There are a large number of approaches to target audiences, especially in relation to behavioural change with varying underlying theories of behavioural change. This report provides an overview of these variables and the theories and approaches of behavioural change which employ them.
Audience segmentation can be defined as a ‘data-based method of identifying smaller target groups of people who share some relevant characteristics’ (Parvanta et al, 2010: 150). Audience segmentation has its origins in multiple disciplines including the behavioural sciences but is most commonly used for commercial and social marketing. Commercial and social marketing differ in that commercial marketing’s primary purpose is to achieve ‘profit/shareholder value’ and social marketing’s primary purpose is to achieve a social good (e.g. improving health, reducing inequalities) (Blair-Stevens, expert comments). However, they both use forms of audience segmentation to reach their target audience.
Though audiences are often segmented by ‘profile variables’ (i.e. demographic, socio-economic and geographic variables), they can also be broken down by ‘behavioural variables’ (e.g. the frequency of usage, readiness to purchase/use the good), and ‘psychographic variables’ (e.g. lifestyle characteristics, activities, interests and opinions, attitudes and personality). There are also psychosocial variables (e.g. self-esteem, depression, and stress levels), which are conceptually different to psychographic variables, and tend to be used in health research rather than marketing.
The effectiveness of audience segmentation depends on the ability to identify population subgroups that are homogeneous with respect to certain variables associated with a given outcome or behaviour. The greater the degree of homogeneity of an identified subgroup, in relation to a given outcome or behaviour, the more likely a targeted intervention will be effective. Rather than define subgroups, or segments solely by one type of variable, research suggests that combining variable types produce more homogenous segments. For example, Boslaugh et al. (2005) compared the results of audience segmentation that is based on either demographic, health status or psychosocial variables alone, or a combination of all three types of variables. Using psychosocial variables alone resulted in greater variability in segments than health status or demographic variables. Relying on demographic variables alone provided little improvement. However, combining all three produced a significant improvement in homogeneity of the segment.
