an audience.
How marketers can use it: This data provides raw facts about a person or group of people, such as how often they visit a certain site, what content they read, and their demographic information, such as gender, age and income. Descriptive attributes are the fundamentals when it comes to getting a basic understanding of an audience. For example, if you know that buyers of a certain product tend to be female and have high household income, you can start to make some inferences about how to target them with marketing messages and ads.

Predictive Attributes

Definition: Data based on inferences or predictions of behaviour about a person or group of people. It is derived by applying statistical testing or an algorithm to descriptive statistics as described above.
How marketers can use it: Based on a set of created rules and theories, you can attempt to predict behaviour or possible action based on how a known set of people act. This technique can be valuable when looking for new sales prospects. For example, from statistical testing, you know that men under 40 from urban centres who make multiple visits to your site and engage with video content are the most likely to convert. You can create a program with an aggressive call to action for prospects that fit those criteria to attempt to accelerate them through the sales funnel.